loyalty is rocket science for a major canadian grocer

© 2015 Fair Isaac Corporation. All rights reserved. 1

More than 40% of grocery sales now come from loyalty program members, who shop more frequently and buy more products than other customers.

INSIGHTS WHITE PAPER NO. 89

Analytics drive unique set of digital offers every week to over 9 million customers

Retail loyalty programs are ubiquitous and predictable — except in the Canadian grocery space. Here, a large retailer is changing the game, transforming what customers expect from loyalty programs and what retailers can accomplish with them.The grocer’s loyalty program uses sophisticated analytics to identify — from more than 380 billion possible offer combinations — the handful of offers that will be most relevant and appreciated by each customer for the upcoming week. The program is a game changer not only for its ability to anticipate customer needs, point sensitivities and even product sentiments, but also because it’s purely digital. Customers receive offers, along with recipes and other information, via the website, email or mobile app.

The loyalty program, which has rocketed to near the top of the nation’s grocery programs in just one year, is also changing the economics of cultivating loyalty. Marketing executives feel the program delivers high performance because it pinpoints where incentive investments will produce the most profitable changes in customer behavior. As a result, loyalty members are making more trips, buying bigger baskets and shopping more categories than nonmembers — and the company has substantially increased share of wallet from its best customers.

This white paper looks at how the grocer is achieving this success. We discuss how the retailer:

• Knows when customers will buy and what they care about most

• Predicts individual demand elasticity to point thresholds in offers

• Optimizes offer sets for best customer-company outcomes

• Learns what actually causes customers to buy

INSIGHTS WHITE PAPER Loyalty Is Rocket Science for a Major Canadian Grocer


How do you launch a new loyalty program in a saturated market during one of the worst years for grocery sales in decades…and rocket to success? By making relevance to individual customers the core fuel of everything you do.

In 2013, when a major Canadian grocer was preparing to launch its first loyalty program, millions of Canadians already had loyalty cards from other grocers in their wallets — yet grocery sales dropped 0.4% industry-wide. Clearly, there had to be a better way of growing recurring revenue than the largely indistinguishable approach taken by competitors.

The grocer decided to take a different approach. If it could digitally deliver relevant offers within opportune timeframes, customers would receive more realizable value. They would also have a tangible sense of being known and appreciated.

The company turned to FICO for advanced analytics to fuel its loyalty program. Predictive and prescriptive analytics tell the program how to make the best use of available marketing funds. Instead of indiscriminately dispensing points for every dollar spent, the program gives offers and rewards to customers based on a multidimensional understanding of their individual shopping behavior. Instead of cross-selling aimed at loading up baskets and pantries, it generates personally unique sets of offers that are tailored to the food and products members love and buy.

Customers are flocking to the program, attracted by the opportunity for up to double-digit savings on grocery bills, and thrilled by the relevancy and precision timing of the unique set of weekly offers they receive. Their Tweets tell the story: “How did they know we ran out of mayo this week?” “They really know me :)”

In its first year, the program has produced unequivocal evidence of the power of this next-generation approach to loyalty. More than 40% of the company’s grocery sales are now coming from program members, who shop more frequently and buy more products across more categories than other customers.

Figure 1: Using digital

channels for relevant,

highly personalized

communications

My own personalized weekly digital flyer!

MarkYour personalized

deals are ready.

Offers

Accepted

$ off KetchupExp. Aug 2nd

$ off SpaghettiExp. Aug 2nd

Ketchup

$ off BeefExp. Aug 2nd 90 pts

75 pts

71 pts

...to 1 for 1From 1 for all...

Thinking differently

about loyalty

September 2015



For over 9 million loyalty members (with large numbers of new members signing up every month), personalized offers are so relevant, it’s almost like having an old-fashioned relationship with the corner grocer. In this new-fashioned relationship, however, members can redeem their offers at more than a thousand stores of various types, in many locations across Canada.

Delivering a personally unique set of relevant offers every week on such massive scale is a computationally demanding, mathematically intense undertaking. The heavy lifting is performed by over 4,000 time-to-event (TTE) predictive models, generated and updated every four months by a FICO-built analytic “factory.”

As depicted in Figure 2, each TTE model predicts the propensity of a customer to purchase a particular product — say, a specific brand of laundry detergent — within a specific timeframe. Together these models output over 90 million offers per week — metrics that tell the grocer the likelihood, for every loyalty member, of buying each of some 60,000 products in the next seven days.

Analytics also help the grocer understand customer sensitivities to loyalty rewards. It is using such insights to make very smart investments of its incentives budgets. Its loyalty program is high-performance because incentives are focused where they are most likely to not only cultivate loyalty but also produce profitable changes in customer behavior.

Understanding

customers as more

than strings of

transactions

Figure 2: Purchase

probabilities and

offer susceptibilities

change rapidly

Time-to-event models pinpoint the best time to make an offer

Best offer timingis six weeks from now

PRO

PEN

SITY

TIME

Offers

A C DB

Simple probabilitymodel generates constantestimate over an extended

time horizon

TODAY

Analytics help the grocer focus incentives where they are most likely to not only cultivate loyalty but also produce profitable changes in customer behavior.



Each week, TTE models generate 90 million offer recommendations for loyalty members. As depicted in Figure 3, optimization is used to automatically select a maximum of 20 best offers per customer.

Optimization is a mathematical process that analyzes problems of huge dimensionality and complexity to identify the best choices for achieving a goal. For the grocer, the goal is to identify the offer set for each customer that maximizes relevancy and fuels profit.

The optimization engine in FICO® Analytic Offer Manager solves for this goal, while adhering to the grocer’s constraints that include concentrating a significant amount of incentive investment on its best customers. Other constraints are imposed by company investment targets and vendor trade marketing budgets. Optimization also enables the grocer to balance relevancy — a key driver for share-of-wallet growth — with other offer features that drive incremental sales.

Identifying the

best offers from

tens of millions

Figure 3: The big

numbers behind 1-to-19 millionmembers

60,000products

1,000stores

Prescriptive analytics select up to

20 offers per customer, optimized to maximize relevancy while meeting company/vendor objectives and constraints

380 billionpossible offer combinations

Predictive analytics generate

90 million scored offer

recommendations each week

What sets this loyalty program apart:

• Relevancy-driven

• Personally unique (no two customers receive the same offer set)

• Extends across multiple retail formats — including hard discount stores

• No base currency — offers and points allocated based on multidimensional customer behavior

• High-performance — analytics focus marketing funds where they produce biggest impact

http://www.fico.com/en/products/fico-analytic-offer-manager



To continue bringing loyalty members relevant, profitable offers each week — and get better and better at doing it — the grocer needs a constant stream of data that reveals cause and effect. Did the 1,000-point offer cause Carmen to buy a rotisserie chicken this week, or did the offer just correlate with a purchase she would have made anyway?

The only way the grocer could know for sure that offers are having the desired effect on an individual customer would be to observe what that customer does when presented with an offer compared to what they do when not presented with that offer. Since it’s impossible to observe two opposite conditions on the same customer simultaneously, the next best method is to conduct controlled experiments among customers with similar purchase probabilities.

A fundamental aspect of the grocer’s analytic process is to withhold an offer that would otherwise have appeared in an offer set from 10–15% of randomly selected customers. (See Figure 4.) In this way, the company is able to compare the behavior of this control group against that of customers with similar purchase probabilities who received the offer.

Learning what causes

customers to buy

Figure 4: Learning about cause and effect through ongoing controlled experimentation

In ongoing test-and-learn cycles: Among customers with similar propensity to make the same purchase this week, a randomly selected 10–15% will not receive the offer.

ProbabilityCustomers with similar probabilities to purchase

are given different treatments

Matched sample

Offer included

Offer excluded

GenevaYour personalized

deals are ready.

Offers

Accepted


$ off Hot DogsExp. Aug 2nd 90 pts

$ off KetchupExp. Aug 2nd 75 pts

71 pts

HOTDOGS

MarkYour personalized

deals are ready.

Offers

Accepted


$ off BeefExp. Aug 2nd 90 pts

$ off KetchupExp. Aug 2nd 75 pts

71 pts

Receives ketchup offer

Ketchup

K

Ketchup

Ketchup offer replaced by another offer

MUSTARD

Ketchup

Designing the experiment:

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The Insights white paper series provides briefings on research findings and technology innovations from FICO.

The rapid success of this loyalty program has created a rocket trail above the Canadian grocery landscape. As competitors try to trace it, the grocer is already looking to invent the next stage in loyalty programs and reaching out to FICO for the analytic innovation needed. According to the company, FICO analytics have fueled strong loyalty program performance thus far, and will continue to drive the next stage in its success.

To learn more about analytic best practices in retail, visit the FICO Blog and read these Insights papers:

• Marketing Mythbusting: Six Maxims Get Put to the Test

• From Big Data to Big Marketing: Seven Essentials

• Which Retail Analytics Do You Need?

Conclusion

FICO® Analytic Offer Manager enables companies to make relevant offers with the right incentive to the right individual at the right time — when most likely to act — and do it quickly for millions of customers. This precision approach has been proven to drive response rates in loyalty programs and general marketing from tepid industry averages of 2–3% to as much as 20–30%.

The system combines predictive and prescriptive analytics into a complete solution for fast-paced, data-driven marketing. Sophisticated time-to-event models predict customer purchasing behavior and the timeframe in which it will take place. Mathematical optimization balances multiple objectives within business constraints to identify the best actions for achieving an overall goal such as increasing profitability.

Together, these analytic insights enable companies to focus their marketing resources where they will produce the greatest results.

Righ

t Tim

e

Right Offer

PERSONALIZED AND OPTIMIZEDBY SEGMENT

TRIG

GER

ED R

EAL

TIM

EAD

HO

C BA

TCH Generic offer made

at the wrong time

Response ratesless than 1%

Generic offer made at the right time


Pertinent offer made at the wrong time


Timely and pertinent offer

Response ratesmore than 30%

Personalized marketing on a massive scale

http://www.fico.com/en/blogs/

http://www.fico.com/en/latest-thinking/white-papers/marketing-mythbustingsix-maxims-get-put-to-the-test

http://www.fico.com/en/latest-thinking/white-papers/from-big-data-to-big-marketing-seven-essentials

http://www.fico.com/en/latest-thinking/white-papers/insights-49-which-retail-analytics-do-you-need

http://www.fico.com/en/products/fico-analytic-offer-manager

loyalty is rocket science for a major canadian grocer

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