An overview of The Data People

2. All Customers Are Not Equal
‘‘ 80% of sales or
profit will come
from 20% of
’’
customers

3. What We Do
We identify the customers that
matter from your data and
develop strategies that take
advantage of this knowledge

4. Who Are Your Best Customers?
• The 80:20 Pareto Effect is alive and well and should be the
driving force behind your marketing and business strategies
• So who are your best customers?
– Where they are
– What they do
– When they do it
– How often do they do it
– How to find more of them
– How best to talk to them
– How to keep them
– How to cross-sell and up-sell to them
– How to identify those that are unprofitable

5. We Build Detailed Pictures of Your
Best Customers
Profitability Attitudes
Lifestyle Demographics
Usage Needs
A Marriage of all the Elements

6. We Unleash The Power Of Data
Personal
Personal
Personal
Database
General General General
Your Best
Customers
Customer Knowledge

7. Your Best Customers Online
• Do you understand how your best customers behave online
• Why should your site be all things to all people? Make it the
most effective for those who matter most to the business.
– If your most profitable customers jump around the site,
design should enable that behavior
– If those top-tier customers remain in one category, don't
clutter their experience with links they'll never use
• Do you have incentives that appeal to your best customers?
• Combining profitable customer data with online behaviour
data is the future

8. Who We Are
• A data planning consultancy
• Set up in 2009
• Based in Leeds and Bristol
• 5 employees with a network of associate consultants
• Working in the private and public sectors
• Part of the Journey Group

9. Data Planning
data

10. Data Interpretation
• What Is Your Data Telling You?
• We will audit your current data and create interpretation
from it
– this often starts with the basics of quality and quantity
– turning your data into timely, relevant and meaningful
information
– turning that information into marketing advantage
– Helping you ‘see the wood for the trees’
data

11. Data Analytics
• What could your data be telling you?
• We will undertake analysis on your data to build a fuller
picture. For example:
– models can be developed that predict which of your customers are
most likely to churn or which are most likely to buy a specific product
– segmentation models can be developed that group your customers
into specific clusters to help refine your contact strategy
– basket analysis models can be developed that analyse the
combinations of products customers have purchased and help
predict their next purchase

12. Data Strategy
• What will your data allow you to do?
• We develop data led business and marketing strategies to
maximise business growth
– CRM, Acquisition & Retention Strategies
– Cross-sell & Up-sell Strategies
– Data Collection & Data Partnerships Strategies
– Creative Testing & Message Hierarchies

13. What We Manage
• Through a network of third party partners we will source
and manage
– Large Data Analysis & Segmentation projects
– Data Enhancement
– Data Cleaning
– Database Design & Build
– List Purchase
– Data Collection
– Processing Data
– Web Analytics

14. Our People
Peter Rivett-Jones David Emslie Steve Raper
Director Director Director

15. Peter Rivett-Jones - Director
• 20 years of data and marketing
experience
• Senior client services and planning
positions in top DM agencies
including Joshua, GGT Direct & EWA
• Founded DM agency Made With Love
(MWL) in 1999 which was later sold
to Chemistry in 2003
• Joined the board at Poulters in 2005
heading up all data and direct
marketing accounts
• Co-founded The Data People in 2009

16. David Emslie - Director
• 25 years of data and marketing
experience
• Senior management positions in top
agencies including Poulters & JDA
• Head of Marketing for Strachan
Bedrooms
• Joined Equifax in 1998 where he
spent 10 years in a variety of senior
Sales and Business Development
roles in Marketing Services and
Consumer Risk
• Co-founded The Data People in 2009

17. Steve Raper - Director
• A statistician with 25 years of data
analysis and marketing experience
• Started career with British Gas in
various sales and marketing
positions
• Went agency side in 1994 as Data
Manager for Bedrock
Communications
• independent consultant since 1996
providing data strategy & data
analysis for agencies and clients
• Co-founded The Data People in 2009

18. What Makes Us Different?
• We are marketeers first and data planners second
• We turn numbers into words and pictures.
• We answer the quot;so what?quot; of data and statistics
• We have vast experience in data and all its touch points
• We are independent consultants with nothing to sell apart
from our time
• We turn the complexity of data into strategies that make
sense
• We champion simplicity

19. Sector Experience
• •
NHS & Health Retail
• •
FMCG Leisure
• •
Automotive Office Equipment
• •
Industrial Telecoms
• •
B2B Financial Services
• •
Travel & Tourism Mail Order
• •
Airlines Utilities
• •
Government Drinks

20. Case Study 1
Alliance & Leicester

21. The Brief
• Alliance & Leicester had been using cold contact lists to
direct potential customers to their web site, with limited
success
• Registered users of the site were segmented by answers to
basic financial questions only upon registration
• Communications to registered users had minimal tailoring
• With results from nearly 2 years’ activity now available, our
brief was to optimise results –
– Increase visits to the site from dm activity
– Maximise the potential value of visitors to the site

22. The Solution
• The first step was to take the client’s database of registered
users, plus a sample file of non-respondents, and append
lifestyle and demographic overlays to the data
• CHAID modelling based on each set of overlays was carried
out and gains charts compared to improve targeting
• The client’s registered user base was segmented in terms of
their long-term behaviour in relation to the site
• The resulting 6 clusters were profiled in terms of their likely
financial requirements and long-term value potential
• The rules for optimum allocation to segments were
modelled using discriminant analysis

23. The Solution
• A series of new questions at registration were identified to
give the client data to allocate the new user immediately to
the appropriate segment

24. The Results
• There was an immediate increase of over 100% in site visits
generated from direct mail through the improved targeting
• Value models within the segmentation allowed the client to
estimate long-term potential value
• Thus determining the products advertised and marketing
investment for each segment
• In addition, extra information about customers’ potential
value are being added to the model as experience gives us
more accurate information about the web-site’s longer term
usage patterns and sales values

25. Case Study 2
Holmes Place

26. The Brief
• Like many of its competitors, Holmes Place concentrated on
acquisition during the unprecedented growth phase of the
industry
• Customer retention and improved targeting for acquisition
were recognised as important business drivers as:
– competition increased
– cost of acquisition increased
– attrition rates exceeded 50% per annum
• Little was known about the customer, and no estimates of
customer value and what drives it had been evaluated
• The brief was to understand the customer better to allow for
smarter and more efficient marketing activity

27. The Solution
• The first step was to take the client’s membership and
transaction databases and combine them
• Append demographic and lifestyle information
• Identify valuable customers – including length of
membership and additional spend (e.g. personal training)
• Profiles for each club by value band were compiled
• In addition, value groupings by type of membership and by
number of visits to clubs were made

28. The Solution
• Key variables – transactional and lifestyle - for predicting
closure of membership were identified
• The resulting model was applied to the customer base to
predict the likelihood of attrition
• Although there are many factors affecting renewal of
membership (such as moving away from the area), many
members do not renew because of their lack of usage of the
facilities available
• The models allowed us to identify the probability of each
member renewing, and allows communication strategies to
be put into practice for valuable but potentially disloyal
customers

29. The Results
• Targeting for new customers has been revitalised
• After years of reducing returns from marketing targeted by
demographics only, the new models coupled with data
cleaning processes have resulted in a five-fold increase in
response rates
• Costs per new member have been reduced
• Average value of each new member acquired was increased
• Early indications are that the modelling of likely defectors,
coupled with communications designed to retain them, is
starting to reduce churn rates

30. Case Study 3
Nescafe

31. The Brief
• A major development in the Nescafe UP’s brand strategy was
to narrow the target audience that all marketing
communications were aimed at.
• Extensive work by the brand team had re-defined the
audience that Nescafe UP would target.
• Two target audiences called Roast & Ground Dippers and
Instant Dippers had been identified – c1.7m HH’s
• The brief was how, from a data perspective, do we find this
audience to allow a major dm sampling campaign to take
place

32. The Solution
• Nescafe did not have marketing data of their own
• There was not sufficient volumes of external data to
purchase that identified ‘dipping’
• In order to get the quantity and quality of data needed we
proposed data modelling
• In simple terms, this meant creating a profile of the people
we wanted and then finding lookalikes.
• The secret lay in having the most accurate profile at the start
• We recommended using Tesco Clubcard data to create the
profile that the data model would be built around
• The model would then be applied to external lifestyle data
sources

33. The Model
TGI DATA
VALUE DATA
£
Build Matched to
Data Claritas
Model database
Audience
Characteristics

34. The Results
• The data model used in the direct marketing campaign
proved to be highly successful
• The mailing delivered £280k uplift in the first three months
alone
• The mailing had an impact on customers behaviour resulting
in sustained change over a year – once customers had tried
it they remained loyal
• Customers moved from the targeted product areas of Freeze
Dried and R&G proving the model’s accuracy
• At a brand level customers were most likely to have moved
from Kenco Ultra Premium and other Premium freeze dried
coffees

35. The Data People use customer
data to deliver greater profits
and more effective outcomes

36. Thank You