The document discusses methods for understanding customer behavior and preferences, emphasizing the importance of both static and dynamic data for personalized marketing strategies. It compares traditional retail approaches to innovative ones that utilize more extensive data analysis and context-aware targeting techniques. Privacy concerns and customer consent are crucial considerations in balancing data collection with customer service.

1. On Customer Insight
Richard Veryard
October 2013

2. Contents
Our Goal
• Building Knowledge about Customers
• To Support Reasoning about Customers
Sources of Knowledge
• Customer Tracking
• Customer Profiling
• Exploration and Experiment
Supporting Reasoning
• Managing Uncertainty
• Managing Privacy
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3. Our Goal: Building Knowledge about Customers to
Support Reasoning about Customers
Knowledge about
Customers in General
– Sales History
– Behaviour Patterns and
Trends
– Demographic Categories
Knowledge about
Specific Customers
– Static Classification
– Dynamic Classification &
Context
– Behaviour History
– Associations
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Top-Down Reasoning
– Customers with large gardens purchase a new lawnmower
every three years.
– John bought a new lawnmower two years ago.
– Therefore we know he has a garden.
– And we predict he will buy another lawnmower soon.
Bottom-Up Reasoning
– People who bought X also bought Y.
– This is a statistical correlation, which may have no obvious
explanation or meaning.
– But we can exploit this pattern to sell more Y.
Sideways Reasoning
– People often buy beer and nappies together.
– We think this is because parents with small children tend to
drink at home.
– So perhaps we can also sell them a baby listening kit.

4. The emergence of bottom-up reasoning
At one point, Amazon actually had an editorial team
that would personally craft book recommendations to
be featured on the home page.
But as Amazon became more proficient at exploiting
the data it was gathering about user preferences and
behavior, the added value provided by real living
breathing humans came under existential threat from
the automated ―personalization‖ ―P13N‖ team.
The fight was brutal, but the end was preordained.
The algorithmic power of automatically personalized
recommendations boosted sales more quickly than
the plodding touch of corporeal bodies.
Source: Brad Stone, ―The Everything Store: Jeff Bezos and the
Age of Amazon‖ (2013)
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5. Retail Becoming Smarter
Traditional Retail
Innovative Retail
Segment customers into fixed
demographic categories
Respond dynamically to
customer’s current context and
concerns.
Focus on top-down reasoning
Combine all forms of reasoning.
Based on limited amounts of
data.
Based on much larger quantities
of data.
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6. Examples
Fixed Segment
Context
Emma
•
•
•
•
Married woman.
Part-time job.
Age 30-35.
2 children.
Last week, Emma’s husband
announced on Facebook that he was
changing jobs.
Laura
•
•
•
•
Single woman
Full-time job.
Age 25-30
No children
Laura has recently stopped buying
alcohol, and has switched to
fragrance-free toiletries.
Insight
How does this information help
you to provide a personalized
service?
Fixed data is relatively easy to
collect and maintain, but provides
little competitive advantage.
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Are there any clues here that might
help you provide a better service?
Contextual data potentially has far
greater value, but only if you can
interpret and respond promptly.

7. In-Store Customer Tracking
• Existing technologies will become
cheaper, and new technologies will
emerge …
• … enabling a huge increase in the
volume and granularity of the
available data.
• Customer device (smartphone)
• Customer given hand-held scanning
device on entering store
• Electronic tags on all goods (RFID)
• Cameras and face-recognition
• And some other technologies we
can’t talk about yet
• A retail store can collect a much
higher volume of data about the
customer's behaviour …
• … not merely the items that the
customer takes to the check-out but
also the items that the customer
views but doesn’t buy.
• (Subject to privacy concerns.)
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8. Network Tracking
• All aspects of digital activity may be
captured and interpreted.
• Comments and complaints on
Facebook, Twitter, etc.
• Photos of customer wearing your
clothes, using your products.
• Customer-supplied content and
creations (recipes, outfits, room
designs, etc)
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• New ways of identifying customers
• New combinations and mashups of
material
• New ways of tracking friendship
networks and influence networks.
• New ways of tracking internet ―buzz‖.

9. Who is your customer?
Individual as Customer
Family as Customer
People buy clothes and household
goods for themselves
Many items are purchased for use and
consumption by a family.
• based on their own individual
characteristics and preferences
People are defined, and their behavior
determined
• without reference to other family
members
• not just by their individual
characteristics
• but also by their positions, roles, and
relationships in the context of their
families.
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10. Customer Profile Elements
Individual
Permanent
(slow-moving)
Name and address
Demographic category
Socioeconomic status
Friends and family
History
(recent dominates
older)
Purchase history
Browsing history
Response to retail
communications (e.g.
promotions and offers)
Social network trace
Purchased gifts.
Social network ―likes‖ and
other interactions.
Current
(fast-moving)
Present location (e.g.
geolocation)
Before/after lunch
E.g. now browsing the store
with two friends.
Planned
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Network Associations
Scheduled trip to China next
week.
Wedding next year.
Graduation party

11. Understanding what the customer is doing
Clayton Christensen
• The customer is the wrong unit of
analysis for innovators to focus on.
• Instead, focus on the job that
customers are trying to get done
when they use your product or
service.
• Source: Clayton Christensen et al, Finding the
Right Job For Your Product. Sloan Mgt Review
2007
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His Example
• A fast-food company discovered that
a significant portion of its customers were
―hiring‖ its milkshakes for an unexpected
use: as a food to consume early in the
morning, while driving on a long, boring
morning commute.
• By focusing not on the customer for the
product but, more specifically, on what the
customer was trying to do – consume a
filling food on a boring daily drive – the
fast-food company could customize the
product for its early-morning milkshake
buyers in ways to make it more effective
in that function. It also gave the company a
greater understanding of its competition —
which, in the case of the morning
milkshake, ranged from bananas to
doughnuts.

12. Divided Loyalties
Clever Retail Tactics
Customer Counter-Tactics
• There is no point offering generous
• Many loyalty cards for different
deals to your regular customers. Data
retailers. If you shop elsewhere you
shows that it is more important to
could get more deals sent to you to
target offers at disloyal customers –
lure you back.
those who flit from store to store.
• Source: Joe LaCugna, Starbucks director of
business intelligence and analytics, 2013.
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• And some customers even get
several loyalty cards for a single
retailer. The aim is to trick retailers
into giving you better deals by making
them think you're shopping much less
frequently.

13. Don’t show off your knowledge
Use the “Dog Whistle” approach
WRONG
BETTER
• “Based on your recent purchasing • “Here is an apparently random set
history, we reckon you might be
of coupons, which just happens to
pregnant, so here are some
include some baby stuff.”
coupons.”
But what if you are wrong?
And what if she hasn’t told
her husband yet?
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Only those customers interested
in childbirth and babies will pay
attention to these.

14. Never forget that a profile is only an approximation
WRONG
BETTER
• We know which customers will be
attracted to this offer.
• We have a rough idea which
customers will be attracted to this
offer.
• We optimize the offer by excluding • We optimize our learning by
sending it to a selection of other
all other customers.
customers as well.
But what if you are wrong?
You will never find out.
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15. Treat every promotion as a scientific experiment
learning opportunity
compare
compare
Customers
within target
segment who
receive offer
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Customers
outside target
segment who
receive offer
Customers
within target
segment who
don’t receive
offer
Customers
outside target
segment who
don’t receive
offer

16. Managing Uncertainty
• John has a garden behind his house.
Three quarters of the garden is plain
green, presumably grass.
• There is an object in the garden visible
from a satellite picture. It might be a
rusty lawnmower. It has been there all
winter.
• It is nearly Spring. John may need a
new lawnmower soon.
• A garden equipment company sends
John an attractive brochure of garden
equipment, including a range of
lawnmowers.
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Perhaps it is artificial grass
Perhaps it is a bicycle. Or a sculpture
Perhaps John hires someone else to
look after his garden.
Perhaps we should verify John’s profile
before we send him anything?
But it doesn’t matter how accurate
John’s profile is, as long as there’s a
good chance he might buy something.

17. The “Two-Second” Advantage
• Can the retailer respond to tracking
data before the customer leaves the
store?
• For example, a food retailer might infer
from the customer's browsing behaviour
that she is looking for her favourite
brand of pasta sauce. The shelf is
empty, but there's a new box just being
unloaded from a truck at the back of the
store.
• Find a way of getting a jar to the
customer before she reaches the
checkout, and there's your ―two-second‖
advantage.
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18. Privacy versus Customer Service
DO
Customers allow you to collect and
use their personal data
So what do they get in return?
•
•
•
•
Loyalty rewards
Targeted marketing
Special offers
Special events
If you are generous with your loyal
customers, a larger proportion of
customers will be willing to
participate.
DON’T
Take your loyal customers for
granted, or make them feel
exploited.
The legal small print may allow you to insert customer’s photos into
your advertisements. But if the customers don’t like it, it’s a bad idea.
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19. Recommendations
DONT
• Don’t be intrusive.
• Understand the value of the data.
• Don’t force your customers to
provide meaningless information.
• Build the capability to interpret
and use customer intelligence.
• Don’t let your business partners
manage your customer data.
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DO
• Keep asking new questions.

20. Where Next?
Customer
Experience
Management
Turning Customers
into Fans
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Social Network
Engagement
Turning Fans into
Customers

21. Contacts
www.replyltd.co.uk.
r.veryard@replyltd.co.uk
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