New Product Management
• COMPONENTS OF DESIGN
– Specify:
• "WHO"
• "WHAT"
• "WHY"
1. Target Consumers
2. Core Benefit Proposition (CBP)
3. New Product Position
vs.
Competition Product Position
4. Physical Characteristics of Product ---->CBP
• "FEATURES"
• "MARKETING
MIX"
5. Initial price, Advertising & Distribution survey
THE DESIGN PROCESS
Opportunity
Identification
Consumer Measurement
1. Qualitative
2. Quantitative
Refinement
* MKTG.
*R&D
* ENGR.
* PRODCTN.
Evaluation
Go / On / No Go
GO
T
e
s
t
i
n
g
MODELS OF CONSUMERS
* Perception
* Preference
SURVEYS
FOCUS
GROUPS
* Segmentation
No Go
D
r
o
p
* Choice
IN-DEPTH
INTERVIEWS
PREDICT MARKET BEHAVIOR
BEST CBP ====> 4 P's
THE DESIGN PROCESS
SURVEYS
FOCUS GROUPS
IN-DEPTH
• To identify important attributes & predict how a new product concept will be
perceived.
• Identify ideal vector & predict how new product will be compared to existing
products.
• Determine best strategy to serve target customers.
• Determine what to control to ensure purchase & predict probability that consumer
chooses product.
– Aggregate individuals perceptions, etc. yielded by above models.
– Measure awareness & availability.
PRODUCT PERCEPTUAL MAPPING &
POSITIONING
Perceptual Mapping & Positioning: Measurement techniques to reveal how consumers
mentally compare products or brands.
1. Managerial Requirements
a. Abstract & label underlying dimensions.
b. Position existing products on these dimensions.
c. Identify consumer preferences on these dimensions.
d. Identify new product opportunities on perceptual map.
e. Determine the physical features which correspond to the perceptual position.
f. Design (or modify) own product to fit in the BEST position.
PRODUCT PERCEPTUAL MAPPING &
POSITIONING
Perceptual Mapping & Positioning:
Methods: Producing positioning graphs
– * Perceptual mapping techniques
– * Joint space techniques:
adding preference vectors or ideal points
– Perceptual Space - Multidimensional
• Axes - General Properties/ Benefits of Brands
• Brand Similarity - Inversely Proportional to Distance between Brands.
DESIGN PROCESS
Joint Space Analysis (JSA)
A. Perceptual Space Construction Issues
1. Methods: Composition vs. Decomposition
2. Choosing Brands & Attributes (Evoked set?)
3. Alternative method: Discriminant Analysis
4. Alternative method: Correspondence Analysis
B. Joint Space Construction Issues
Adding Preferences: Two Methods
1. Ideal Points
2. Preference Vectors
DESIGN PROCESS
Joint Space Analysis (JSA)
C. Operational Techniques
1. Package Programs (SAS, SPSS, etc.)
2. Specialized Packages (e.g., Adaptive Perceptual
Mapping; Marketing Engineering)
D. Interpretation
1. Perceptual Space
2. Joint Spaces
3. Benefit segments
CONSTRUCTING PERCEPTUAL SPACES (In General)
Decomposition
- Similarity Scaling
Composition
- Rate Brands on Attributes
( 5 to 10 pt. scale)
– e.g., MDS
- Space Reduction:
Factor Analysis
Discriminant Analysis
A * useful for exploratory work
speedier==> Option:
Contingency Data -A * attributes do not need
to be defined
D * Respondent must be familiar with
large number brands
Dichotomous scale -* Possess attribute or not.
Use correspondence analysis.
D * Interpretation of axes difficult
-- manager judgment
PERCEPTUAL MAPPING TECHNIQUES
MDS
(Decomposition)
FACTOR ANALYSIS
(Composition)
A. Data Objective:
1. Similarities
2. Recovering ranking
1. Attribute rating
2. Weighted summary of ratings
B. Input level:
Individual ====> Aggregated
1. Individual ====> Aggregated
PERCEPTUAL MAPPING TECHNIQUES
MDS
(Decomposition)
C. Advantages:
1. Attribute set not required
2. Indirect measure
===> More honest answer
3. Perceived "Product" Similarity
FACTOR ANALYSIS
(Composition)
1. Easier to name dimensions - use attribute factor loadings.
2. Statistical analysis readily available.
D. Disadvantages:
1. Requires special program
to create similarity matrix
2. Can't use for less than 8 "brands"
3. Comparing N(N-1)/2 pairs is
exhausting.
4. Respondent must be familiar with
large number brands
1. Require complete set of product
attributes.
2. May ignore important attributes.
3. Halo-effect
PERCEPTUAL MAPPING TECHNIQUES
Choosing Brands & Attributes:
Sources: 1. Managers
2. Consumers
Salient?
Evoked set
Consideration set
Brands
(Recall)
- Number ?
- Which ?
-- w/in 1 product class ??
Attributes
(importance
in decision)
- Cognitive
- Affective
Use Principle Components Analysis to reduce set size
( not to collapse into factors -- see which are related).
Benefits
vs.
Features
COMPETITIVE SET CONSTRUCTION
Joint Space Construction
-- adding some measure of preference
(ideal points or preference vectors)
to perceptual map
Determine:
* Most preferred attribute/ benefit combination.
* Segments based on ideal points/ preference vectors.
(Benefit segmentation)
Preference Regression
-- Application --
K
W
Pij 
k
k 1
 X ijk
where: p = preference rating
w = est. importance weight
x = individual's perception of producer
i = individual or person
j = product or brand (eg. Toyota)
k = dimension (eg. performance)
Xijk obtained from:
1 MDS - similarity dimension
or
2 factor scores
pij obtained from transformed preference rankings = J - rij
where:
J = number of brands
rij = rank preference
wk is obtained from regression analysis
Preference Regression
Compare:
product / brand
forecast preference
to
actual preference
Should = 40 to 80% accuracy
Market Share: % who prefer each brand j.
COMPETITIVE SET CONSTRUCTION
Joint Space Interpretation
Brand Projection:
* perpendicular link : brand onto preference vector
* Longitudinal:
During Product Development
During Product Life Cycle
* Map segments onto Geodemographic/socioeconomic profiles.
* Improving Brand Performance
* What combo of marketing - mix for each?
1. - change brand perceptions
2. - change ideal points
3. - change attribute importance weights
4. - add new attributes to achieve (3)
PERCEPTION
PREFERENCE
FEATURES
Preference
Regression
Self-report
Importance
Conjoint
Analysis
Logit
Analysis
Attribute
importance
Preference
Ratings
Choice
Weight relative
importance
Reveal
Trade-off
Explain
Choice
Regression
MonAnova
Maximum
Likelihood
Input level:
Individual/
aggregated
Individual
Individual
Aggreg.
Preference
Regression
Self-report
Importance
Conjoint
Analysis
Logit
Analysis
Advantages:
Conceptually
simple
Easy
Direct
Use hypothetical
profiles
.: can predict
future
opportunities
Predict
Market
Share
Force ranking
preference
.:can
distinguish
order of
importance
Explain
many
Attributes
Data:
Preference ratings
Object:
Explain ratings
Method:
Regression
Easy to analyze
Preference
Regression
More
Accurate
Self-report
Importance
Conjoint
Analysis
Logit
Analysis
Subjective
.: unstable
Difficult
to use
if too
many
attributes
Complex
Disadvantages:
Average weights
may mislead
.: not good for
Heterogeneous
Populations
ADAPTIVE PERCEPTUAL MAPPING SAWTOOTH SOFTWARE
Hybrid Approach
Individual Data
Method
Discriminant
Prin Components
Brand Familiar
Attribute Importance
Preference f (Indiv.
Perception)
f ( Avg. Perception)
Preference
Ideal Point
Self-Rated
Estimated
Pref. Vector
Aggregate Data
X
X
X
X
X
X
X
X
X
X