Martin   Das
2012 ‐ 03 ‐ 04
University   of   Technology   Delft
Faculty   Technology,   Policy   and   Management
Innovation   Systems
Chairman:   Prof.dr.
Kleinknecht
1
2 st nd
Supervisor:   dr.
Zenlin   Kwee
Supervisor:   dr.
Martin   de   Jong

This written document contains information which is stated as confidential.
The information is intended for the use of the individual or entity named on the front page and for the company for whom this research was performed.
Therefore, all confidential material and information is blacked out and/or deleted from the original thesis report.
‐ v ‐

AWM:
C&SPIT:
CEE:
CEEMEA:
DOX:
EE:
F&HC:
FLA:
FMCG:
GDP:
LS/HS:
M:
MM:
MSU:
OAR:
P3M:
SU:
T1/2/3:
WPI:
Automatic   Washing   Machine
Concept   &   Single   Product   Identified   Test
Central   Eastern   Europe
Central   Eastern   Europe,   Middle   East   and   Africa
Design   of   Experiment
Eastern   Europe
Fabric   and   Home   Care
Front   Loading   Automatic   machine
Fast   Moving   Consumer   Goods
Gross   Domestic   Product
Low   Suds/High   Suds
Thousand
Million
One   thousand   statistical   units
Overall   Rating
Past   3   Months
Statistical   Unit
Tier   1/2/3
Weighted   Purchase   Intent
‐ vi ‐

The   occasion   of   this   research   paper   is   the   master   thesis   of   the   study   Management   of   Technology   on   the   Delft   University   of   Technology.
The   research   topic   is   part   of   the   research   program   of   the   department   Economics   of   Innovation   from   the   Faculty   Technology,   Policy   and   Management.
This   master   thesis   is   good   for   30   ECTS   which   means   that   the   duration   of   the   project   is   approximately   six   months.
The   project   itself   was   performed   during   an   internship   at   a   Fast   Moving   Consumer   Good
(FMCG)   company   where   the   assignment   was   placed   at   a   Laundry   Product   Research   Department.
Therefore,   this   research   has   a   more   business   related   topic   which   can   be   used   by   the   FMCG   company   to   improve   their   businesses   in   this   area.
During   this   project,   several   disciplines   were   covered   which   are   taught   during   the   MOT   program.
Examples   of   these   disciplines   are   quantitative   research   methods   and   innovation   management.
I   would   like   to   thank   my   daily   supervisor   from   the   company,   Paul   Stevens,   and   my   section   head,
Emmanuel   Narinx,   for   their   help   and   support   during   this   project   and   providing   me   with   the   freedom   to   complete   this   project   during   the   past   six   months.
I   had   a   great   time   at   the   department   where   I   had   the   chance   to   meet   many   wonderful   people.
This   work   could   not   have   been   occurred   without   the   help   of   many   people   such   as   Angela   Phillips,   Nancy   Vandamme,   Marianne   Bouvette   and   many   more.
The   time,   effort   and   energy   that   all   these   people   put   into   this   internship   was   amazing   and   helped   me   to   make   my   time   in   the   company   a   great   learning   experience.
Besides   my   colleagues,   I   would   to   special   thanks   to   the   other   (summer)   interns   for   their   help,   support   and   company   during   the   time   we   spend   together.
They   made   my   time   during   the   internship   truly   wonderful   and   they   made   sure   I   had   a   good   time   during   and   after   work.
Living   together   in   an   unknown   city   was   a   fun   experience   and   I   am   sure   I   will   see   many   of   them   soon   again.
Lastly,   I   would   like   to   acknowledge   my   supervisors   from   the   TU   Delft.
I   would   like   to   thank   dr.
Zenlin
Kwee   from   the   TU   Delft   who   supported   me   from   a   distance.
It   is   not   easy   to   help   a   student   who   is   not   nearby   and   therefore   I   would   like   to   thank   her   for   her   support   and   faith   in   the   project.
Finally,   I   would   like   to   thank   prof.dr.
Kleinknecht   and   dr.
Martin   de   Jong   for   participating   in   my   master   thesis   panel,   for   their   guidance   during   this   project   and   their   valuable   feedback   on   the   results   and   the   report.
‐ vii ‐

Due   to   the   opening   of   Central   and   Eastern   European   borders,   many   multinational   companies   see   opportunities   for   their   products   in   these   regions.
However,   these   markets   are   not   similar   to   the   markets   they   usually   operate   in.
Local   customization   strategies   are   required   in   order   to   expand   their   market   share   not   only   for   cultural   bounded   products   but   also   for   non ‐ cultural   bounded   products   such   as   laundry   detergents.
Companies   producing   laundry   detergents   used   to   have   holistic   strategies   for   their   products   but   now   recognize   that   local   customization   strategies   of   marketing   are   needed   and   a   better   understanding   of   specific   consumer   needs   is   required.
The   aim   of   this   research   is   to   find   out   how   Fast   Moving   Consumer   Goods   companies   can   perform   an   effective   and   fast   market   research   to   investigate   what   excites   consumers   most   in   a   laundry   detergent   product   and   what   trade ‐ offs   are   made   between   price   and   product   attributes.
A   quantitative   research   method   is   developed   for   the   laundry   detergent   market   in   which   a   conjoint   and
Maxdiff   test   are   combined   in   a   so ‐ called   virtual   prototyping   test.
The   usefulness   of   this   virtual   prototyping   test   is   illustrated   in   a   case   study   for   a   FMCG   company.
Together   with   consumers,   an   attribute ‐ based   conjoint   test   interface   is   developed   which   can   provide   valuable   information   on   which   product   attribute   is   most   important   at   which   price   level.
It   will   give   insights   on   trade ‐ off   behaviour   of   consumers   and   its   influence   on   the   Overall   Rating   of   the   product.
The   same   consumers   help   to   design   a   maximum   difference   scaling   test   in   which   consumers   have   to   select   their   most   and   least   preferred   statement   out   of   a   set   of   benefit   statements.
This   provides   a   rank ‐ order   of   benefit   statements   and   benefit   vectors.
Together,   these   two   tests   provide   a   holistic   understanding   of   the   consumer.
It   helps   companies   to   gain   fast   and   accurate   knowledge   on   their   key   consumers   and   to   apply   this   knowledge   in   a   local   customized   product   strategy.
The   virtual   prototyping   test   was   performed   in   a   central   location   among   consumers.
To   the   knowledge   of   the   author,   this   research   shows   the   first   combination   of   a   conjoint   and   Maxdiff   test.
Consumers   are   able   to   answer   the   questionnaires   without   further   help   and   the   results   show   a   clear   distinction   in   data   between   different   consumer   groups   and   segments.
This   indicates   that   all   product   attributes   and   price   are   considered   by   consumers   in   the   test   interface.
These   conjoint   models   reveal   there   is   a   non ‐ linear   and   asymmetric   relationship   between   product   attribute   and   consumer   acceptance   which   makes   the   Kano   model   a   useful   tool   to   analyse   the   conjoint   data   curves.
This   research   shows   that   a   combination   of   a   conjoint   and   a   Maxdiff   test   can   reveal   important   information   on   key   consumer   groups.
A   clear   interface   is   developed   which   can   be   used   on   internet   panels   in   the   future   for   rapid   prototyping.
The   case   study   demonstrates   that   the   virtual   prototyping   test   helps   to   sharpen   the   localized   strategy   and   to   steer   product   modifications   and   formulations   to   gain   a   better   market   position.
‐ viii ‐

List   of   Acronyms   ....................................................................................................................................
vi
Preface   ..................................................................................................................................................
vii
Summary..............................................................................................................................................
viii
Table   of   Contents   ..................................................................................................................................
ix
1.
Introduction   ..................................................................................................................................
11
1.1
Research   Questions   ....................................................................................................................
11
2.
Literature   Review   .........................................................................................................................
13
2.1
Laundry   Detergent   products   ......................................................................................................
13
2.2
Laundry   Detergent   Market   .........................................................................................................
14
2.2.1
FMCG   Market   Characteristics   ..............................................................................................
15
2.2.2
Competitors   on   the   Market   .................................................................................................
16
2.2.3
The   Laundry   Market   ............................................................................................................
17
2.3
Pricing   in   the   Detergent   Market   .................................................................................................
18
2.4
Laundry   Detergent   Brand   ...........................................................................................................
19
2.5
Market   Segmentation   .................................................................................................................
19
2.6
Customer   Empowerment   in   Product   Development   ...................................................................
19
2.7
Virtual   Prototyping   .....................................................................................................................
20
2.7.1
Conjoint   methodology   .........................................................................................................
21
2.7.2
Maximum   Difference   Scaling   methodology   ........................................................................
21
3.
Research   Framework   ....................................................................................................................
23
3.1
Variables   .....................................................................................................................................
23
3.2
Theoretical   Framework   ..............................................................................................................
26
4.
Research   Methods   ........................................................................................................................
29
4.1
Data   Collection   Procedures   ........................................................................................................
29
4.1.1
Qualitative   Research   ...........................................................................................................
29
4.1.2
Quantitative   Research   .........................................................................................................
30
4.2
Operationalization   and   Measurement   Variables   .......................................................................
34
5.
Data   Analysis   &   Results   ................................................................................................................
37
5.1
Interface   Development   ...............................................................................................................
37
5.1.1
Maxdiff   Interface   .................................................................................................................
37
‐ ix ‐

5.1.2
Conjoint   Interface   ...............................................................................................................
38
5.2
Maxdiff   Analysis   .........................................................................................................................
39
5.2.1
Results   based   on   the   Strategic   Target   ................................................................................
39
5.2.2
Results   based   on   segments   within   the   Strategic   Target   .....................................................
39
5.2.3
Results   based   on   Tier   users   within   the   Strategic   Target   .....................................................
39
5.3
Conjoint   Analysis   ........................................................................................................................
39
5.3.1
Vector   Impact   Analysis   ........................................................................................................
40
5.3.2
Model   Based   Conjoint   Analysis   ...........................................................................................
40
6.
Discussion.....................................................................................................................................
41
6.1
New   methodological   Approach   .................................................................................................
41
6.2
Discussion   on   the   Strategic   Target   Overall   ................................................................................
43
6.3
Discussion   on   Segments   within   the   Strategic   Target   .................................................................
43
6.4
Discussion   on   Tier   Users   within   the   Strategic   Target   ................................................................
43
7.
Conclusion   ....................................................................................................................................
45
8.
Recommendations   and   Limitations   .............................................................................................
47
Literature   .............................................................................................................................................
49
Index   Appendices   .................................................................................................................................
53
‐ x ‐

1.
Introduction
Due   to   the   opening   of   Central   and   Eastern   European   borders,   many   multinational   companies   see   opportunities   for   their   products   in   these   regions.
Since   the   1990s,   Fast   Moving   Consumer   Goods
(FMCG)   companies   have   tried   to   penetrate   the   Eastern   European   markets   with   their   products.
However,   these   markets   are   not   similar   to   the   markets   they   usually   operate   in.
Therefore,   local   customization   strategies   are   required   in   order   to   expand   their   market   share   in   these   regions.
Not   only   cultural   bounded   products   but   also   non ‐ cultural   bounded   products   such   as   laundry   detergents   suffer   from   this   principle.
Companies   producing   laundry   detergents   used   to   develop   holistic   strategies   for   the   whole   Eastern   European   area.
Now,   they   recognize   that   local   customization   strategies   of   marketing   are   needed.
This   thesis   report   will   therefore   focus   on   how   these   FMCG   companies   can   perform   an   efficient   market   research   to   develop   the   ideal   strategy   for   their   laundry   detergent   in   the   specified   area   of   interest.
The   main   question   to   be   answered   in   this   report   is   defined   as:
How   can   FMCG   companies   perform   an   effective   market   research   in   Russia   to
2.
investigate   what   excites   consumers   most   in   laundry   detergent   products   and   what   trade ‐ offs   are   made   by   consumers?
A   quantitative   research   method   is   developed   for   the   specified   laundry   detergent   market.
This   method   is   based   on   two   concepts   often   used   in   market   research;   the   first   method   is   a   conjoint   test   and   the   second   method   is   a   maximum   difference   scaling   test.
These   two   methodologies   will   be   combined   in   a   virtual   product   prototyping   test   in   which   consumers   are   exposed   to   a   virtual   product   experience.
This   virtual   product   experience   shows   the   performance   of   non ‐ existing   products   on   a   computer   screen   and   forces   consumers   to   make   trade ‐ offs   between   different   concept   products.
These   consumers   have   to   choose   the   product   they   are   willing   to   buy   based   on   product   performance   and   product   benefit.
The   results   of   this   virtual   product   prototyping   test   can   be   used   to   model   relative   impact   of   claimed   product   benefits   on   overall   consumer   acceptance   of   the   product.
1.1
Research Questions
To   perform   this   research   in   an   orderly   and   efficient   manner   and   to   delineate   the   research,   some   research   questions   have   been   developed   based   on   the   main   question   stated   in   the   introduction.
The   first   two   sub ‐ questions   regard   the   development   of   an   interface   which   can   be   used   by   companies   to   leverage   the   knowledge   on   consumers   and   their   product   preferences.
One   of   the   main   targets   in   this   research   will   be   to   find   a   test   interface   which   highlights   the   different   trade ‐ offs   made   by   consumers.
Furthermore,   the   test   data   has   to   be   analysed   in   an   efficient   way   to   ensure   a   high   speed ‐ to ‐ market   capability   of   new   and/or   improved   products.
Therefore,   the   first   two   sub ‐ questions   are   formulated   as:
‐ 11 ‐

1.
Introduction
1.
What   is   the   ideal   test   interface   for   the   virtual   product   prototyping   test   to   leverage   the   knowledge   on   consumer   preference   and   product   trade ‐ offs?
2.
How   can   the   results   of   the   conjoint   and   maximum   difference   scaling   test   be   used   to   get   a   better   understanding   of   consumer   wishes?
To   illustrate   the   effectiveness   of   the   test   interfaces   and   to   check   if   the   analysis   methodology   is   sufficient   to   gather   fast   and   accurate   recommendations   from   the   data,   the   test   will   be   placed   in   the   context   of   a   market   research   for   an   FMCG   company   This   case   study   should   find   out   if   the   test   interface   can   reveal   the   best   product   strategy   for   the   brand.
Hence,   the   third   and   fourth   sub ‐ questions   are   defined   as:
3.
Which   product   benefits   of   the   brane   can   best   excite’s   strategic   target   in   Russia   with   special
emphasis   on   current   Tier   3   users   within’s   strategic   target?
4.
Which   local   customization   strategy   should   be   applied   by   P   to   excite   Russian   consumers   with   the   laundry   detergent   brand   ?
The   four   sub ‐ questions   can   be   divided   into   two   groups:   one   group   is   more   scientific   orientated   while   the   other   group   is   more   focused   on   the   brand   of   the   FMCG   company.
However,   all   four   sub ‐ questions   will   help   to   answer   the   main   question   which   is   to   develop   a   new   methodology   to   gain   fast   and   accurate   knowledge   on   consumer   needs   in   the   laundry   detergent   market.
The   literature   discussion   in   the   next   chapter   will   reveal   two   useful   methodologies   which   are   helpful   in   answering   the   main   question.
These   two   methodologies   are   the   conjoint   and   Maxdiff   tests   which   will   be   combined   in   a   virtual   prototyping   setting.
However,   both   tests   have   not   yet   been   implemented   in   the   field   of   laundry   detergents.
The   two   methodologies   are   very   suitable   for   combining   with   an   on ‐ line   panel   to   increase   the   speed ‐ to ‐ market   of   new   products.
This   requires   that   the   interface   must   be   digital   and   in   such   a   way   that   consumers   can   use   the   interface   independently   without   help   of   a   third   person.
After   the   two   test   interfaces   are   designed,   the   tests   will   be   placed   in
Russia.
When   the   data   comes   back,   a   fast   and   efficient   way   of   analysing   the   results   must   be   developed.
Especially   for   the   Maxdiff   test,   there   is   little   known   on   how   to   interpret   the   data   in   a   meaningful   and   consistent   way.
With   new   software   on   the   market   such   as   Sawtooth   Statistics   and
JMP   Software,   it   becomes   easier   to   analyse   the   data.
Next,   an   overview   of   the   used   literature   (chapter   2)   will   be   provided   including   information   on   the   chemical   properties   of   laundry   detergents,   the   market   and   information   on   the   brand.
Furthermore,   it   will   give   more   information   on   the   two   methodologies   which   are   used   as   the   backbone   for   the   virtual   product   prototyping   test.
Then,   the   third   chapter   will   propose   a   theoretical   framework.
This   is   followed   by   the   research   methods   used   in   this   research   and   the   operationalization   of   the   variables   from   the   theoretical   framework   (chapter   4).
Next,   an   overview   of   the   test   data   (chapter   5)   and   a   discussion   on   these   data   (chapter   6)   will   be   presented.
This   thesis   report   will   finish   with   a   conclusion   in   chapter   seven   and   recommendations   for   further   research   and   recommendations   (chapter   8).
‐ 12 ‐

2.
Literature   Review
This   chapter   contains   an   overview   of   the   literature   used   during   this   research.
The   four   P’s   of   the   marketing   mix   (i.e.
price,   place,   product   and   promotion),   will   be   used   as   a   guideline   to   receive   a   description   of   the   laundry   detergent   market   of   interest.
This   is   followed   by   a   discussion   on   consumer   involvement   in   the   development   of   a   local   customized   product   strategy.
The   chapter   ends   with   a   literature   overview   on   the   different   methodologies   which   will   be   used   during   this   research   such   as   virtual   prototyping,   conjoint   analysis   and   maximum   difference   scaling   tests.
2.1
Laundry Detergent products
A   laundry   detergent   is   a   common   product   which   is   used   on   a   daily   basis   by   millions   of   people   worldwide.
However,   not   many   people   recognize   that   the   science   and   chemistry   behind   an   ordinary   laundry   detergent   is   actually   very   complex.
It   contains   over   50   different   types   of   ingredients   and   over   200   actual   ingredients   (P&G   2007).
Each   of   these   has   its   own   function   to   make   your   clothes   clean   and   fresh.
Designing   a   laundry   detergent   which   completely   fulfils   the   needs   of   a   consumer   segment   can   thus   be   a   challenging   activity.
Only   25%   of   the   dirt   on   clothes   can   be   seen   by   the   consumer   where   the   other   75%   of   the   dirt   is   hidden   on   or   inside   the   laundry.
A   typical   wash   load   of   approximately   3   kg   contains   around   40g   of   soil   which   can   be   classified   in   different   groups.
Table   1   shows   an   overview   of   these   different   groups   and   its   relative   presence   in   a   typical   wash   load   (P&G   2007).
It   becomes   clear   that   a   laundry   detergent   is   able   to   focus   on   different   types   of   dirt   and   stains.
Depending   on   the   preferences   of   the   consumer,   the   laundry   detergent   has   to   be   adjusted   to   perform   best   in   that   specific   area.
In   order   to   remove   all   these   different   types   of   soil,   a   laundry   detergent   has   several   functions   to   fulfil.
These   functions   are   divided   in   six   steps   which   are   summarized   in   Figure   1.
First,   the   detergent   will   hydrate   the   soil   followed   by   the   removal   of   the   soil   by   using   both   chemicals   and   attrition.
When   the   soil   comes   loose   from   the   fabric   it   has   to   be   fragmented   in   order   to   dissolve   in   the   liquid.
After   the   soil   has   been   broken   down   into   smaller   pieces,   anti ‐ re ‐ deposition   chemicals   make   sure   the   soil   cannot   reattach   to   the   fabric   while   the   bleaching   chemicals   in   the   detergent   make   sure   that   the   residual   soil   will   be   bleached.
The   last   step   of   the   detergent   mechanism   is   the   fibre   modification   which   makes   sure   that   the   clothes   feel   soft   and   the   colour   of   the   clothes   will   not   fade.
Table   1:   Groups   of   soil   and   their   presence   in   a   typical   wash   load   of   3   kg
(40g   of   soil   in   total)   (P&G   2007).
Type   of   soil
Body   soil
Accidental   stains
Applied   products
Environment
Professional/hobbies
Presence
70%
10%
5%
10%
5%
‐ 13 ‐

2.
Literature   Review
Figure   1:   Soil   removal   mechanism   of   a   laundry   detergent   (Bodet   and   Gualco   2011).
Each   of   these   functions   is   performed   by   a   formulation   block   of   chemicals   or   a   combination   of   these   blocks.
In   order   to   understand   how   to   improve   a   laundry   detergent,   it   is   of   vital   importance   to   know   what   parameters   can   be   adjusted   and   what   these   parameters   do.
Each   formulation   block   can   have   a   significant   effect   on   how   consumers   experience   the   detergent   and   the   process   of   doing   laundry.
During   this   research,   only   four   formulation   blocks   will   be   distinguished   and   considered   to   be   adjustable.
These   four   formulation   blocks   are   based   on   the   chemicals   of   a   detergent.
Furthermore,   the   performance   of   a   laundry   detergent   is   often   described   in   terms   of   stain/soil   removal,   whiteness/brightness,   perfume   and   care   attributes   such   as   softness,   fabric   care   and   colour   protection   (López ‐ Mahía,   Muniategui   et   al.
2005;   Yangxin,   Zhao   et   al.
2008).
The   four   formulation   blocks   are   defined   as:
1) Cleaning:   cleaning   is   the   overall   removal   of   (accidental)   stains   and   soil.
It   refers   to   the   removal   of   concentrated   soil   on   clothes   and   the   overall   cleaning   of   the   item.
2) Whiteness:   this   refers   to   the   overall   brightness   of   your   white   clothes.
Detergents   can   make   your   clothes   shining   white   and/or   renew   whites   back   to   new   after   they   became   dull   and   dingy.
3) Care:   this   is   a   broad   formulation   block   which   refers   to   the   maintenance   of   the   fabric   texture,   preventing   colour   fading   and   providing   softness   and   a   nice   fabric   feel   after   washing.
4) Freshness:   freshness   refers   to   the   scent   of   the   detergent.
It   can   relate   back   to   wet   laundry   clothes   after   wash   or   dried   clothes   when   using/wearing   them.
2.2
Laundry Detergent Market
Laundry   detergents   are   part   of   the   FMCG   market   and   are   used   all   over   the   world.
It   is   considered   to   be   a   slow ‐ growth   market   with   fierce   competition   between   three   main   players:   Procter   &   Gamble,
Unilever   and   Henkel   (Slater   and   Olson   2002).
This   section   will   describe   the   market   of   laundry   detergents.
‐ 14 ‐

2.
Literature   Review
2.2.1
FMCG Market Characteristics
Since   the   opening   of   Central   and   Eastern   Europe   borders,   many   Multinational   Companies   (MNCs)   see   opportunities   for   their   products   in   these   regions.
These   MNCs   believe   that   highly   standardized   products   can   be   used   in   these   types   of   markets   as   they   are   convinced   of   the   fact   that   European   markets   become   more   and   more   homogeneous.
However,   research   has   shown   that   local   customization   strategies   in   Central   and   Eastern   Europe   often   show   best   results   (Schuh   2007).
In   order   to   apply   local   customization   on   products,   a   holistic   understanding   of   the   Central   and   Eastern
European   market   is   needed   to   identify   similarities   and   differences   in   the   cultural,   political   and   economic   environment.
Research   has   shown   that   continuous   market   penetration   of   Fast   Moving
Consumer   Goods   (FMCG)   has   occurred   since   the   1990s   (Schuh   2007).
International   brands   have   gained   footage   on   these   markets   especially   in   the   less   cultural   bounded   products   like   detergents.
However,   local   food   products   still   remain   more   popular   compared   to   international   food   brands   and   there   is   still   a   lot   to   win   for   MNCs   in   these   FMCG   markets   (Schuh   2007).
This   indicates   that   local   customization   strategies   of   marketing   and   a   better   understanding   of   cultural   diversity   is   needed   for   firms   in   order   to   succeed   in   Central   and   Eastern   Europe   (Mooij   2009).
There   are   two   methods   suggested   in   literature   on   how   companies   can   gain   a   better   understanding   of   their   consumers   (Kim   and   Atuahene ‐ Gima   2010).
The   first   method   suggested   is   exploratory   organizational   learning.
Exploratory   market   learning   is   used   to   gain   knowledge   from   outside   the   organizations’   current   boundaries   and   provides   information   about   consumers   and   competitors.
The   second   method   suggested   in   literature   is   the   exploitative   organizational   learning   which   uses   consumer   and   competitor   information   within   the   neighbourhood   of   the   firm’s   current   expertise   in   order   to   provide   a   deeper   understanding   of   the   consumers’   actions   and   needs.
Especially   exploitative   market   learning   can   help   companies   to   gain   more   knowledge   on   consumer   needs   and   reasons   why   people   reject   new   technologies.
As   individuals   reject   new   technologies   for   different   reasons,   it   is   important   to   know   the   needs   of   the   consumers   when   considering   to   innovate   new   or   existing   products
(Onwezen   2011).
Examples   of   such   reasons   for   rejection   are   lack   of   trustworthiness,   lack   of   knowledge   about   possible   health   effects   and   unwillingness   to   pay   high   prices   for   new   product   innovations.
Still   many   companies   suffer   from   an   incomplete   understanding   of   their   consumers   despite   their   information   gathering   through   market   research.
This   incomplete   picture   prevents   managers   within   companies   to   take   appropriate   decisions   concerning   product   innovation   (Parry   and   Song   2010).
One   way   to   solve   this   incomplete   picture   is   to   engage   consumers   in   the   innovation   process   of   existing   and   new   products;   the   so ‐ called   customer   empowerment   (Fuchs   and   Schreier   2011).
Companies   have   to   transform   from   a   company ‐ centric   market   strategy   towards   a   consumer ‐ centric   market   strategy   in   which   consumers   are   seen   as   a   source   of   value   creation   (Prahalad   2004).
Through   personalized   interactions   with   consumers,   a   dialogue   is   formed   which   leads   to   more   transparency   and   a   better   understanding   of   consumer   needs.
In   order   to   achieve   such   a   dialogue   between   the   company   and   the   consumers,   the   managers   of   the   company   have   to   invest   in   new   technologies   enabling   more   interaction   (Prahalad   2004).
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Literature   Review
2.2.2
Competitors on the Market
Several   competitors   can   be   distinguished   on   the   Eastern   Europe   laundry   market.
The   number   of   competitors   observed   in   the   market   determine   the   “threat   of   substitute   products”   and   the   “threat   of   new   entry”   according   to   Porter’s   five   forces   model   (Slater   and   Olson   2002).
These   two   threats   affect   the   competitive   rivalry   within   the   industry   which   has   a   big   impact   on   the   Return   on   Investment   of   the   company.
The   main   producers   of   laundry   detergents   and   their   brands   are   listed   in   Table   2.
Each   of   these   producers   provides   laundry   detergents   on   three   price   levels:   Tier   1,   Tier   2   and   Tier   3.
The   higher   the   Tier   level   (i.e.
Tier   1),   the   higher   the   price   and   the   more   benefits   are   included   in   the   detergent   which   results   in   a   better   quality   of   the   product.
The   global   laundry   care   market   consisted   of   $52   billion   dollar   in   2009   where   a   share   of   34%   for   P&G,
18%   for   Unilever   and   11%   share   for   Henkel   (HAPPI   2011).
Reckitt ‐ Benckiser   and   Colgate ‐ Palmolive   both   held   5%   of   the   market   in   2009.
Focusing   on   the   laundry   care   market   of   interest,   the   largest   producers   of   laundry   products   are   P&G   with   a   market   share   of   47.2%   and   Henkel   with   a   market   share   of   29.4%   (CEEMEA   2009).
The   remaining   market   share   is   distributed   amongst   Unilever   (10%),
Reckitt   Benckiser   and   small   local   producers.
Although   both   P&G   and   Henkel   are   non ‐ native   companies,   they   have   gained   a   big   footage   on   the   market   which   may   be   due   to   the   non ‐ cultural   bounded   character   of   a   laundry   detergent.
The   high   market   shares   of   P&G   and   Henkel   make   it   difficult   for   new   companies   to   enter   the   market   which   reduces   the   “threat   of   new   entries” .
The   Herfindahl ‐ Hirschman   index   for   the   laundry   detergent   market   is   0.32
(3,200)   indicating   a   high   concentration   and   thus   an   oligopolistic   market.
The   oligopolistic   market   characteristic   together   with   the   strong   brand   equities   of   the   main   laundry   detergent   producers,   make   the   “threat   of   new   entries”   low.
The   “threat   of   substitute   products”   is   low   as   there   are   not   many   different   laundry   products   on   the   market.
However,   this   does   not   necessarily   mean   that   the   “ bargaining   power   of   customers”   is   limited.
The   switching   costs   for   consumers   are   low   which   means   that   the   companies   have   to   keep   the   customers   satisfied   and   need   to   listen   to   their   wishes   very   carefully.
Customers   can   easily   switch   products   if   they   are   not   satisfied.
The   “ bargaining   power   of   the   suppliers”   is   relatively   big   as   modern   trade   is   only   45%   of   the   market   while   open   markets   represent   a   large   part   of   the   trade   (CEEMEA   2009).
P&G   has   with   8,576   million   euro   (P&G   2010)   the   highest   investments   in   marketing   compared   to   4,257   million   euro   for   Henkel
(Henkel   2010)   and   1445   million   euro   for   Unilever   (Unilever   2010).
Based   on   these   numbers,   it   can   be   concluded   that   the   “intensity   of   competitive   rivalry”   is   limited   for   P&G   but   is   high   for   Henkel   and
Unilever.
Tier   1
Tier   2
Tier   3
Tier   4
Table   2:   Overview   of   all   the   key   competitors   and   their   available   brands   in   the   CEEMEA   area   (CEEMEA   2009).
Procter   &
Gamble
Ariel
Unilever   Henkel
Omo/Skip Persil
Tide/Vizir/Alo   Dero/Rinso   Losk/Dac/Extra
/Biopon/Palmex
Bonux/Myth/Acel
‐
‐
Reckitt   Benckiser
‐
‐
Rex/Deny/Tomi   Dosia/Bryza/Ava/Lanza
Pemos   ‐
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2.
Literature   Review
With   four   well ‐ known   brands   on   the   Russian   market   (i.e.
Ariel,   Persil,   Losk,   Tide),   the   threat   of   substitute   products   is   relatively   high.
Therefore,   both   P&G   and   Henkel   constantly   need   to   improve   their   products   to   keep   consumers   satisfied   and   to   maintain   their   market   share.
A   more   detailed   description   of   the   brand   is   given   in   section   2.3.
2.2.3
The Laundry Market
The   specified   detergent   market   is   one   of   the   most   important   markets   in   Central   and   Eastern   Europe
(Figure   2).
With   approximately   140.04
million   inhabitants   and   17   million   square   kilometres,   Russia   is   the   largest   country   in   the   world   by   land   area.
The   country   has   the   world’s   11 th
largest   economy   in   the   world   by   nominal   GDP   where   the   average   income   of   an   inhabitant   is   approximately   16,100
US$/year   per   capita   (based   on   GDP).
With   an   average   family   size   of   2   to   4   people   the   consumer   belongs   to   the   6 th
largest   by   purchasing   power   parity   and   belongs   to   the   top   5   of   most   important   markets   for   the   laundry   market.
Because   of   these   reasons,   this   country   is   an   interesting   area   for
FMCG   companies   to   market   their   products.
(CEEMEA   2009)
Despite   the   fact   that   the   economy   develops   relatively   fast,   the   modern   trade   only   represents   45%   of   the   total   laundry   volume   (supermarkets   and   minimarkets)   (Sipos   2008).
High   frequency   stores   and   open   markets   are   still   an   important   channel   to   trade   products.
However,   the   consumers   can   be   very   sceptical   when   it   comes   down   to   product   claims.
The   average   consumer   demands   more   scientific   explanation   of   how   the   product   works   compared   to   other   consumers   in   Central   and   Eastern   Europe
(CEEMEA   2009).
This   can   partly   be   explained   by   a   high   percentage   of   people   with   a   higher   education   and   partly   by   habit   of   distrust   to   promises   made   by   the   “officials”.
Additionally,   dressing   good   and   fashionable   is   a   common   practice   (CEEMEA   2009).
It   is   part   of   their   daily   standard   and   a   way   to   maintain   social   status.
Figure   2:   The   top   30   most   attractive   emerging   markets   for   international   companies   in   1995.
This   rank ‐ order   is   based   on   the   global   development   index   of   A.T.
Kearny.
(Igan   and   Suzuki   2011)
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Literature   Review
Table   3:   Market   split   by   country   for   Central   and   Eastern   Europe   (CEEMEA   2009).
One   MSU   is   one   thousand   statistical   units,   1   statistical   unit   is   8   kg.
Total   CEE Russia   Poland Turkey
Total   Laundry   (MSU) 319,376   53,457 33,631   44,192
Low   Suds
High   Suds
90%
10%
71.2%
28.8%
98%
2%
97%
3%
The   total   laundry   market   in   Central   and   Eastern   Europe   (CEE)   and   its   split   by   country   is   shown   in
Table   3.
This   table   illustrates   that   the   total   laundry   market   can   be   divided   in   a   Low   Suds   (LS)   and
High   Suds   (HS)   market.
The   LS   detergents   produce   fewer   suds   and   are   mostly   used   in   Front   Loading
Washing   Machines   whereas   the   HS   detergents   are   mainly   used   for   hand   washing.
This   research   will   only   focus   on   LS   detergents   as   more   and   more   people   switch   from   hand   washing   to   front   loading   washing   machines.
As   can   be   seen   from   Table   3,   Ru    ssia   is   the   biggest   market   in   Central   and   Eastern
Europe   with   the   largest   growth   potential   in   LS   as   the   group   of   HS   consumers   is   still   relatively   big.
The   total   low   suds   market   in   ussia   is   38,061   MSU   for   which   P   has   a   market   share   of   47.2%   (17,964
MSU).
Market   shares   are   depicted   in   MSU   which   represents   one   thousand   statistical   units   with   one   statistical   unit   representing   8   kg   of   detergent.
The   reason   for   using   MSU   as   a   unit   is   to   compare   other   products   with   laundry   detergents   which   is   essential   for   a   company   producing   several   types   of   products.
The   total   high   suds   market   for   Russia   is   considerably   smaller   with   15,395   MSU   and   a
Pshare   of   35.7%   (5496   MSU).
When   focusing   on   the   LS   market   in   Russia,   ’s   share   is   15.4%   with   a   market   volume   of   5,861   MSU.
All   these   numbers   show   that   the   low   suds   market   has   potential   to   grow   by   converting   high   suds   users   to   the   low   suds   detergents.
Also   Ti    d   e   has   room   to   grow   and   become   an   even   bigger   brand   on   the   market.
The   average   number   of   washes   for   the   Russian   consumer   is   5.2
to   6.0
loads   per   two   weeks.
The   most   popular   package   size   for   the   Russian   consumer   is   400   grams   which   they   buy   on   average   10   times   a   year   (Sipos   2008).
2.3
Pricing in the Detergent Market
There   are   only   a   few   key   competitors   active   on   the   Russian   laundry   care   market   as   was   seen   in   Table
2   of   section   2.2.1.
Each   of   these   large   laundry   care   firms   has   a   relatively   large   market   share,   i.e.
small   companies   represent   less   than   10%   of   the   market,   and   the   companies   are   relatively   independent   with   a   Herfindahl ‐ Hirschman   index   of   0.32
(or   3,200).
H

N 
i

1 s i
2 (1.1)
Here   H   is   the   index   number,   s   is   the   market   share   of   company   i   in   percentage   and   N   is   the   total   amount   of   companies   on   the   market.
The   higher   the   Herfindahl ‐ Hirschman   index,   the   larger   the   independence   of   the   different   companies   is.
Independence   means   in   this   case   that   the   profit   earned   by   each   firm   is   dependent   on   the   firm’s   own   actions   and   by   the   actions   of   its   direct   competitors.
Therefore,   the   laundry   detergent   market   is   considered   to   be   an   oligopolistic   market   instead   of   a   perfect   competition.
The   oligopolistic   behaviour   directly   affects   the   pricing   strategy   held   by   these   laundry   detergent   producers.
The   laundry   detergent   producers   have   to   face   a   prisoner’s   dilemma   in
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Literature   Review
which   they   can   choose   to   keep   the   current   price   or   to   decrease   the   price   in   order   to   gain   market   share.
In   most   cases,   the   large   laundry   detergent   producers   can   be   considered   to   have   a   pricing   leadership.
However,   the   laundry   detergent   market   is   divided   into   four   price   categories   which   reduce   the   prisoner’s   dilemma.
These   four   price   categories   are   Tier   1   to   Tier   4   in   which   the   Tier   1   price   category   is   the   highest   price   category   and   the   Tier   4   is   the   lowest   price   category.
The   higher   Tier   price   categories   represent   the   top ‐ line   products   with   high   performance.
Each   pricing   category   has   its   own   brand   to   target   the   consumers   within   this   pricing   category.
This   reduces   in   some   extend   the   prisoner’s   dilemma   as   each   large   detergent   producer   has   a   brand   in   each   of   the   pricing   segments   and   thus   target   all   consumers.
Reducing   the   price   of   a   Tier   1   brand   doesn’t   make   much   sense   as   then   this   brand   is   competing   with   one   of   their   own   Tier   2   brands.
In   this   research,   only   the   price   categories   Tier   1   (1.5
kg   =   €4.20),   Tier   2   (1.5
kg   =   €3.43)   and   Tier   3   (1.5
kg   =   €2.48)   are   considered   as   these   represent   the   largest   part   of   the   laundry   market.
2.4
Laundry Detergent Brand
[Confidential]
2.5
Market Segmentation
The   laundry   market   is   divided   in   different   segments   which   are   company   specific.
Each   detergent   producer   attempts   to   target   one   of   these   segments   by   adjusting   the   chemical   properties   of   their   brand.
In   this   case,   the   segmentation   is   described   and   will   be   used   in   the   remained   of   the   report.
This   segmentation   is   referred   to   as   the   new   segmentation   and   will   be   discussed   in   detail   below.
The   segmentations   is   derived   through   a   process   containing   3   stages.
In   the   first   stage   a   multifunctional   alignment   is   gained   to   ensure   they   address   the   core   business   issues   and   that   the   output   is   actionable   in   both   upstream   and   go ‐ to ‐ market   stages.
The   second   stage   is   the   data   collection   and   segmentation   definition   stage.
Data   is   collected   to   leverage   existing   or   new   knowledge   on   hypothesized   segmenting   and   profiling   variables.
The   consumers   are   then   segmented   in   an   infinite   variety   of   ways.
The   challenge   in   this   stage   is   to   identify   the   few   segmenting   variables   that   discriminate   consumer   behaviour.
The   segments   must   be   meaningful   from   a   consumer   perspective,   attractive   from   a   business   standpoint   and   relevant   to   the   core   business   to   be   addressed.
The   third   stage   is   about   bringing   the   segment   to   life   using   profiling   variables   to   create   a   robust   profile   of   the   segment.
(Ewing,   Findley   et   al.
2004)
[Confidential]
2.6
Customer Empowerment in Product Development
One   technology   to   improve   the   interaction   with   consumers   and   thereby   the   understanding   on   the   up ‐ tiering   consumers   behaviour   is   the   internet.
The   internet   can   be   used   as   an   interactive   platform   in   which   online   consumer   communities   are   formed   (Fuchs   and   Schreier   2011).
Internet   enables   firms   to   engage   with   consumers   in   the   innovation   process.
Research   indicates   that   the   internet   allows   firms   to   interact   with   consumers   more   broadly,   more   richly   and   faster.
It   creates   an   on ‐ going
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2.
Literature   Review   dialogue   between   the   consumers   and   the   firm   in   which   the   firm   is   neither   limited   by   geographical   nor   market   boundaries.
It   gives   these   firms   the   capacity   to   tap   into   the   social   dimensions   of   consumer   knowledge   (Sawhney   2005).
The   internet   platform   can   be   used   in   different   stages   of   the   product   innovation   process.
A   trade ‐ off   must   be   made   by   the   firm   between   reach   and   richness
(Sawhney   2005).
When   the   company   is   interested   in   generating   new   ideas   and   insights,   richness   is   chosen   while   reach   enables   a   firm   to   validate   hypotheses.
Many   companies   already   use   internet   platforms   like   advisory   panels;   an   example   of   such   advisory   panel   is   the   one   from   Procter   &   Gamble   (P&G)   with   their   P&G   Advisory   program   (Sawhney   2005).
This   online   advisory   program   allows   P&G   to   integrate   their   consumers’   innovative   new   product   ideas   into   the   development   process   more   actively,   more   directly   and   more   systematically   (Fuchs   and
Schreier   2011).
2.7
Virtual Prototyping
Besides   improving   the   interaction   with   consumers,   the   internet   offers   an   attractive   environment   for   new   product   market   research;   virtual   prototyping.
The   costs   of   building   and   testing   virtual   prototypes   are   considerably   lower   which   allows   more   product   concepts   to   be   tested   within   the   same   budget.
The   access   to   respondents   is   efficient   and   fast   feedback   can   be   provided   using   the   internet.
Nevertheless,   the   internet   may   result   in   sample   bias   from   using   web ‐ based   respondents   only   (Dahan   and   Srinivasan   2000).
Virtual   prototypes   help   to   gain   an   accurate   assessment   of   consumer   acceptance   which   will   enable   better   customized   products.
Not   only   price   competition   but   also   competition   on   product   variety   and   speed   to   market   determine   the   success   of   a   product   on   the   market   (Tseng,   Jiao   et   al.
1998).
Consequently,   (local)   customization   of   products   becomes   an   important   marketing   instrument   because   old   products   constantly   need   improvements   and   new   variations   on   old   products   need   to   be   introduced.
Short   product   cycles   are   crucial   for   a   company’s   success.
Virtual   prototyping   provides   a   rapid   prototyping   environment   in   which   concepts   can   be   developed   and   tested   using   simulations.
Research   has   shown   that   an   attractive   appearance   draws   customers   to   a   product   and   increases   customer   preferences   (Tseng,   Jiao   et   al.
1998).
However,   preference   of   laundry   detergents   is   mostly   based   on   performance   rather   than   appearance.
Identifying   the   elements   that   enhances   the   consumer   acceptance   of   laundry   products   requires   a   different   approach   of   virtual   prototyping.
A   conjoint   analysis   is   a   widely   used   methodology   to   measure   preferences   for   different   products   and   to   build   market   simulation   models   to   predict   market   shares.
Unfortunately,   these   conjoint   methods   are   again   focused   on   aesthetics   and   looks   of   the   product   and   not   on   the   performance   of   the   product.
A   conjoint   interface   must   be   developed   and   used   which   tells   the   user   how   the   prototype   will   perform   and   behave   in   its   intended   environment.
This   approach   is   called   analytical   virtual   prototyping   which   normally   uses   standard   computing   techniques   (Tseng,   Jiao   et   al.
1998).
Testing   prototypes   is   one   side,   whereas   the   most   challenging   decisions   faced   by   a   new   product   development   team   is   concept   selection;   the   narrowing   of   multiple   product   concepts   to   a   single,   best   design   (Dahan   and   Srinivasan   2000).
There   are   two   ways   to   select   the   best   concept:   prototyping   and   testing.
With   many   FMCG   companies   on   the   market,   time   to   market   is   critical   in   the   laundry
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2.
Literature   Review
detergent   market.
The   number   of   tested   prototypes   increases   with   the   increased   importance   of   time   to   market   (Dahan   and   Srinivasan   2000).
Therefore,   a   suitable   method   must   be   used   to   test   a   large   amount   of   prototypes   in   a   short   time   as   only   a   small   percentage   of   the   total   number   of   prototypes   proves   to   be   profitable   and   a   success   on   the   market   (Dahan   and   Srinivasan   2000).
2.7.1
Conjoint methodology
A   methodology   which   can   be   used   in   the   product   innovation   process   is   the   quantitative   conjoint   analysis.
Product   innovation   is   generally   conceptualized   as   a   five ‐ stage   New   Product   Development
(NPD)   process   consisting   of   ideation,   concept   development,   product   design,   product   testing   and   product   introduction   (Sawhney   2005).
By   using   consumer   input   through   the   conjoint   technique,   companies   can   create   new   product   faster.
The   conjoint   analysis   test   can   be   used   to   create,   test   and   refine   new   product   concepts   in   a   fast   and   efficient   manner   (Sawhney   2005).
A   special   form   of   the   conjoint   analysis   is   the   attribute ‐ based   conjoint   analysis   which   refers   to   the   understanding   of   customer   trade ‐ offs   on   multiple   product   attributes   and   price.
The   design   team   generates   multiple   product   concepts   that   address   customers’   needs   for   both   quantifiable   performance   attributes   and   qualitative   attributes   such   as   aesthetics   and   ease ‐ of ‐ use   (Dahan   and
Srinivasan   2000).
Whereas   attribute   based   conjoint   analysis   explains   a   significant   portion   of   the   variability   in   product   preferences,   it   does   not   explain   preferences   resulting   from   design   issues   such   as   aesthetics   and   ease   of   use   (Srinivasan,   Lovejoy   et   al.
1997).
However,   for   laundry   products   this   is   not   a   big   issue   as   the   consumer   preference   is   mostly   based   on   performance   and   not   on   aesthetics   of   the   laundry   detergent.
During   the   attribute ‐ based   conjoint   test,   the   consumer   is   asked   to   answer   questions   showing   virtual   concepts.
The   concepts   are   accompanied   by   different   attribute   areas   and   their   performance   levels   by   means   of   a   virtual   communication   interface.
The   consumer   is   then   asked   to   choose   the   virtual   product   she   is   willing   to   buy   for   the   indicated   price.
The   consumer   is   forced   to   make   a   trade ‐ off   between   the   different   performance   levels   of   attributes   and   its   price.
It   will   be   used   to   understand   how   consumers   build   their   preferences   for   detergents   and   how   they   combine   the   value   of   a   product   with   the   value   provided   by   each   attribute.
The   attribute ‐ based   conjoint   test   can   provide   valuable   information   on   which   benefit   attribute   is   most   important   at   which   price   level.
It   will   give   insights   on   trade ‐ off   behaviour   of   consumers   and   the   influence   on   the   Value   and   the   Overall   Rating   of   the   product.
2.7.2
Maximum Difference Scaling methodology
Another   method   often   used   in   market   research   is   the   so ‐ called   maximum   difference   scaling   test
(Maxdiff).
A   respondent   is   asked   to   select   both   the   best   and   worst   option   in   an   available   (sub)set   of   choice   alternatives.
The   assumption   behind   this   method   is   that   individuals   can   identify   the   best   and   worst   items   in   sets   of   three   or   more   and   that   the   pair   chosen   by   a   person   in   any   particular   set   is   the   pair   perceived   to   be   the   farthest   apart   on   an   underlying   scale   (Louviere   and   Islam   1998).
Despite   the   increased   use   of   Maxdiff   methodologies,   the   underlying   model   has   not   been   revealed   entirely,
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2.
Literature   Review   resulting   in   unclear   guidelines   on   appropriate   experimental   designs,   data   analysis   and   interpretation   of   results   (Marley   and   Pihlens   2011).
The   Maxdiff   methodology   is   in   fact   an   extension   of   paired   comparisons   which   is   an   established   methodology   in   sensory   and   consumer   sciences   (Jaeger,   Jorgensen   et   al.
2008).
The   methodology   adds   a   further   layer   of   sophistication   by   identifying   not   only   the   best   item   but   also   the   worst   item.
In   this   way,   it   provides   more   information   compared   to   paired   comparison   method.
Furthermore,   the   best ‐ worst   choice   data   can   be   transformed   to   a   probability   scale   when   analysed   by   a   multi ‐ nominal   logic   (Jaeger,   Jorgensen   et   al.
2008).
This   is   in   contrast   to   the   rating   scales   in   which   theoretical   scaling   properties   are   unknown.
There   is   often   little   differentiation   among   attribute   importance   measured   on   a   category   rating   scales.
The   advantage   of   the   rating   scale   is   that   it   requires   little   time   on   each   rating   and   all   attributes   are   rated   relatively   important.
However,   the   maximum   difference   scaling   approach   has   a   cognitive   psychology   basis,   which   is   easy   to   implement   and   in   which   it   is   easy   to   make   trade ‐ offs   between   attributes   (Louviere   and   Islam   1998).
Another   advantage   of   the   Maxdiff   methodology   is   that   an   increased   discrimination   among   items   is   achieved.
To   the   knowledge   of   the   author   of   this   thesis   report,   no   previous   published   studies   exist   that   combine   both   a   conjoint   and   Maxdiff   methodology   together   in   a   market   research   to   perceive   a   better   understanding   on   consumer   behaviour.
‐ 22 ‐

3.
Theoretical   Framework
In   this   chapter   a   theoretical   framework   is   proposed   which   will   set   the   foundation   of   the   research.
This   theoretical   framework   will   represent   the   beliefs   on   how   the   consumer   acceptance   of   a   laundry   detergent   is   influenced   by   different   parameters.
The   full   theoretical   framework   is   presented   in
Figure   3   in   paragraph   4.2.
First   the   different   variables   will   be   discussed   followed   by   a   thorough   discussion   of   the   theoretical   framework   itself.
3.1
Variables
The   variable   of   interest   in   this   research   is   the   consumer   acceptance   of   the   laundry   detergent   which   is   represented   by   a   so ‐ called   Weighted   Purchase   Intent   (WPI)   or   willingness   to   pay.
The   WPI   is   derived   from   the   welfare   economics.
It   represents   the   marginal   rate   of   substitution   of   particular   attributes   for   money.
There   are   two   advantages   of   using   the   WPI   as   a   measure   for   consumer   acceptance   (Louviere   and   Islam   1998):
1) The   utility   for   each   attribute   in   a   conjoint   test   is   measured   on   an   interval   scale   which   is   unique   to   each   attribute   and   individual.
This   makes   it   impossible   to   compare   estimated   attribute   utilities.
The   measure   of   WPI   for   each   attribute   is   stated   in   common   dollars/euros   allowing   comparison.
2) All   discrete   choice   models   blur   the   magnitudes   of   estimated   attribute   parameters   with   the   error   variance   of   individuals   such   that   large   error   variances   lead   to   small   parameter   estimates.
The   WPI   ratios   cancel   this   blurring   behaviour   between   individuals,   allowing   a   clear   comparison   of   the   numbers.
A   high   weighted   purchase   intent   indicates   that   consumers   are   sufficiently   excited   by   the   product   and   thus   willing   to   spend   money   on   the   product.
Therefore,   it   is   important   to   discover   how   the   WPI   is   influenced   by   different   independent,   moderating   and   intervening   variables.
A   good   local   customized   strategy   should   eventually   lead   to   a   higher   WPI   and   thereby   preserve   or   increase   the   market   share   of   the   brand.
The   relationship   between   the   different   attribute   levels   of   the   product,   the   overall   rating   of   the   product   (OAR)   and   the   WPI   is   of   critical   importance   for   managers   in   a   customer ‐ driven   organization.
A   high   overall   rating   increases   loyalty,   reduces   price   elasticity,   increases   cross ‐ buying   and   results   in   a   positive   word   to   mouth   (Matzler,   Bailom   et   al.
2004).
In   other   words,   high   attribute   levels   result   in   a   high   overall   rating   which   eventually   leads   to   a   higher   WPI.
The   relationship   between   attribute   level,
OAR   and   WPI   used   to   be   conceptualized   as   linear   and   symmetric.
However,   research   has   shown   that   negative   performance   of   an   attribute   has   a   greater   impact   on   the   overall   rating   and   WPI   compared   to   a   positive   performance   on   the   same   attributes   (Mittal,   Ross   Jr.
et   al.
1998).
Thus,   attribute   satisfaction   and   dissatisfaction   have   a   significant   effect   on   the   overall   rating   of   a   product   where   dissatisfaction   has   a   larger   weight   compared   to   satisfaction.
It   is   thought   that   the   attribute   performances   affect   satisfaction   and   WPI   in   a   different   way.
Satisfaction   is   a   judgment   with   cognitive   and   affective   dimensions,   whereas   repurchase   intension   also   has   a   behavioural
‐ 23 ‐

3.
Theoretical   Framework   component.
Therefore,   the   overall   rating   and   performance   are   related   nonlinearly   to   repurchase   intentions   or   loyalty.
(Mittal,   Ross   Jr.
et   al.
1998)
Previous   research   shows   that   attribute   performance   has   an   indirect   impact   on   repurchase   intentions   through   its   effect   on   the   overall   rating   of   the   product   (Mittal,   Ross   Jr.
et   al.
1998).
Multiple   regression   models   are   normally   used   to   identify   the   key   attributes   of   a   product   in   which   managers   should   invest   their   (limited)   resources.
The   assumption   underneath   these   so ‐ called   key ‐ driver   models   is   the   existence   of   a   symmetric   and   linear   relationship   between   attribute   performance,   overall   rating   and   WPI.
Several   reasons   can   be   identified   for   implementing   a   multiple   regression   model   (Mittal,   Ross   Jr.
et   al.
1998).
First,   consumers   evaluate   the   product   based   on   their   post   purchase   expectations   of   satisfaction   at   an   attribute   level   rather   than   at   the   product   level.
Second,   an   approach   based   on   attributes   enables   researchers   to   conceptualize   common   observed   phenomena.
Mixed   feelings   might   exist   as   consumers   may   be   satisfied   with   some   of   the   attributes   but   dissatisfied   with   the   other   attributes.
Therefore,   studying   the   overall   rating   at   an   attribute   level   can   help   extend   both   the   conceptual   and   the   empirical   understanding   of   the   phenomena   (Mittal,
Ross   Jr.
et   al.
1998).
The   importance ‐ performance   analysis   (IPA)   has   long   been   an   important   model   to   investigate   the   relationship   between   quality   attribute   performance   and   overall   rating   (Matzler,   Bailom   et   al.
2004).
The   assumption   behind   this   model   was   the   linear   relationship   between   these   two   factors   and   the   symmetric   behaviour.
However,   research   from   Mittal   et   al.
showed   that   there   exist   a   non ‐ linear   and   non ‐ symmetric   relationship   between   quality   attribute   performance   and   OAR.
Kano’s   model   of   satisfaction   also   disconfirms   the   basic   assumption   of   the   IPA   model   (Matzler,   Bailom   et   al.
2004).
Kano   states   that   satisfaction   is   formed   through   a   cognitive   comparison   of   perceived   performance   and   pre ‐ purchase   expectations.
This   Kano   model   of   satisfaction   will   be   used   in   combination   with   the   conjoint   test   data   to   develop   useful   recommendations   for   the   company.
The   Kano   model   will   be   further   explained   in   the   next   chapter.
Another   methodology   often   used   to   identify   the   relationship   between   attributes   and   overall   rating   is   the   linear   regression   method.
Because   of   the   collinearity   among   the   attribute   performances   when   used   as   predictors   of   overall   performance,   this   method   can   lead   to   poor   precision   of   the   regression   coefficients   (Abalo,   Varela   et   al.
2007).
A   second   criticism   of   linear   regression   method   is   that   the   relationship   between   the   overall   performance   of   a   product   and   its   performance   with   respect   to   its   individual   attributes   may   well   be   nonlinear.
As   is   discussed   earlier,   the   conjoint   analysis   may   be   a   better   option   as   this   method   has   the   advantage   of   using   an   orthogonal   design   which   excludes   the   possibility   of   collinearity   (Abalo,   Varela   et   al.
2007).
Furthermore,   the   use   of   several   levels   for   each   attribute   reduces   the   risk   of   non ‐ linear   dependence   of   the   overall   rating   on   attribute   performances.
However,   the   disadvantage   of   the   conjoint   analysis   is   that   it   requires   a   large   data   collection   process   and   becomes   unfeasible   when   involving   more   than   a   very   few   attributes.
(Abalo,   Varela   et   al.
2007)
Thus,   the   weighted   purchase   intent   is   influenced   by   the   overall   rating   of   the   product   which,   on   his   turn,   is   influenced   by   the   performance   of   several   product   attributes.
The   next   step   is   to   identify   the   main   product   attributes.
When   identifying   these   attributes   it   should   be   reminded   that   the   number
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3.
Theoretical   Framework
of   attributes   should   be   limited.
The   reason   for   limiting   the   number   of   attributes   is   that   the   data   collection   process   becomes   unfeasible   when   involving   more   than   a   few   attribute   areas.
There   are   four   product   attribute   areas   chosen   based   on   the   chemical   properties   of   a   laundry   detergent.
In   chapter   2   a   description   of   the   different   chemical   building   blocks   was   given   and   four   main   building   blocks   were   identified.
These   four   building   blocks   will   be   used   as   the   basis   for   four   product   attributes   which   will   be   used   in   the   theoretical   framework.
These   four   attributes   are   treated   as   independent   variables   as   they   can   be   adjusted   by   the   chemical   configuration   of   the   laundry   detergent.
In   this   theoretical   framework,   the   four   performance   vectors   are   pictured   as   independent   variables   but   in   reality   these   four   vectors   influence   each   other   and   can   cause   multi   co ‐ linearity   issues.
An   increase/decrease   in   one   can   cause   an   increase   or   decrease   in   one   or   more   of   the   other   vectors.
In   order   to   fully   understand   the   influence   of   these   four   performance   vectors,   these   will   be   discussed   separately.
Whiteness:   The   performance   vectors   whiteness   and   cleaning   are   closely   related.
When   focusing   on   the   vector   whiteness,   the   actual   brightness   of   the   white   fabric   is   meant.
Laundry   detergents   can   give   a   blue   or   pink   hue   to   white   fabric   depending   on   the   preference   of   the   region/country.
Therefore,   the   perception   of   whiteness   is   preference   based   and   can   influence   the   WPI   and   OAR   of   the   detergent.
Cleaning:   As   explained   earlier,   whiteness   and   cleaning   are   closely   related.
When   talking   about   cleaning,   the   actual   stain   removal   on   fabrics   is   meant.
Different   types   of   stains   are   possible   and   each   detergent   targets   different   groups   of   stains.
The   cleaning   vector   can   have   a   large   impact   on   the   WPI   and   the   AOR   of   a   laundry   detergent   product.
Care:   The   variable   care   is   harder   to   explain   as   each   consumer   can   interpret   this   vector   in   a   different   way.
Care   can   be   subdivided   into   several   categories:   care   for   fabric,   colour   care,   softness   and   skin   care.
Each   of   these   attributes   determines   the   overall   rating   for   care.
However,   the   weights   put   on   each   of   these   attributes   can   differ   among   consumers   and   among   countries.
Freshness:   The   vector   freshness   is   related   to   the   scent   of   the   detergent.
During   the   laundry   process,   there   are   different   moments   when   consumers   experience   the   scent   of   the   detergent:   when   opening   the   package   of   the   laundry   product   itself,   during   drying   the   wet   clothes,   the   scent   in   the   washing   room,   the   dry   scent   when   putting   the   clothes   in   the   closet,   the   scent   during   wearing   and   the   cupboard   scent.
Each   of   these   scent   experiences   can   have   a   large   impact   on   the   product   experience   and   thereby   on   the   WPI   and   OAR   of   the   product.
However,   the   OAR   is   not   only   influenced   by   these   four   product   attributes.
There   are   also   some   moderating   variables   which   have   a   strong   contingent   effect   on   the   overall   product   rating   and   thereby   influencing   the   WPI.
One   of   these   moderating   variables   is   the   brand   equity.
The   value   of   a   brand   lies   in   what   consumers   have   experienced   with   the   brand   in   the   past.
This   past   experience   constitutes   a   brand   image   which   has   a   large   effect   on   the   behaviour   of   the   consumer.
Therefore,   brand   associations   are   very   important   building   blocks   for   customer ‐ based   brand   equity   (Torres   and
Bijmolt   2009).
The   association   is   from   the   brand   to   the   product   attribute   and   the   other   way   around.
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3.
Theoretical   Framework
Brand   equity   consists   of   two   components:   brand   awareness   and   brand   image.
Brand   awareness   is   related   to   the   strength   of   the   brand   as   reflected   by   the   ability   to   identify   the   brand   under   different   conditions   while   brand   image   can   be   defines   as   the   consumers’   perception   about   a   brand   as   reflected   by   brand   associations   held   in   memory   (Torres   and   Bijmolt   2009).
Especially   the   brand   association   held   in   memories   are   affected   by   past   experience   with   the   brand   and   is   defined   by   the   different   product   attributes.
Consumers   generally   use   brand   awareness   as   a   decision   heuristic   in   which   they   link   the   related   brand   knowledge   to   the   brand   name,   which   finally   constitutes   brand   equity.
In   general,   literature   indicates   a   positive   relationship   between   brand   awareness   and   market   outcome.
However,   other   factors   such   as   the   shopping   environment,   product   placement,   and   on ‐ the ‐ spot   promotion,   are   also   likely   to   influence   the   purchase   decision.
(Huang   and   Sarigöllü   2012)   Therefore,   it   is   important   to   include   brand   equity   in   the   theoretical   framework   as   a   moderating   variable.
Each   product   is   accompanied   by   one   or   more   benefits   which   are   captured   in   a   statement.
The   way   this   statement   is   set   up   and   communicated   towards   the   consumer   can   influence   the   WPI   and   OAR   independent   of   the   product   attributes.
This   is   strongly   related   to   the   concept   communication   as   most   concepts   are   covered   in   a   benefit   statement.
However,   concept   communication   includes   more   such   as   commercials,   packaging   and   images.
3.2
Theoretical Framework
In   Figure   3,   a   schematic   representation   of   the   theoretical   framework   is   shown.
As   can   be   seen   from   this   picture,   the   product   attributes   have   a   direct   influence   on   the   overall   rating.
However,   the   benefit   statements   and   the   Brand   Equity   have   a   disturbing   effect   on   this   relationship.
In   the   end,   the
OAR   determines   the   weighted   purchase   intend   of   the   product   and   thus   the   product   satisfaction.
Figure   3:   Theoretical   framework   as   proposed   in   this   research   report.
There   are   four   independent   variables   which   affect   two   intervening   variables.
These   intervening   variables   are   also   affected   by   one   moderating   variable.
The   dependent   variable   is   the   consumer’s   affinity   to   the   brand.
Despite   this   figure   implicates   the   use   of   a   linear   regression   method,   this   method   will   not   be   used   due   to   some   disadvantages   as   discussed   earlier.
Because   of   the   collinearity   among   the   attribute
‐ 26 ‐

3.
Theoretical   Framework
performances   when   used   as   predictors   of   overall   performance,   this   method   can   lead   to   poor   precision   of   the   regression   coefficients   and   the   relationship   between   the   overall   performance   of   a   product   and   its   performance   with   respect   to   its   individual   attributes   may   well   be   nonlinear   (Abalo,
Varela   et   al.
2007).
Therefore,   the   relationship   between   the   four   product   attributes   and   the   overall   rating   of   the   product   will   be   investigated   by   means   of   the   Kano   model   and   by   means   of   so ‐ called   desirability   functions.
This   is   further   explained   in   the   next   chapter.
The   first   challenge   is   to   design   a   test   which   provides   more   insight   in   the   relationship   between   the   four   benefit   attributes   and   their   effects   on   the   overall   rating.
During   this   test,   the   effect   of   the   moderating   variables   on   the   overall   rating   is   reduced   by   not   including   brand   names   and   benefit   statements   in   the   conjoint   test.
The   next   challenge   is   to   investigate   the   effect   of   the   moderating   variables   on   the   overall   rating.
The   effect   of   the   brand   equity   and   the   effect   of   the   concept   of   the   product   will   not   be   considered   in   this   research.
To   investigate   the   effect   of   benefit   statements   on   the   overall   rating,   the   Maxdiff   test   will   be   developed.
More   information   on   the   different   research   methods   will   be   given   in   the   next   chapter.
‐ 27 ‐

3.
Theoretical   Framework
‐ 28 ‐

4.
Research   Methods
Several   data   collection   methods   were   proposed   in   chapter   2   and   4.
This   chapter   provides   an   outline   of   the   proposed   research   methods   illustrated   with   a   few   examples.
The   second   part   of   this   chapter   will   discuss   the   operationalization   of   the   different   variables   of   the   theoretical   framework   as   described   in   the   previous   chapter.
4.1
Data Collection Procedures
The   different   data   collection   procedures   will   be   discussed   in   this   section.
Both   Qualitative   and   quantitative   data   collection   procedures   are   used.
Qualitative   research   will   be   used   to   develop   a   quantitative   test   which   will   be   placed   among   consumers.
The   results   from   the   quantitative   test   will   be   supported   and   verified   by   further   qualitative   data   derived   from   an   internet   panel.
4.1.1
Qualitative Research
The   qualitative   part   of   this   research   will   be   performed   in   two   ways:   by   means   of   consumer   interviews   in   a   so ‐ called   consumer   lounge   and   by   means   of   an   internet   panel.
Both   qualitative   methods   will   be   based   on   structured   (online)   interviews   and   are   discussed   below.
Consumer   lounge:   The   consumer   lounge   is   a   special   facility   where   consumers   can   be   invited   for   an   in ‐ depth   interview.
The   facility   simulates   a   living   house   containing   several   rooms   like   a   living   room,   a   kitchen,   dining   room   and   a   bathroom.
Besides   the   normal   housing   areas   the   facility   also   holds   a   small   supermarket   where   the   shopping   behaviour   of   consumers   can   be   observed.
Each   room   is   provided   with   cameras   and   microphones   in   order   to   observe   consumers   during   their   normal   day   activity.
The   aim   of   the   exploratory   interviews   in   the   consumer   lounge   is   to   test   early   interfaces   for   an   online   quantitative   test   to   be   placed.
The   interview   consists   of   three   parts   which   in   total   takes   1.5
hour.
In   the   first   part   the   consumer   will   see   different   illustrations   related   to   the   four   benefit   areas   of   laundry   detergents:   cleaning,   whiteness,   care   and   freshness.
These   illustrations   will   be   used   for   communicating   the   different   benefit   levels   for   the   conjoint   test.
The   second   part   of   the   interview   will   be   focused   on   different   scales   to   communicate   the   level   of   each   benefit   area.
Different   scales   are   shown   and   the   respondent   is   asked   to   give   her   opinion   on   the   type   of   communication.
The   last   part   of   the   interview   will   focus   on   benefit   statements   in   combination   with   illustrations.
Again,   the   consumer   is   asked   to   give   feedback   on   the   combination   of   the   benefit   statement   and   the   illustration.
During   the   interview   consumers   are   constantly   stimulated   by   illustrations   in   order   to   visualize   their   thoughts.
The   consumer   is   talking   and   the   interviewer   will   interrupt   as   little   as   possible.
The   interviews   took   place   in   the   consumer   lounge   where   the   respondent   was   interviewed   directly   by   the   interviewer.
The   downside   of   the   consumer   lounge   is   that   this   facility   is   located   in   Brussels   and   not   in   the   target   country.
Therefore,   the   consumers   coming   to   this   facility   are   mainly   expats   who   used   to   live   in   their   country   of   origin   but   have   moved   to   Belgium.
It   is   therefore   important   to   select   the   respondents   for   the   consumer   lounge   based   on   the   time   they   have   spent   in   their   country
‐ 29 ‐

4.
Research   Methods   of   origin   and   the   time   they   have   spent   in   Belgi      um.
The   respondents   have   to   be   representative   for   their   home   country   and   therefore   a   limit   of   3   years   in   Be         gium   is   often   taken.
Based   on   these   selection   criteria,   6   Eastern   European   female   interviewees   were   selected   who   are   strongly   involved   in   the   purchase   decision   of   the   laundry   products   and   the   laundry   process   itself.
Internet   panel:   As   explained   in   the   literature   research,   consumer   engagement   becomes   more   and   more   important   in   the   FMCG   market.
Especially   involving   and   engaging   consumers   in   the   innovation   process   of   existing   and   new   products   become   the   new   challenge   as   speed ‐ to ‐ market   is   crucial.
In   order   to   anticipate   on   this   new   development,   the   company   decided   to   launch   an   internet   panel   where   researchers   can   post   questionnaires,   add   quick   polls,   can   introduce   topics   on   a   forum   and   can   chat   with   consumers.
By   means   of   this   online   panel,   the   company   will   be   able   to   quickly   receive   results   from   an   online   community.
The   internet   panel   will   be   used   to   gather   further   information   on   data   derived   from   the   quantitative   test.
The   internet   panel   will   probe   on   observations   from   the   quantitative   test   and   gather   qualitative   rather   than   quantitative   data   to   support   hypothesis   and   observations.
It   will   help   to   investigate   the   influence   of   brand   equity   and   product   concepts   on   the   value   of   the   product   and   its   effect   on   the   overall   rating   of   the   product.
The   online   community   consists   of   consumers   divided   over   different   market   segments.
For   each   research,   a   specific   target   group   can   be   selected   in   order   to   get   the   right   information.
The   language   used   on   the   internet   panel   is   native   which   means   that   each   questionnaire   will   be   thoroughly   translated   and   checked   before   posting.
There   are   many   possibilities   with   the   internet   panel,   opening   a   variety   of   opportunities.
Using   these   possibilities,   a   real   two ‐ way   conversation   can   be   established   with   the   consumer   involving   them   in   the   innovation   process   of   a   product.
It   is   important   to   give   the   consumer   the   feeling   they   are   taking   part   in   a   community   and   that   they   can   have   an   influence   in   the   whole   innovation   process.
Therefore,   it   is   essential   to   keep   the   internet   panel   alive   and   attractive   by   posting   approximately   two   questionnaires   a   week,   one   quick   poll   a   week   and   keeping   involved   on   the   forum.
4.1.2
Quantitative Research
Quantitative   data   will   be   a   large   part   of   this   research.
In   order   to   perform   the   quantitative   research   in   an   efficient   way,   the   Design   of   Experiment   (DOX)   approach   will   be   used.
DOX   is   a   designed   experiment   containing   a   test   or   a   series   of   tests   in   which   purposeful   changes   are   made   to   the   input   variables   of   a   process   or   a   system   in   order   to   observe   and   identify   reasons   for   the   change   in   the   output   of   the   system   (Myers   2007).
The   advantage   of   using   the   DOX   approach   is   that   experiments   can   be   designed   in   such   a   way   that   a   minimum   of   test   samples   is   needed   in   order   to   predict   the   outcomes   of   a   whole   group/segment   in   a   statistical   and   significant   manner.
One   of   these   DOX   approaches   is   called   the   factorial   design.
This   approach   will   model   all   the   possible   factor   combinations   and   include   them   in   the   DOX   design.
When   each   factor   has   only   2   measurement   levels,   the   total   number   of   test   samples   is   calculated   by   2 k
(Myers   2007).
Another   DOX   approach   is   the   computer   generated   (custom)   design   in   which   the   researcher   can   select   the   total   number   of
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4.
Research   Methods
samples   (Myers   2007).
The   program   will   then   select   the   best   combination   of   samples   in   order   to   make   statistical   representative   model.
This   approach   is   very   helpful   when   there   is   limited   time   or   funding   to   perform   the   complete   set   of   test   samples.
In   this   research,   the   computer   generated
(custom)   design   is   used   to   select   the   sample   base   for   the   conjoint   and   Maxdiff   test.
A   Quantitative   test   will   be   placed   to   broaden   the   consumer   knowledge   on   the   laundry   market   of   interest.
This   test   is   based   on   exploitative   organizational   learning   which   uses   consumer   and   competitor   information   within   the   neighbourhood   of   the   firm’s   current   expertise   in   order   to   provide   a   deeper   understanding   of   the   consumers’   actions   and   needs.
It   can   help   companies   to   gain   more   knowledge   on   consumer   needs   and   the   reasons   why   people   reject   new   technologies.
(Onwezen
2011)
Virtual   Product   Prototyping   test:   The   so ‐ called   virtual   product   prototyping   test   refers   to   a   conjoint   and   maximum   difference   scaling   (Maxdiff)   test.
The   two   test   methodologies   will   be   combined   in   a   virtual   product   experience   where   consumers   are   confronted   with   different   virtual   (non ‐ existing)   products.
The   Maxdiff   methodology   will   show   statements   to   consumers   from   which   the   respondent   has   to   choose   the   best   and   worst   statement.
More   details   on   the   Maxdiff   test   design   will   be   given   in   the   next   chapter.
The   conjoint   will   force   respondents   to   make   trade ‐ offs   between   different   product   attributes   and   price   which   will   be   further   discussed   below.
A   total   base   size   of   3    45   consumers   is   recruited   with   the   focus   on   the   strategic   target:   Organizers,
Protectors   and   Enjoyers.
An   over   quota   is   introduced   to   make   sure   the   minimum   base   size   for   a   breakout   analysis   on   the   conjoint   is   established.
Based   on   the   market   penetration   data   this   means   that   there   will   not   be   enough   Protectors   recruited   to   do   a   conjoint   analysis   on   this   segment.
Reason   for   not   having   an   over   quota   for   Protectors   is   budget   restrictions.
Another   over   quota   is   established   regarding   the   P3M   MO   Tier   3   consumers.
Recruiting   was   done   using   a   questionnaire   developed   by   the   marketing   department.
In   total   100   Organizers,   196   Enjoyers   and   49   Protectors   were   recruited.
From   this   consumer   base,   114   Tier   1,   106   Tier   2   and   125   Tier   3   users   were   recruited   which   enables   a   breakout   on   these   user   groups.
The   analysis   of   the   conjoint   data   is   done   in   the   software   JMP.
This   statistical   software   is   able   to   build   a   model   based   on   different   product   attributes;   in   this   case   the   attributes   discussed   in   chapter   4.
The   model   consist   of   a   so ‐ called   prediction   profiler   based   on   a   desirability   function   (Ramsey,   Stephens   et   al.
2005).
The   desirability   is   based   on   values   assigned   to   each   consumer   response   that   reflects   its   desirability.
The   JMP   software   defines   the   desirability   function   based   on   control   parameters   set   by   the   researcher.
One   of   these   parameters   is   an   importance   factor   for   each   attribute   which   can   be   included   when   one   attribute   weights   more   to   the   consumer   acceptance   compared   to   another   attribute.
Each   attribute   will   be   represented   by   three   levels   to   indicate   the   performance   of   the   attribute   (i.e.
low,   medium   and   high).
The   output   of   this   software   is   given   in   five   graphs   in   which   the   performance   levels   of   each   attribute   can   be   adjusted   independently.
This   model   takes   two ‐ way   interactions   into   consideration   which   means   that   sliding   one   of   the   sliders   on   the   x ‐ axis   affects   the   steepness/shape   of   the   other   four   remaining   curves.
Only   two ‐ way   interactions   are   taken   into   account   as   the   three ‐ way   interactions   are   too   complicated   to   analyse   and   interpret.
An   example   of
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4.
Research   Methods   one   of   the   conjoint   models   is   shown   in   Figure   4.
The   y ‐ axis   of   the   graph   represents   the   utility   or   consumer   acceptance.
The   higher   the   utility,   the   more   preferred   the   product   is   by   consumers.
Figure   4:   Example   of   a   consumer   model   from   the   JMP   software.
The   y ‐ axis   shows   the   utility   or   consumer   acceptance   while   the   x ‐ axis   shows   the   different   levels   of   benefit.
Each   level   can   be   adjusted   independently   and   will   affect   the   steepness   of   the   curves.
The   higher   the   utility,   the   more   preferred   the   product   by   consumers.
When   observing   the   curvature   of   the   plots   in   Figure   4,   it   can   be   seen   that   the   effect   of   each   of   the   attribute   performances   is   not   always   linear   and   symmetric.
This   confirms   the   proposals   made   by
Mittal   and   Matzler   that   the   effect   of   each   attribute   on   WPI   is   not   linear   and   symmetric   but   can   be   asymmetric   and   non ‐ linear.
Therefore,   the   Kano   model   will   be   used   to   analyse   the   results   of   the   conjoint   model.
This   will   be   explained   next.
In   order   to   analyse   the   graphs   of   the   different   consumer   models,   Kano’s   two ‐ dimensional   quality   model   is   used.
The   Kano   model   is   an   effective   tool   for   categorizing   product   benefits   according   to   their   effect   on   consumer   acceptance   (Chen   and   Chuang   2008).
Researchers   have   believed   in   the   past   that   the   consumer   satisfaction   is   proportional   to   the   level   of   benefit   performance   (Chen   2012).
However,   Kano   revealed   that   there   exist   a   non ‐ linear   asymmetrical   relationship   between   satisfaction   and   performance   (Kano,   Seraku   et   al.
1984).
Based   on   the   Kano   classification   a   valuable   guidance   in   trade ‐ offs   can   be   offered   in   the   product   development   stage.
In   this   case,   the   Kano   model   consists   of   five   dimensions   (4   benefit   areas   and   price).
Each   of   these   dimensions   can   be   classified   based   on   their   relationship   between   customer   satisfaction   and   product   criterion.
These   classifications   are   shown   in   Figure   5   and   are   described   as   (Chen   and   Chuang   2008):
1) Must ‐ haves:   customers   become   dissatisfied   when   the   benefit   is   not   present   but   satisfaction   is   not   much   increased   when   benefit   is   highly   present.
2) Linear   satisfiers:   customer   satisfaction   is   a   linear   function   of   benefit   level   presence.
High   benefit   levels   result   in   high   customer   satisfaction   and   vice   versa.
3) Delighters:   customer   satisfaction   increases   super   linear   with   increasing   attribute   performance.
When   the   attribute   is   not   there,   customer   satisfaction   will   hardly   decrease.
4) Indifferent:   the   presence   of   absence   of   the   benefit   attribute   has   hardly   any   effect   on   customer   satisfaction.
5) Reverse   quality:   customer   satisfaction   decreases   when   benefit   is   present   and   increase   when   absent.
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4.
Research   Methods
Each   of   the   five   product   attributes   can   be   characterized   using   the   Kano   model.
This   reveals   valuable   information   on   which   attributes   are   important   for   consumers   and   which   effect   each   attribute   has   on   consumer   acceptance.
Before   the   Kano   model   can   be   used   to   classify   each   attribute,   the   ideal   product   in   the   right   price   category   must   be   specified   for   each   consumer   group,   meaning:   for   every   segment   or   group   of   consumers,   the   ideal   distribution   of   attribute   levels   must   be   established   resulting   in   the   highest   utility   or   consumer   acceptance   in   the   model.
The   procedure   for   extracting   the   ideal   product   from   the   model   is   explained   below.
Figure   5:   Kano’s   two ‐ dimensional   quality   model   and   it’s   characterization   of   the   different   benefits   (Chen   2012).
Because   two ‐ way   interactions   are   incorporated   into   the   consumer   model,   this   means   that   the   shape   of   the   curve   is   dependent   on   the   levels   set   for   each   benefit   attribute.
First,   the   price   level   is   set   to   the   right   level   because   the   consumer   satisfaction   for   that   particular   product   is   modelled.
In   reality   it   is   impossible   to   offer   high   levels   of   each   benefit   at   low   prices   or   no   benefit   at   all   at   high   prices.
To   simulate   reality   it   is   decided   that   at   a   Tier   2   price   level,   in   total   8   “stars”   can   be   divided   among   the   four   benefit   areas.
In   order   to   find   the   product   with   8   stars   which   gives   the   highest   consumer   preference   the   “stars”   are   divided   each   one   after   the   other.
Each   time   the   “star”   is   given   to   the   benefit   area   with   the   steepest   curve   or   highest   utility   increase.
This   process   is   shown   in   Figure   6.
This   process   can   be   seen   as   giving   the   consumer   a   coin   and   asking   her   the   question   where   she   would   invest   this   coin.
Based   on   ,   her   first   answer   will   be   to   invest   in   cleaning.
The   second   coin   that   is   given   will   be   invested   in   whiteness   and   so   on.
This   yields   the   most   preferred   product   according   to   the   consumer.
Applying   this   method,   the   most   preferred   product   in   the   particular   price   category   is   specified   for   the   strategic   target,   the   segments   within   the   strategic   target   and   the   different   Tier   groups   within   the   strategic   target.
The   next   step   is   to   identify   each   curve   according   to   Kano’s   model.
This   will   be   done   in   the   next   chapter.
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4.
Research   Methods
Figure   6:   Example   of   how   the   ideal   Tier   2   product   is   found.
In   the   first   picture   it   can   be   seen   that   attribute   2   gives   the   highest   utility   increase.
Therefore,   this   benefit   attribute   is   set   from   level   1   to   level   2.
In   total   5   “stars”   are   divided   among   the   four   benefit   areas.
Then   first   the   price   will   increase   as   an   investment   of   6   “stars”   is   too   expensive   at   a   Tier   3   price   level.
Next,   attribute   1   gives   the   biggest   utility   increase   which   means   that   the   next   “star”   will   be   given   to   the   attribute   1   benefit.
This   goes   on   until   8   stars   are   divided   among   the   four   benefit   areas.
4.2
Operationalization and Measurement Variables
The   operationalization   of   the   different   variables   in   the   theoretical   framework   is   an   important   aspect   of   this   research   as   it   indicates   how   the   variables   should   be   measured.
Therefore,   this   section   will   focus   on   how   to   operationalize   the   different   variables   of   the   theoretical   framework   and   how   to   measure   them   in   order   to   answer   the   research   questions.
The   most   important   (dependent)   variable   is   the   consumer   acceptance   or   weighted   purchase   intent.
This   affinity   can   be   described   as   the   consumer   experience   of   the   product.
Therefore,   overall   rating   of   the   product   is   an   important   aspect   to   operationalize   the   WPI.
Furthermore,   the   product   sales   are   an   important   way   to   check   the   WPI.
The   overall   rating   also   has   to   be   operationalized   in   order   to   measure   it.
The   WPI   is   operationalized   by   asking   the   consumer   the   multiple ‐ choice   question   “Would   you   buy   this   product?”   on   which   the   consumer   can   answer   “Definitely   would   buy,   Probably   would   buy,….,   Definitely   would   not   buy” .
The
WPI   is   then   calculated   by   looking   at   the   percentage   of   people   that   answered   this   question   with
“Definitely   would   buy” .
The   OAR   is   operationalized   by   asking   the   consumer   the   question   “How   would   you   rate   this   product?”   which   can   be   answered   by   “ Excellent   (5),   Very   Good   (4),…,   Poor   (1)” .
The   OAR   is   then   calculated   by   looking   at   the   average   after   use   for   overall   rating.
From   these   answers,   an   average   can   be   calculated   representing   the   average   after   use   value   for   price/money.
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4.
Research   Methods
To   conclude   the   methods   used   in   this   research;   both   qualitative   and   quantitative   methods   will   be   used   to   gather   information   on   the   different   consumer   segments.
The   qualitative   methods   will   be   used   to   develop   an   interface   for   the   quantitative   test   and   to   further   probe   on   the   quantitative   observations.
Both   the   conjoint   and   Maxdiff   test   will   be   combined   in   a   quantitative   virtual   product   prototyping   test.
Both   tests   need   to   be   designed   using   the   design   of   experiment   approach   and   qualitative   data   from   the   consumer   lounge.
The   Maxdiff   test   will   be   analysed   using   the   Sawtooth   software   whereas   the   conjoint   test   will   be   analysed   using   the   JMP   software   and   the   Kano   model.
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4.
Research   Methods
‐ 36 ‐

5.
Data   Analysis
This   chapter   will   summarize   the   information   gained   from   the   different   qualitative   and   quantitative   tests   and   interviews.
First,   the   qualitative   results   of   the   interface   development   will   be   discussed.
This   is   followed   by   the   analysis   of   the   quantitative   Maxdiff   results   and   the   quantitative   conjoint   results.
The   next   chapter   will   contain   a   discussion   on   these   data   analysis.
5.1
Interface Development
Unfortunately,   there   was   no   readymade   conjoint   and   Maxdiff   interface   available.
Therefore,   the   consumer   lounge   was   used   to   select   the   ideal   benefit   vectors   and   to   develop   an   easy   interpretable   interface.
The   objective   was   to   design   an   interface   which   could   be   used   by   consumers   without   further   help   of   other   people.
5.1.1
Maxdiff Interface
For   the   Maxdiff   test,   32   benefit   statements   in   6   different   categories   were   developed   using   feedback   from   consumers   in   the   consumer   lounge.
The   categories   for   the   benefit   statements   are   cleaning,   whiteness,   skin   protection,   care,   simplification   and   freshness.
These   categories   were   only   used   for   internal   use   and   some   statements   can   belong   to   different   categories.
It   was   noticed   that,   during   the   qualitative   interviews,   illustrations   help   consumers   to   understand   the   specific   benefit   statements.
Therefore,   each   of   the   statements   was   accompanied   by   an   illustration   in   order   to   help   consumers   understand   the   benefit   statement.
Different   illustrations   were   shown   to   consumers   and   the   best   illustrations   were   selected   which   can   be   found   in   Appendix   4.
However,   the   consumer   should   focus   on   the   statement   itself   and   not   on   the   illustration   as   the   illustration   can   bias   the   consumer.
The   illustration   is   only   intended   for   clarification   of   the   statement.
Therefore,   the   benefit   statement   is   placed   in   a   large   font   while   the   illustration   is   small.
Figure   7   shows   two   examples   of   benefit   statements   with   their   illustrations.
Due   to   these   measures,   the   consumers   in   the   consumer   lounge   indicated   that   they   hardly   focus   on   the   illustrations   anymore   while   the   benefit   statements   were   clearly   understood.
To   avoid   bias   all   benefit   statement   should   be   pictured   on   the   same   screen   without   scrolling   up/down   or   sideways.
The   final   Maxdiff   interface   can   be   found   in   Appendix   5.
Figure   7:   Example   of   two   benefit   statements   with   their   illustrations   designed   for   the   Maxdiff   test   methodology.
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5.
Data   Analysis
5.1.2
Conjoint Interface
The   final   interface   design   of   the   conjoint   test   was   developed   based   on   qualitative   consumer   interviews   in   the   consumer   lounge   and   can   be   found   in   Appendix   6.
During   this   qualitative   research   several   conjoint   interfaces   were   shown   to   consumers   which   can   be   found   in   Appendix   7.
These   were   used   to   receive   feedback   from   consumers.
Based   on   this   feedback,   the   following   learnings   were   achieved:
Use   visuals   instead   of   wordings:   Consumers   indicated   that   wordings   work   too   polarizing.
When   wordings   have   to   be   translated   to   another   language,   the   new   translation   could   bias   consumers.
This   is   the   case   when   talking   about   benefit   attributes   such   as   “Care”   which   have   multiple   meanings   and   different   translations.
Scales   using   numbers   are   considered   to   be   too   mathematical   and   too   difficult   to   read.
This   results   in   a   loss   of   overview   where   consumers   cannot   compare   the   different   products   efficiently.
Instead,   use   visuals   in   order   to   avoid   misunderstanding   due   to   illiteracy
and   dyscalculia.
Visuals   using   bars   were   positively   accepted   by   the   consumers   but   to   be   found   less   catchy   than   icons.
The   use   of   smileys   was   not   accepted   as   these   were   not   distinctive   enough   making   it   too   difficult   for   consumers   to   compare.
Emphasize   pricing   level:   Consumers   tend   to   focus   only   on   their   benefit   area   of   interest   and   thereby   forgetting   to   pay   attention   to   the   price   level   of   the   prototype.
Every   consumer   has   his/her   own   preference   of   benefit   attribute   which   creates   the   tendency   to   choose   the   product   that   is   best   in   this   attribute.
In   order   to   avoid   this,   the   price   should   be   indicated   by   both   illustrations   and   wordings   and   should   be   mentioned   at   the   top   of   every   prototype.
In   addition,   the   question   itself   should   state   that   consumers   have   to   focus   on   both   price   level   and   benefit   attributes.
Use   equally   conspicuous   visuals:   The   icons   and   border   lines   used   for   the   scale   should   be   similar   in   character   and   equally   conspicuous.
When   one   of   the   icons   is   more   conspicuous   (for   example   the   black   stain   for   cleaning),   it   will   draw   the   consumer’s   attention   and   thereby   biasing   the   consumer’s   choice.
White   icons   are   to   be   avoided   as   these   icons   are   unnoticed   by   consumers.
Avoid   colour   differences   in   borders   as   this   will   draw   the   attention   of   the   consumer   towards   this   product   prototype.
Use   a   three ‐ star   instead   of   a   four/five ‐ star   rating   level:   Consumers   prefer   a   three ‐ star   rating   level   instead   of   a   four/five ‐ star   rating   level.
The   three ‐ star   rating   interface   (low   ‐  medium   ‐  high)   gives   the   consumer   enough   freedom   to   choose   between   the   different   levels,   maintains   the   overview   for   the   consumer   to   compare   the   different   prototypes   and   simplifies   the   DOX   design   for   the   conjoint   test.
Consumers   don’t   see   any   added   benefit   of   having   a   four ‐  or   five ‐ star   rating   system.
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5.
Data   Analysis
Get   consumers   in   the   right   mindset:   In   order   to   get   the   consumer   in   the   right   mindset,   an   introduction   should   be   given   where   is   explained   that   the   virtual   products   are   tested   by   a   technical   institute   on   four   benefit   levels   using   a   three ‐ star   rating   system.
The   four   benefit   attributes   should   be   explained   as   this   is   the   first   time   the   consumer   is   confronted   with   these   attributes.
After   this   introduction,   the   interface   will   be   explained   guiding   the   consumer   through   the   different   areas   of   the   interface.
This   is   followed   by   an   example   “solution”   in   which   a   fictive   person   explains   her   motivation   to   choose   one   of   the   different   prototypes.
The   consumer   may   or   may   not   agree   with   this   example   question   but   the   main   purpose   is   to   use   this   as   an   illustration   of   how   to   approach   the   question.
This   approach   allowed   consumers   in   the   consumer   lounge   to   use   the   interface   independently   without   extra   clarification   or   help.
In   order   to   avoid   bias   the   brands   will   not   be   displayed   on   screen.
5.2
Maxdiff Analysis
The   results   of   the   Maxdiff   test   can   be   summarized   in   a   top   ranking   of   the   benefit   statements.
Each   benefit   statement   has   an   Average   Score   assigned   which,   when   added   together,   will   sum   up   to   100.
The   higher   the   Average   score,   the   more   preferred   the   benefit   statement   by   consumers.
The   average   of   the   Average   Score   is   3.1
as   there   are   32   benefit   statements.
Statements   with   higher   scores   can   be   seen   as   delighters   while   statements   lower   than   this   value   are   less   preferred   by   consumers.
The   Probability   of   Choice   is   the   probability   that   the   statement   is   chosen   when   shown   with   five   other   statements   on   screen.
The   higher   the   Probability   of   Choice,   the   more   preferred   the   benefit   statement   is   by   consumers.
To   calculate   both   numbers   the   Hierarchical   Bayes   method   is   applied   to   the   data.
5.2.1
Results based on the Strategic Target
[Confidential]
5.2.2
Results based on segments within the Strategic Target
The   ranking   shown   in   Error!
Reference   source   not   found.
is   based   on   the   strategic   target.
Because   the   base   size   of   the   test   is   large   enough,   it   is   also   possible   to   do   a   breakout   based   on   the   three   segments   within   the   strategic   target.
This   breakout   is   shown   in
[Confidential]
5.2.3
Results based on Tier users within the Strategic Target
[Confidential]
To   see   whether   these   benefit   preferences   can   be   confirmed,   the   data   of   the   conjoint   test   can   be   used.
The   next   section   will   discuss   these   data   and   will   show   the   model   derived   from   these   conjoint   data.
This   model   will   help   to   explain   trade ‐ offs   made   by   consumers.
5.3
Conjoint Analysis
The   conjoint   results   can   be   analysed   in   two   ways:   using   the   Sawtooth   Software   and   using   the   JMP   software.
Because   both   methods   show   different   relevant   conclusions   both   methods   will   be   covered   in   this   section.
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5.
Data   Analysis
5.3.1
Vector Impact Analysis
Sawtooth   is   a   special   software   developed   to   design   and   to   analyse   (choice   based)   conjoint   tests.
When   the   Sawtooth   Software   is   used,   the   impact   of   the   different   benefit   vectors   on   consumer   acceptance   can   be   calculated.
This   impact   of   benefit   vectors   on   the   consumer   acceptance   is   shown   in   Error!
Reference   source   not   found.
.
[Confidential]
5.3.2
Model Based Conjoint Analysis
The   JMP   software   can   be   used   to   build   a   model   from   the   conjoint   data.
When   the   sample   is   large   enough   (i.e.
>   100   consumers),   the   software   is   able   to   predict   consumer   acceptance   according   to   different   benefit   levels   and   the   price   of   the   product.
In   chapter   5   it   was   explained   that   the   relationship   between   a   benefit   attribute   and   consumer   acceptance   might   not   be   linear   and   symmetric.
Therefore,   the   Kano   model   will   be   used   to   analyse   the   different   benefit   attributes   and   its   relationship   to   consumer   acceptance.
The   graphs   discussed   below   show   the   ideal   product   derived   by   the   method   explained   in   the   previous   chapter.
[Confidential]
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6.
Discussion
In   this   chapter   the   results   from   the   previous   chapter   will   be   discussed.
The   observations   from   both   the   Maxdiff   and   the   Conjoint   test   will   be   combined   to   investigate   whether   they   correlate   with   the   existing   knowledge   presented   in   the   first   chapter   and   whether   there   are   new   observations   which   change   the   way   of   operation.
Furthermore,   the   new   approach   of   using   the   conjoint   together   with   the   Maxdiff   will   be   discussed   including   its   application   in   future   projects.
6.1
New methodological Approach
This   research   reports   the   first   combination   of   a   Maxdiff   test   together   with   a   conjoint   test.
The   goal   was   to   combine   these   two   tests   together   in   a   virtual   prototyping   test   to   leverage   knowledge   on   the   consumer   preference   of   a   laundry   product   (i.e.
question   1).
There   were   two   objectives   with   combining   these   two   test   methodologies:   the   first   objective   was   to   develop   an   interface   which   could   be   used   independently   by   consumers   without   help.
The   second   objective   was   to   get   a   full   picture   of   the   benefit   space   and   the   trade ‐ offs   made   by   consumers   within   this   benefit   space.
The   literature   study   pointed   out   that   the   attribute ‐ based   conjoint   analysis   is   a   good   methodology   to   involve   consumers   in   the   development   of   new   products   by   providing   a   better   understanding   of   customer   trade ‐ offs   on   multiple   product   attributes   and   price.
Question   1:   What   is   the   ideal   test   interface   for   the   virtual   prototyping   test   to   leverage   the   knowledge   on   consumer   preference   and   product   trade ‐ offs?
From   the   qualitative   data   presented   in   the   previous   chapter   it   becomes   clear   that   consumers   prefer   visuals   in   the   conjoint   test   supported   by   wordings.
Wyer   et   al.
presented   a   research   on   the   role   of   visuals   in   communicating   verbal   information   (Bagozzi   2008).
He   demonstrates   that   people   spontaneously   visualize   verbal   statements   at   the   time   they   read   the   statement   and   can   relate   these   visualizations   back   to   the   phrases.
However,   he   also   demonstrated   that   consumers   triggered   by   only   visualizations   cannot   relate   these   back   to   phrases   or   specific   statements   (Wyer,   Adval   et   al.
2002).
These   observations   are   also   confirmed   by   Dahan   et   al.
(Dahan   and   Srinivasan   2000).
The   qualitative   data   from   the   previous   chapter   is   in   line   with   these   observations.
Using   visuals   in   combination   with   wordings   helps   consumers   to   get   a   better   understanding   on   the   topic.
Especially   in   the   Maxdiff   test,   it   helps   consumers   to   get   a   better   understanding   on   the   meaning   of   the   benefit   statement.
Without   an   illustration,   the   consumer   will   read   the   benefit   statement   and   make   a   visualization   themselves   which   might   be   wrong.
Furthermore,   the   qualitative   data   shows   that   the   number   of   discretization   in   the   different   benefit   areas   of   the   conjoint   test   should   not   be   too   large.
Consumers   in   the   qualitative   tests   indicate   that   3   levels   of   discretization   and   four   benefit   areas   are   enough   to   make   a   grounded   choice.
More   levels   of   discretization   confuse   consumers   and   make   them   lose   the   overview   of   the   question   and   the   choices   they   can   make.
Research   has   shown   that   the   larger   the   number   of   benefit   areas   in   a   conjoint   test,   the   larger   the   complexity   of   the   test   and   the   more   difficult   for   consumers   to   make   a   decent   choice
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6.
Discussion   among   the   prototypes   (Zhu   and   Timmermans   2010).
This   corresponds   with   the   observations   made   in   this   research.
Based   on   the   data   from   the   previous   chapter   and   feedback   from   the   agency   that   placed   the   test   in
Russia,   it   can   be   concluded   that   consumers   are   able   to   use   the   test   without   further   help.
The   foundation   of   the   conjoint   test   is   based   on   visuals   supported   by   some   wordings   provided   with   the   right   introduction   to   get   the   consumer   in   the   right   mindset.
This   is   considered   to   be   the   right   approach   to   communicate   the   different   benefit   levels   of   a   laundry   detergent.
Furthermore,   the   different   responses   among   the   different   segments   and   Tier   users   indicate   that   the   icons   are   equally   conspicuous   and   that   there   is   no   benefit   attribute   which   draws   more   attention   compared   to   others.
The   different   slopes   of   the   price   curves   indicate   that   consumers   react   on   the   price   differences   of   the   prototypes.
This   shows   that   using   both   numbers   and   icons   to   provide   the   price   of   the   prototype   is   a   good   decision.
However,   caution   should   be   taken   with   interpreting   the   price   attribute.
Research   has   shown   that   consumers   can   have   a   different   choice   behaviour   online   compared   to   their   behaviour   in   a   real   shop   (Degeratu,   Rangaswamy   et   al.
2000).
Consumers   are   often   more   price   sensitive   in   the   virtual   world   as   they   can   compare   different   prices   more   easily.
Although   the   agency   indicated   that   the   Maxdiff   test   was   no   problem   for   consumers,   there   are   still   some   doubts   on   the   Maxdiff   approach.
The   use   of   illustrations   helps   consumers   to   get   a   better   understanding   on   the   benefit   statement.
However,   the   drawback   of   using   illustration   is   that   it   can   bias   consumers.
During   this   research,   there   was   no   time   to   test   if   the   same   results   are   obtained   when   illustrations   are   absent   or   when   illustrations   and   benefit   statements   are   mixed.
It   might   be   that   some   illustrations   attracted   consumers   more   than   others   even   though   the   question   mentioned   to   only   focus   on   the   statement   itself.
According   to   the   analysis   of   the   previous   chapter,   the   second   objective   was   partly   met.
The   Maxdiff   test   gives   a   nice   overview   of   the   benefit   space   whereas   the   conjoint   interface   reveals   insights   in   the   trade ‐ off   behaviour   of   consumers.
However,   the   conjoint   interface   does   not   show   how   high   the   level   of   each   benefit   attribute   should   be.
It   gives   an   indication   of   “low ‐ medium ‐ high”   which   is   defined   by   the   researcher   itself.
The   actual   level   of   “low”,   “medium”   or   “high”   is   not   defined   in   terms   of   product   formulation.
In   order   to   reveal   what   these   levels   represent   according   to   consumers,   a   product   placement   test   can   bring   a   useful   outcome.
The   different   levels   of   “low ‐ medium ‐ high”   can   be   defined   by   placing   products   with   different   (known)   formulations.
In   this   way,   the   conjoint   model   can   be   coupled   with   the   product   placement   test   to   link   product   formulation   with   consumer   acceptance.
This   combination   makes   it   even   possible   to   predict   the   consumer   acceptance   of   future   product   formulations.
This   brings   us   at   the   discussion   on   how   to   analyse   the   results   of   the   conjoint   and   Maxdiff   data   (i.e.
question   2).
Despite   that   more   and   more   software   becomes   available   on   analysing   these   results,   there   are   few   options   mentioned   in   literature.
Question   2:   How   can   the   results   of   the   conjoint   and   maximum   difference   scaling   test   be   used   to   get   a   better   understanding   of   consumer   wishes?
‐ 42 ‐

6.
Discussion
It   was   decided   to   use   the   Design   of   Experiment   approach   to   select   the   right   samples   for   the   test   to   make   this   test   as   efficient   as   possible.
This   DOX   design   has   shown   to   be   very   helpful   as   the   sample   size   was   held   as   small   as   possible   while   the   results   are   still   statistically   significant.
Also   the   Kano   model   in   combination   with   the   JMP   software   show   to   be   very   helpful   in   analysing   the   conjoint   results.
Using   this   Kano   model   and   the   software,   the   conjoint   data   confirms   the   conclusions   drawn   by   Chen   (2012)   and   Chen   &   Chuang   (2008)   who   state   that   the   relationship   between   product   attributes   and   consumer   satisfaction   is   not   necessarily   linear   and   symmetric.
The   graphs   from   the   different   conjoint   models   often   show   non ‐ linear   and   asymmetric   behaviour.
Therefore,   the   use   of   the   Kano   model   is   a   welcome   outcome   which   helps   to   identify   the   key   product   attributes   for   each   consumer   group.
The   graphs   were   classified   by   a   manual   procedure   which   can   cause   some   bias   in   the   analysis.
This   could   be   improved   in   the   future   by   automating   the   classification   based   on   the   angle   between   the   three   points   on   the   graph.
Despite   the   manual   classification   method,   the   Kano   model   revealed   valuable   information   on   the   different   product   attributes   which   will   be   discussed   in   the   next   section.
6.2
Discussion on the Strategic Target Overall
[Confidential]
6.3
Discussion on Segments within the Strategic Target
[Confidential]
6.4
Discussion on Tier Users within the Strategic Target
[Confidential]
As   is   discussed   earlier,   the   conjoint   test   reveals   which   level   of   performance   is   needed   to   delight   or   satisfy   consumers.
However,   these   levels   are   not   specified   yet.
The   three   levels   need   to   be   specified   in   the   formulation   of   the   detergent.
A   product   placement   test   can   be   used   to   specify   the   three   performance   levels   in   the   four   benefit   attributes   and   how   these   levels   relate   back   to   the   laundry   detergent   formulation.
Unfortunately,   the   placement   of   the   test   is   not   part   of   this   research   due   to   time   limitations.
‐ 43 ‐

6.
Discussion
‐ 44 ‐

7.
Conclusion
Due   to   the   opening   of   the   Central   and   Eastern   European   borders,   many   multinational   companies   see   opportunities   for   their   products   in   these   regions.
However,   these   markets   are   not   similar   to   the   markets   they   usually   operate   in.
Therefore,   local   customization   strategies   are   required   in   order   to   expand   their   market   share.
Not   only   cultural   bounded   products   but   also   non ‐ cultural   bounded   products   such   as   laundry   detergents   suffer   from   this   principle.
Companies   producing   laundry   detergents   try   to   develop   holistic   strategies   for   these   Eastern   European   countries.
They   recognize   that   local   customization   strategies   of   marketing   are   needed   and   a   better   understanding   of   specific   consumer   preferences   is   required.
The   aim   of   this   research   was   to   find   out   how   FMCG   companies   can   perform   an   effective   market   research   to   investigate   what   excites   consumers   most   in   a   laundry   detergent   product   and   what   trade ‐ offs   are   made   by   consumers.
A   quantitative   research   method   was   developed   for   the   Russian   laundry   detergent   market.
This   research   method   used   a   combination   of   two   concepts   in   a   so ‐ called   virtual   prototyping   test;   the   maximum   difference   scaling   concept   and   the   conjoint   concept.
The   combination   of   these   two   methodologies   has   never   been   seen   before   but   can   reveal   valuable   information   on   trade ‐ offs   made   in   different   benefit   vector   areas.
This   virtual   prototyping   test   was   performed   during   a   case   study   for   the   company   Procter   &   Gamble   with   the   main   focus   on   their   laundry   detergent   brand.
Consumers   prefer   a   virtual   prototyping   test   interface   which   makes   use   of   visuals.
Illustrations   can   bias   consumers   but   the   test   data   show   that   equally   conspicuous   icons   were   used   where   none   of   the   icons   drew   particular   attention.
Too   many   discretization   levels   make   the   test   too   complicated   and   distracts   consumers.
They   can   overlook   some   of   the   product   attributes   and   thereby   make   a   non ‐ informed   choice.
However,   the   price   curves   were   not   flat   which   indicates   that   consumer   took   this   product   attribute   into   consideration   when   choosing   their   ideal   prototype.
Furthermore,   illustrations   helped   consumers   to   understand   the   benefit   statements   of   the   Maxdiff   test   resulting   in   a   clear   and   distinctive   rank ‐ ordering   of   benefit   statements.
The   conjoint   models   confirm   the   existence   of   a   non ‐ linear   and   asymmetric   relationship   between   product   attributes   and   consumer   acceptance.
The   Kano   model   was   thus   a   helpful   model   to   identify   the   different   characteristics   of   the   product   attributes   to   define   a   local   customized   product   strategy.
Concluding,   this   research   has   shown   that   a   combination   of   a   conjoint   and   a   Maxdiff   test   can   reveal   important   information   on   trade ‐ off   behaviour   between   price   and   product   benefits.
A   clear   interface   was   developed   which   enabled   consumers   to   do   the   test   without   further   help.
The   case   study   demonstrated   that   the   virtual   prototyping   test   helped   to   sharpen   the   company’s   strategy   for   their   brand   and   to   steer   product   modifications   and   formulations   in   the   right   direction.
‐ 45 ‐

7.
Conclusion
‐ 46 ‐

8.
Recommendations   and   Limitations
Due   to   time   limitations   of   this   research,   there   are   still   some   aspects   that   need   more   clarifications   and   work.
This   chapter   gives   an   overview   of   the   recommendations   for   further   improvement   of   the   virtual   product   prototyping   test   and   will   provide   recommendations   on   how   the   company   can   improve   their   laundry   detergent   brand   to   increase   market   share.
Furthermore,   the   limitations   of   this   research   will   be   discussed.
This   research   shows   the   first   virtual   prototyping   test   combining   a   conjoint   and   Maxdiff   test.
It   illustrates   that   it   is   a   powerful   combination   to   explore   what   product   attributes   most   excite   consumers   and   which   trade ‐ offs   are   made   by   consumers.
In   the   field   of   research,   this   thesis   report   contributes   on   four   areas.
First,   this   research   produced   an   interface   which   can   be   reapplied   in   different   markets   for   different   types   of   products.
In   most   cases,   the   interface   needs   small   adjustments   in   the   selection   of   benefit   areas   which   can   then   be   tested   with   a   small   test   panel.
The   interface   can   even   be   used   on   an   internet   panel   to   increase   speed ‐ to ‐ market   development.
Second,   this   research   illustrates   the   use   of   the   Kano   model   and   the   different   software   possibilities   to   analyse   the   data   from   the   virtual   prototyping   test.
The   data   from   the   virtual   prototyping   test   confirms   the   non ‐ linear   behaviour   of   consumer   benefit   preference.
And   last,   the   case   study   presented   in   this   research   shows   the   usefulness   of   a   virtual   prototyping   test   in   developing   a   localized   customization   strategy   for   FMCG   companies.
However,   this   research   was   done   for   a   company   and   there   are   therefore   some   scientific   limitations   to   the   results   presented   in   this   research.
The   main   limitation   of   this   research   is   its   focus   on   the
Russian   market   and   its   specialization   on   the   laundry   detergent   products.
The   test   interface   has   been   designed   with   the   Russian   consumer   in   mind.
Therefore,   wide   applications   for   this   interface   in   other   countries   and   markets   are   not   granted.
Furthermore,   the   results   presented   in   this   study   are   based   on   a   small   segment   of   the   total   market.
Only   consumers   within   the   strategic   target   were   recruited   which   limits   the   wide   application   of   this   test   interface.
Therefore,   the   recommendations   for   improving   the   interface   are   as   follow:

Check   the   benefit   statements   in   an   internet   panel   using   qualitative   questionnaires.
As   is   discussed   earlier,   the   benefit   statements   were   placed   in   combination   with   illustrations   which   can   cause   bias   among   consumers   and   their   preferences.
Therefore,   testing   the   benefit   statements   without   any   illustrations   can   reveal   whether   the   use   of   illustrations   resulted   in   biased   data.
Another   possibility   is   to   mix   the   benefit   statements   and   illustrations   to   see   whether   the   same   ranking   is   obtained.

The   conjoint   test   shows   which   level   of   each   benefit   attribute   should   be   present   in   the   laundry   detergent.
However,   the   test   does   not   reveal   what   each   attribute   level   represents.
Therefore,   the   virtual   prototyping   test   can   be   expanded   by   placing   a   product   placement   test.
The   virtual   prototyping   test   highlighted   which   benefit   areas   are   most   appealing   and   which   levels   should   be   maintained   in   the   product   to   satisfy   the   consumers.
However,   these
‐ 47 ‐

8.
Recommendations   and   Limitations   levels   were   characterized   as   low,   medium   and   high.
A   product   placement   test   can   link   these   three   levels   to   the   product   formulation   of   a   laundry   detergent.
In   this   way,   it   will   be   possible   to   tell   what   the   performance   of   the   detergent   should   be   in   terms   of   chemical   formulation   to   satisfy   consumers.

A   virtual   prototyping   test   is   an   ideal   test   to   be   placed   on   an   internet   panel.
The   current   interface   has   been   designed   such   that   consumers   can   answer   the   different   questions   without   further   help.
However,   the   current   test   has   not   been   placed   on   an   internet   panel   yet.
The   test   was   performed   at   a   central   location   in   Russia   where   help   was   available   when   respondents   had   questions.
Placing   the   current   virtual   prototyping   test   on   the   internet   panel   can   expose   whether   the   same   results   are   obtained.
If   the   same   results   are   obtained,   the   test   can   be   used   in   future   projects   on   the   internet   panel   which   saves   money   (no   agency   required)   and   saves   time   (results   from   internet   panel   are   often   available   within   a   week).
In   this   way,   companies   will   be   able   to   innovate   fast   and   accurate   with   the   help   of   consumers.
Besides   these   recommendations   to   improve   the   virtual   prototyping   test   design,   there   are   also   some   recommendations   related   to   the   case   study.
These   are   some   recommendations   for   the   company   to   improve   their   brand   position   in   the   market:
[Confidential]
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‐ 51 ‐

Literature
‐ 52 ‐

Appendix   2   –   Chemicals   in   Laundry   Detergents   .…………………………………………………………………………
Appendix   4   –   Overview   of   Illustrations   for   Maxdiff………………………………………………………………………
Appendix   5   –   Layout   of   Maxdiff   Interface……………………………………………………………………………………
Appendix   6   –   Layout   of   Conjoint   Interface…………………………………………………………………………………
67
68
Appendix   7   –   Overview   of   Scales   for   Conjoint   Test………………………………………………………………………   69
64
65
‐ 53 ‐

Chemical
Surfactants:
Builders:
Bleach:
Buffers:
Polymers:
Chelants:
Softener:
Enzymes:
Brightener:
Perfume:
Table   4:   Description   of   chemicals   and   their   function   that   can   be   found   in   a   laundry   detergent.
Attribute Description
Cleaning
Cleaning
Surfactants   are   the   most   important   ingredients   in   a   detergent.
They   contribute   for   almost   35%   to   the   detergents   performance   and   make   up   between   10%   and   42%   of   the   detergent   formula.
The   type   of   surfactant   determines   the   overall   cleaning   performance   of   a   laundry   detergent.
By   adjusting   the   type   of   surfactant   (positively   or   negatively   charged),   a   laundry   detergent   can   produce   more   foam,   improve   the   solubility   of   oil   and   dirt   in   water   or   reduce   detergent   deactivation   in   hard   water.
Builders   make   up   around   1 ‐ 10%   of   a   detergents   formula.
The   hardness   of   the   water   can   harm   some   ingredients   in   the   laundry   detergent.
Therefore,   the   main   function   for   a   builder   is   to   capture   free   water   hardness   ions   (Ca
2+
,   Mg
2+
,   etc.)   in   order   to   avoid   deactivation   of   the   laundry   detergent.
Whiteness
Cleaning
Care
Whiteness   &
Cleaning
Care
Whiteness
Freshness
Bleach   acts   as   a   soil   and   stain   remover   and   affects   the   whiteness   experience   of   a   detergent.
It   attacks   soil   chemically   and   thereby   breaking   it   down   into   smaller   particles   to   increase   the   solubility   of   dirt   and   stains.
Bleach   can   work   during   the   laundry   process   or   afterwards   when   activated   by   sunlight   (UV)   depending   on   the   consumer   needs   and   drying   habits   of   the   region.
Buffers   are   molecules   that   bring   some   alkalinity   to   the   solution   to   stabilize   the   ingredients   and   in   the   same   time   control   the   product   alkalinity.
Chemicals   used   for   buffers   are   NaOH,   MEA   and   Boric   acid.
Polymers   are   molecules   that   have   different   functions   within   a   detergent.
They   make   sure   that   dye   from   coloured   clothes   is   captured   and   cannot   be   transferred   to   other   clothes;   they   decrease   colour   fading   and   increase   colour   care.
Polymers   also   make   sure   that   soils   cannot   redeposit   on   other   clothes   and   it   makes   sure   that   soil   releases   much   easier   from   the   fabric   to   remove   tough   stains   (stain   removal).
These   molecules   remove   heavy   metal   ions   like   Fe
3+
,   Mn
2+
,   Al
3+
,   etc.
Many   colours   of   stains   originate   from   pigments   which   structure   is   a   stable   complex   based   on   metal   ions.
The   chelants   remove   this   metal   ion   and   thereby   weaken   the   structure   of   the   stain   and   lighten   the   colour   of   any   residual   stain.
Furthermore,   it   stops   bleach   decomposition   during   storage   of   the   detergent.
These   consist   of   positively   charged   molecules   that   bind   strongly   to   the   negatively   charged   surface   of   cotton.
The   result   is   a   soft   and   greasy   material   which   reduces   the   friction   between   fibres   and   yarns.
Fibres   are   covered   by   an   active   layer   delivering   a   lubricating   effect   on   the   fibre   surface   and   thereby   increase   the   softness   experience.
Enzymes   are   proteins   made   of   millions   of   amino   acids.
Each   enzyme   has   its   own   unique   function   in   the   detergent.
Even   a   slight   change   in   the   sequence   of   amino   acids   alters   the   shape   and   function   of   the   enzyme.
The   different   enzymes   used   in   a   detergent   are:
‐ Protease:   For   protein   containing   soils   like   blood,   grass   etc.
‐ Amylase:   For   sugar/starch   containing   soils   like   chocolate
‐ Mananase:   For   guar   gum   containing   food   like   choco   ice ‐ cream
‐ XyloGlucanase:   For   particulate   soils   like   clay
‐ Lipase:   For   fatty   esters   like   sebum   and   greasy   food
‐ Cellulase:   For   cellulose   bonds   like   fibres
These   are   molecules   enhancing   the   whiteness   of   clothes   by   absorbing   daylight   and   reflecting   white   light.
The   perfume   of   a   laundry   detergent   is   made   of   10   to   200   raw   materials   or   sub ‐ components.
The   role   of   perfume   in   a   laundry   detergent   is   to   mask   unattractive   base   odours,   provide   freshness   benefit   across   consumer   touch   point   and   reinforce   perceptions   of   other   product   benefits   like   cleaning   and   softness.
There   are   three   moments   when   consumers   experience   the   perfume   of   a   detergent:
1) On   wet   garments
2) On   dry   garments
3) In   the   box/package
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