Generating data-driven analytical insights backed with personalized execution using digitized channels could substantially enhance consumers experience for the brand, there-by result-in long-term loyalty win-backs and potential rewards

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Generating data-driven analytical insights
backed with personalized execution using
digitized channels could substantially enhance
consumers experience for the brand, there-by
result-in long-term loyalty win-backs and
potential rewards
The correlation between brand loyalty and bargaining
favorable consumer finance is not new. Brand loyal
consumers are likely to negotiate better prices, since fixed
prices are virtually unknown and discounts are ubiquitous
and order of the day. This interplay has been running for
ages. In the 1800s for instance, merchants on London’s
Bond Street would typically extend 6-9 months credit to
their consumers. Even the practice of modern-day brand
loyalty card dates back to the 1980s.
What is new today is that brands know a lot more about the
demographics and behavior of their consumers. One way
to leverage this insights to add on to your consumers
experience is to supplement them with co-branded loyalty
cards. Co-branded programs do provide information into
consumer’s lifestyles and preferences, including
understanding a brand’s likely share of wallet.
Connect this information with Analytics insights (using
advanced Statistical approaches) and digital channels. You
can then action on the derived insights using channels (say
smart device apps) to deliver targeted, tailored, real-time
offers based on individual preference pattern. For eg,

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matching consumers location information to promote
products, value-added services using the Co-branded
program, which should then have a higher uptake given
that they are designed leveraging insights from their
historical purchase behaviors and preferences. Brands can
further enrich their personalized messaging by
incorporating unstructured social data, thereby aggregating
the public knowledge about ‘likes’, ‘comments’, ‘follows’,
‘shares’, ‘tweets’, ‘uploads’ etc.
A marketing promotion designed to run campaigns backed
with such 360-insights should result in enhanced consumer
experience and thereby result in long-term win-backs from
competition, increased wallet-share and revenue
increments through targeted cross/up-sell for eg; a
promoted dining, entertainment or luxury experience
promotion in conjunction with main purchase.
Today these tailored personalized offers can be directly
linked to consumers’ loyalty programs /
accounts. Implementation of this can be through digitized
wallets (eg; Apple Pay), which allows users to store
coupons, event tickets, loyalty cards and credit cards on
their handheld.
Mentioned below is a practical use-case to demonstrate the
above correlation leveraging analytics insights and
digitized execution;

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Business Challenge
• Client is a leading airlines company in the United States
• The airlines wanted to;
 Identify members whose behavioral
characteristics are similar to their current loyalty
program’s Signature and Platinum Visa members.
Airlines wanted to use the member information
to expand their existing loyalty Visa membership
to target prospects
 Airline would also like to identify members from
its current loyalty base who would be suitable
candidates to offer its recently launched co-
branded program, having varied affiliate tie-ups
with premium products, services, fashion and
leisure experience providers
Business Solution I. Expanding existing loyalty
program Visa membership
 A representative population was extracted from the last
one year data of a leading US travel-hospitality loyalty
program considering members who were US residents
and would fall under one of the categories of air, non-air
or award active from both Visa and non-Visa members
 A look-a-like model (using logistics regression), similar
to that of the airlines existing loyalty program was then
developed to identify potential prospects from the
representative sample population
 Suitable candidates would depict a behavioral pattern
similar to that of its current loyalty members already
holding the Visa card
 To bring in appropriate weightages, the model was
developed with % samples of current Visa and %
samples of Non-Visa members

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 The model identified over 30% of Visa cardholders in the
top decile and over 83% in top 5 deciles as suitable target
prospects
 The actual proportion of Visa cardholders in the top
decile was 29.9% versus an average of 10.29%
Business Solution II. Identify suitable prospects to
extend the recently launched co-branded card offer
 Suitable candidates would have a behavioral profile
similar to current loyalty members who already hold the
co-branded card
 A propensity model (Using logistic regression) was
created to identify suitable target candidates from the
loyalty program members who were non holders
 The model helped understand the propensity of
customers who would be much likely to accept the co-
branded visa card.
 To further enhance the experience and financial success
of the program, social insights were infused upon those
who had been offered and newly inducted, from the
target set. Social listening and digitized profiling was
conducted upon this prospect set, helping mine
incremental insights regarding individual’s behavioral
preferences.
 Leveraging this in the next step, the new inductees were
clustered based on demographic, social and behavioral
variables. A personalized promotion strategy was
designed for each segment. The predesigned array of
customized campaigns were then executed periodically
in-line with the segments behavioral preferences. The
campaign lifts of were measured against a control group.
Business Outcome
• Both developed models were implemented by client in a
phase-wise approach

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• In solution I the recommended segment was targeted for
expanding existing loyalty Visa membership. The offer
uptake in the targeted segment was 38% above random.
Usage of data-driven decision making for expanding the
program, resulted in rich dividends for the client. Out of
the newly inducted program members, 52% became
medium term brand advocates. Their stickiness to fly
with the same airline demonstrated the success of the
airlines loyalty base expansion, resulting in meaningful
incremental revenue over the period of next 12 months.
• In solution II the model recommended segment were
targeted to offer the co-branded card. The offer uptake in
the targeted segment was 43% over average. Further the
personalized targeted promotions resulted in much
higher conversions vis-a-vis the control group.
Appropriate data driven cross-sell/up-sell propositions
backed with real-time / near real-time digitized
execution of affiliate products, services, fashion and
luxury experiences resulted in substantial incremental
revenue for the client over the next 9-12 months period.