Predictive marketing is a powerful tool to improve sales and competitive advantage. Learn how you can harvest the potential of your big data and take a big step forward in securing your long-term success.

1. POWER TOOL
for Building Sales
by Doug Edwards
Predictive Marketing:
Retail’s
Big “New”

2. COPYRIGHT © INTEMA SOLUTIONS 2013 2
Looking for growth, many retail profession-
als still rely on broad segmentation strate-
gies as the foundation of their merchandis-
ing and marketing efforts. These retailers
ignore or shy away from the efficiencies
and business-building advantages of pre-
dictive analytics and strategic customer
segmentation. Today’s predictive tech-
nology is poised to tell us what customers
are likely to buy, do, or want next. Using
cost-effective solutions that allow retailers
to road-test predictive marketing tools,
retail professionals can see for themselves
how being truly relevant in their customers’
eyes is essential to success.
PREDICTIVE MARKETING

3. COPYRIGHT © INTEMA SOLUTIONS 2013 3
“15 out of 17 sectors in the
Unites States have more
data stored per compa-
ny than the US Library of
Congress.”
Retailers looking to carve out a bigger slice of the sales
pie, without adding stores or expanding internation-
ally, may settle for using their old, wasteful merchan-
dising and marketing tools. But in today’s competitive
environment, settling is risky.
Surveys show that some businesses looking for an
advantage are turning to the vast amounts of data
they’ve collected about their customers’ transactions
and experiences. Employing the relatively new tools
of big data, these retailers are gaining significant sales
and operational efficiencies.
Out with the garden hose, in with the tsunami
Retailers are certainly not neophytes when it comes to
using their customer data to build sales. Point-of-sale
(POS) data have been collected and pored over since
Walmart popularized the concept, and Web analytics
are almost equally familiar territory. But as everybody
knows, the colossal, ever-growing wave of social me-
dia and smartphone usage has led to a stunning in-
crease in the volume and velocity of the data accumu-
lating about customer behavior. In fact, according to a
2011 report by McKinsey,

4. COPYRIGHT © INTEMA SOLUTIONS 2013 4
Holding on to the old toolbox
In spite of this abundance of customer data, many re-
tailers still base their merchandising and marketing
decisions on broad demographic segmentations. Re-
tailers who may be considering micro-segmentation
imagine it to be so complex that they would get lost
marketing to and managing multitudinous customer
segments. When these retailers consider soliciting big
data expertise or building their own analytics depart-
ment, they shy away from the perceived expense and
risk. As a result, many retailers hold back on justifying
the resources to exploit their big data.
Hanging onto the status quo, though, is risky for two
reasons. First, using broad segmentations wastes a
huge amount of time, energy, and resources in market-
ing and merchandising by leading retailers to:
• Create merchandise assortments based on arbi-
trary store groupings rather than neighborhood
needs.
Retailers create merchandise assortments by store
groupings (i.e., province X, region Y) that have little
relation to customer needs. If a store’s inventory
responded to the specific needs of its neighborhood,
would it not find more customers likely to purchase
its merchandise?
4 out of 10 subscribers
press the spam button
because of irrelevant content
*Marketingsherpa wisdon report

5. COPYRIGHT © INTEMA SOLUTIONS 2013 5
• Launch nation-wide promotions based on what
was bought in the past.
Different regions not only have different needs for
merchandise, but their inhabitants have different lev-
els of discretionary income. Why not tailor the offer
and pricing to regional or neighborhood economics?
• Distribute mass offers that engage a low percent-
age of customers.
Over 50% of companies continue to “blast” emails
with the same message or promotion to their entire
customer list. It’s been proven that as the relevancy
of the message increases so does the reader’s en-
gagement with the message.
“Over 50% of companies
continue to “blast” emails with
the same message or promotion
to their entire customer list.”

6. COPYRIGHT © INTEMA SOLUTIONS 2013 6
WalMart influences the future
The second major risk of sticking with broad seg-
mentations is that your competition may eat, or may
be eating, your lunch. Forward-thinking retailers are
already investing in the big data techniques of pre-
dictive analytics to discover what products or ser-
vices individual customers are likely to buy.
In June 2013, @WalmartLabs, the technology arm of
the world’s biggest retailer, announced that it had
acquired Inkiru, a start-up whose “predictive analyt-
ics platform will enable us to further accelerate the
big data capabilities that @WalmartLabs has pro-
pelled forward at scale…including site personaliza-
tion, search, fraud prevention and marketing.”
With Walmart investing its tremendous resources
in predictive analytics, what choice does that leave
other retailers?
In June 2013, Walmart
annouced the aquisition of
the predictive intelligence
start-up Inkiru.

7. COPYRIGHT © INTEMA SOLUTIONS 2013 7
What is predictive analytics?
Predictive analytics goes beyond the traditional
tools of business intelligence (BI) like simple queries
or rules. Using algorithms based on advanced statis-
tics, data mining, and machine-learning, predictive
analytics looks for deep patterns in your data that
traditional BI cannot reveal. This technology assigns
a predictive score to each customer, or whatever
element you’re interested in, based on a predictive
model that has been trained on your data. What
makes the technology predictive is its capacity to
learn and adapt from experience. This capacity to
learn and adapt to what it observes separates it
from other BI and analytics techniques.
What makes the technology
predictive is its capacity to
learn and adapt from
experience.

8. COPYRIGHT © INTEMA SOLUTIONS 2013 8
Benefits of predictive analytics
68%
Achieve competitive advantage
55%
New revenue opportunities
52%
Increased profitability
45%
Increased customer service
44%
Operational efficiencies
How retailers can benefit from predictive
analytics
As the predictive engine learns and gets fine-tuned
by new interactions, your marketing and merchan-
dising is enhanced by a continuously improved re-
sponsiveness to customer needs. As a result, you:
• Improve your margins
When you talk to customers about the things
they’ve shown interest in, they’ll buy more at the
regular price, thus your margins will improve.
• Accelerate inventory turnover
When you’re more attentive to your customers’
needs, you buy better, so you’ll have more rel-
evant stock on the floor, which builds sales and
loyalty.
• Increase market share
When you’re engaging customers better with
more relevant content, your marketing messages
are more powerful and more likely to attract cus-
tomers going elsewhere.
*Ventura research Predictive analytics benchmark

9. COPYRIGHT © INTEMA SOLUTIONS 2013 9
A simple approach to scoring
Predictive marketing begins with predictive scoring.
This use of “scoring” is not to be confused with the
same term used in lead nurturing, where points are
attributed subjectively to various prospect behav-
iors. In predictive marketing, the scoring of the ele-
ment you’re interested in is attributed by advanced
analyses of statistics and probabilities, data-mining,
and machine-learning.
Scoring behaviors and/or actions to predict the fu-
ture is not new as a tactic to “beat the competition.”
Professional gamblers who count cards in black jack
do it all the time. These players attribute a numerical
value to the cards they’re seeing and, by keeping a
running total, then predict whether the next hand is
going to be to their advantage or not.
Unlike the win-lose scenarios of black jack, a scoring
approach in retail can be a win-win situation for both
the retailer and consumer. Let’s look at a simple
example. Each SKU a grocery store sells belongs
to one of five categories: produce, meats and dairy,
frozen foods, basics (milk, eggs, bread), and other
packaged goods. Let’s assume for this example that
each category has the same number of SKUs. Over
a four-week period, we capture customer transac-
tional data.
“Scoring behaviors and/or
actions to predict the future
is not new as a tactic to ‘beat
the competition’.”

10. COPYRIGHT © INTEMA SOLUTIONS 2013 10
In our simple scoring approach, we give a score of
+1 to the respective category after a SKU is added to
the cart. After four weeks, the median score for pro-
duce is +100. Customers at +150 in produce are sent
an e-mail in which organically grown tomatoes are
on special. Aren’t these customers more likely to be
engaged with the message? They are indeed!
This simple scoring approach can also clarify which
messages not to send or not to make prominent
in the customer e-mail. For example, if the median
score for frozen foods is +60, it wouldn’t be worth-
while to send customers that scored +20 or less a
promotional offer for ice cream. Thus, your e-flyer
message to these customers should either remove
ice cream altogether or “demote” the product to
page two or three.
In this simple scoring approach, it’s easy to see how
rewarding the customer with products and prices in
categories they favor would make the e-flyer more
relevant and engaging, and thus make customers
more likely to purchase. This same scoring approach
may be used on any measureable behavior, not just
purchases.
“…a scoring approach in
retail can be a win-win
situation for both the
retailer and consumer.”

11. COPYRIGHT © INTEMA SOLUTIONS 2013 11
Why e-mail automation is not an option
To help retailers benefit from these customer in-
sights, some e-mail automation companies offer
systems that deliver customized content to your
customers. These systems, though, do require extra
resources because you must:
• Own a data model that generates the customer
insights;
• Produce customized content for the different cus-
tomer segments, either internally or externally;
• Instruct the system to link the appropriate con-
tent to the right customer segment.
Ideally, you’d want the system that generates the
insight to also customize the content and deliver it.

12. COPYRIGHT © INTEMA SOLUTIONS 2013 12
A very sophisticated approach to scoring
Naturally, grocery stores and other retailers have
much more complex inventories than what was de-
scribed in the simple scoring approach above. Eggs,
for example, come in many variations: organic or
regular, brown or white, a half-dozen or a dozen to
a tray. That’s a lot to track by customer, by location,
even within a single commodity. If a customer regu-
larly buys a tray or two and then does not buy eggs
for a couple of weeks, the element of purchase fre-
quency also needs to be taken into account, which
further complicates our scoring.
And what if, in search of more precise customer
profiling, we also started tracking clicks on e-mails,
e-flyers, display ads, websites, time spent on web-
sites, Web searches, and other tangible actions
(e.g., Facebook likes and comments)? Scoring these
individual behaviors would lead us to a level of mi-
cro-segmentation that would increasingly approach
a one-to-one profile of your customer.
“…a predictive system,
though, must be structured
simply, comprehensibly,
and offer marketers fore-
casts or predictions they
can act on quickly.”

13. COPYRIGHT © INTEMA SOLUTIONS 2013 13
Going predictive
Having this sophisticated scoring system tied to a
predictive data model allows you to project likely
future behaviors for Mr. or Ms. Grocery Shopper.
Such a predictive system, though, must be struc-
tured simply, comprehensibly, and offer marketers
forecasts or predictions they can act on quickly.
So how do you get to the stage where your firm can
take such a sophisticated scoring model and market
your merchandise based on forecasts of individual
customer behavior? The development of a well-en-
gineered data model and the supporting analytics
may seem like a mammoth undertaking because
you have to:
• Set up sophisticated algorithms that allow the
system to learn with every transaction/data entry
while also minimizing your dependence on ana-
lysts;
• Customize the content per customer, based on
their predicted needs and affinities;
• Create intelligent reporting regarding customer
growth after each interaction.
This is indeed a mammoth undertaking—if you do it
on your own. But you don’t have to. You can partner
with a firm that has already built the scoring algo-
rithms and set up this future practice for you today.
“…the best way to get your
feet wet in the big data
pool is to start with a pilot
project.”

14. COPYRIGHT © INTEMA SOLUTIONS 2013 14
A cost-effective predictive solution that’s
ready now
Consultants in big data recommend that the best
way to get your feet wet in the big data pool is to
start with a pilot project. Such a project would allow
you to get a proof-of-concept for what big data can
do for your company. Concurrently, your people
would gain valuable experience using a powerful
sales-generation tool that will become the norm for
all thriving retailers in the near future.
For retailers and other companies that communicate
regularly with their customers through e-flyers or
newsletters, Intema offers a cost-effective, quick-
to-implement predictive solution—their Predictive
Marketing Engine®
. With this tool, you could:
• Increase customer engagement and sales by dis-
tributing more relevant communications to your
customers;
• Reduce your cost for customized content by
streamlining and automating its production;
• Enhance your customer insight through the con-
stant fine-tuning of scoring and analysis of cus-
tomer behaviors;
• Test how predictive marketing can grow your
sales. As your sales grow, the Predictive Market-
ing Engine®
grows with you.

15. COPYRIGHT © INTEMA SOLUTIONS 2013 15
HowthePredictiveMarketingEngine®
works
Combining a predictive analytics tool and a content
customization and delivery system, the Predictive
Marketing Engine®
:
1. Assigns a predictive score to each of your cus-
tomers and key words about your products.
The predictive score is based on the data collect-
ed from your online data in e-mail distributions
and website visits as well as your offline data in
store-level transactions. Every act of customer
behavior has an effect on the customer’s score
and changes the product’s key-word scores in
line with a self-building scale that’s aligned with
the predictive marketing model.
2. Identifies, produces, and distributes relevant
content for each customer.
The algorithm does its work of customizing the
content per customer, and the Predictive Market-
ing Engine®
produces and distributes either an
e-flyer or newsletter.
3. Learns from customer behavior.
Whenever the customer buys items in your store
or interacts with the new content or other data
sources feeding the Predictive Marketing Engine®
,
the system learns more about the customer and
enhances its predictive capability.
“…the system learns more
about the customer and
enhances its predictive
capability.”

16. COPYRIGHT © INTEMA SOLUTIONS 2013 16
Using retail’s big power tool today
Predictive marketing is not a fad. Understanding
what your customers are like on a one-to-one level
and what they’re likely to buy gives you a powerful
tool to improve sales, competitive advantage, and
market share. This strategic tool is already buzzing
in the hands of the forward-thinking competition.
Partnering with an analytical service provider like
Intema to harness the power of predictive market-
ing offers you a cost-effective, sales-boosting pilot
project to harvest the potential of your big data and
take a big step forward in securing your long-term
success.
For more information, contact Intema today
INTEMA
Toll-free: 1-866-632-7217
E: info@intema.ca
T: (514) 861-1881
As a multi-channel retail consultant, Doug Edwards has
been listening to and analyzing retail customers through
their transactional data and/or online behavior for 30 years.
REFERENCES
McKinsey Global Institute. “Big Data: The Next Frontier for Innovation,
Competition, and Productivity.” May 2011. Executive Summary.
The Official @WalmartLabs Blog. “We Predict Big Data Will Move Much Faster.”
June 10, 2013. [Cited June 24, 2013.]
<http://walmartlabs.blogspot.ca/2013/06/we-predict-big-data-will-move-
much.html>