Getting creative with data
My experience
Agencies
• Ogilvy & Mather Direct
• BMP
• Aspen Direct
• Craik Jones
• HS&P
Clients
• Land Rover
• Honda
• Saab
• Peugeot
• Diageo
• Unilever
• Sony
• Virgin Trains
• COI
• Boots The Chemist
• Silverlink Trains
• The Australian Tourist Commission
• Save The Children
Data should be used creatively
What is data planning?
Using data in your business to drive
your marketing and sales
Once called database marketing or data driven
marketing
But now has to incorporate data from within
the whole organisation and outside sources
We should call it insight
Customer data
Digital
Media
Transactional
data
Insight
Product
Market
research
Econometrics
Identify and
target
segments
Data planning
• Understanding
– Analysis, segmentation, modelling, quantitative
research
• Communication
– Audience identification, means of engagement,
messaging to drive response
• Evaluation
– Sales, media, ROI
– Results interpretation
– Learning
A mixed up world
• Specific data planning disciplines
• The newish world of digital
• Rapidly developing consumer technology
platforms
• The law
• The combination of planning skills to
create true customer insight and
understanding
Sometimes this needs more than just
data planning
What is MPP?
• A team of independent marketing planners
• Disciplines include:
– Data
– Digital
– Brand
• Specific skills:
–
–
–
–
–
Research
Econometrics
Analysis and modelling
Mobile marketing
Media planning
Informed decisions from
independent thinking
Data
Insight
Brand
Digital
Boots case study
Finding those with potential
Demystifying the model
So what is it all about
• Getting Boots to move to a more targeted
approach to customer communications.
• What we set out to achieve.
• Building the model and how it works.
• Using the model in anger.
• Results.
Boots problem
Supermarkets
Lloyds & local chemists
Department Stores
Under siege on many fronts
Dixons
Marks & Spencer
Boots needed to stand for something again
To define itself and master an area
To be focused and single minded
New focused proposition for Boots
PERSONAL
Shopping at Boots
Clarifies the benefit
is a more engaging
of visiting Boots over
and intimate
supermarkets
experience
Recognises Boots
employees
as a critical
element
delivering
this attention
and service
Clarifies the benefit of visiting Boots over
supermarkets
Health and beauty
sits equally
comfortably
under this umbrella
Where Advantage Card fits
1m
high
15m card
holders
9.5m low &
medium value, low
redeemers
10.5m active
card holders
4.5m cardholders,
never active
4.5m non
activated card
holders
19m Boots shoppers with no card
19m
potential
card
holders
Previous approach
• Information not used on
• Information used on
• Existing value to Boots
• Spend outside Boots
• Propensity to change
• 8 segments
magazine
mini mag
tactical
Cos
IE
CB
YF
MBB
LC
HAT
• Message
• Universal, treat yourself
HC
• Treat yourself not applied
in segmented manner
What was working
Biggest
Most generous
Drives sales
New holders
Treats positioning
ATV
Frequency voucher
Magazine
Events
Supplier funded DM
Kiosks
Redeemers
Simplicity
What was working
gain
VIRTUOUS
CIRCLE OF
SPEND
redeem
Areas to address
Sales effect
Kiosks
Mature holders
Highest value ring-fenced
Magazine
Frequency
Non redeemers
30% not active
Sales decline
Appeal
Generosity - 4 X more
Treats fit with brand vision
Getting started
What we set out to achieve
• Identify customer value and potential
– To protect spend in Boots.
– To steal share from competitors.
• Change their behaviour
– Identify the key behaviour triggers that influence
spend.
• Hard triggers - collect and redeem points, distance from
store and store type, make up of family etc.
• Soft triggers - attitudes to health and fitness, dieting
etc.
• Use the wealth of data in Boots to support a
range of business planning functions.
The vision
Score Ad Card customers
and put into three groups for
communications
Protect &
develop
Steal
share
Suppress
manage
The vision
Current value by category
Taken from Ad
Card database
down to
category
Demographics: Database/
External sources
Geography
Boots shopping
behaviourwhat, when, value
Value to market by category
Based on 1,400 exit poll.
Works at category level.
Need more detail to go down
to concept level - TGI overlay
Attitudes: TGI
- attitudes to health and
fitness
- attitudes to personal
appearance/care
- attitudes to environment
- attitudes to shopping/
brands
- attitudes to eating
The vision
Every customer is given a score
Value to Boots
Profit
by
product
+
Value to market
+
Propensity to
change
Scores are calculated at
concept group level
Steal
Protect
Maximum flexibility for targeting/offer to stimulate change at a micro level
The reality
Building the model
How?
• The Boots Database contains over 14
million individuals, with details of every
transaction for the last 2 years
• This amount of data cannot be modelled,
analysed or even held easily anywhere
• In order to even start looking at overlaying
a model a 5% sample had to be extracted
and sent to a specialist analytical company
What?
• A model was needed to identify who had the potential to
buy specific products.
• Past transactional behaviour is by far the best way to
ascertain this
• The analytics company took all data and monitored
trends on
–
purchasing patterns within concept groups
–
demographic profiles
–
number of purchases
–
average time between repeat purchases
–
average spend
–
etc
The Result - A Summary
•
•
•
•
•
Two models were produced
This is based on likelihood to buy plus
potential to spend
Used together they ensure best
response/sales rates
This means increase in sales for Boots
Plus they ensure the best ROI is generated
for the supplier
Affinity Model
Primary
Secondary
Tertiary
Hot
Prospects
Warm
Prospects
Coolest
Prospects
Primary
Affinity
Customers who have directly bought the promoted product in the past 12
months, ranked by frequency and total spend
Secondary
Affinity
Customers who have NOT bought promoted product but have bought
associated products in the past 12 months

Tertiary
Affinity
Customers who have a similar profile to the peer group of the product
Age
e.g 25-40
Gender
e.g female
Segment e.g Cosmopolitan
Mosaic
e.g stylish singles
Lookalikes
Share of Wallet Model
2 example customers SOW for Dove Shampoo
Customer X
Previous spend =£9.60
(High Water Mark)
-
Current Spend
= £4.80
SOW
= 50%
Customer Y
Little previous spend, so
Peer Group Average = £4.60
-
Current Spend
= £1.60
SOW
= 33%
The Model used in Value Mailings
• Offers are tailored to the individual based
on their past transactions
• Each person gets a score for every offer
available, with the top 8 products defined
as their most suitable being selected
• This allows communications to be centred
around ‘offers we know you like and some
you want to try’
Finding offers
• 20 million coupons were required for first
mailing
• Plus additional funding (to top up Boots
budgets)
• Targeted both Boots Category teams and
Suppliers
• Then cajoled, persuaded and bullied
• The result - we generated 56 different
offers for the first Value mailing
The Process of Value Mailings Step
1
•
•
All Offers, approx 60+ per value mailing are matched to the entire Boots
Database
The prime target audience is then identified, ie those who have the
highest affinity to the offers available
The Process of Value Mailings Step
2
•
•
•
The selected universe is then given a score for every offer available, based
on the likelihood of them purchasing
This produces millions of stats, which then need to be optimized and
ranked
To produce the top 8 offers for EVERY PERSON
The Personalisation of Value
Mailings
• The essence of Value is that each person gets
offers applicable to them
• To coincide with this, the rest of the mailing was
personalised to produce a mailing tailored to each
individual
• The personalised information includes:
–
–
–
–
–
–
Number of points available
Number of redeemed points in the last 12 months
Favourite store to shop in
Total Value of coupons
Total points available on promotion
Redemption ideas based on number of points