Heads you win, tails you win–micro-segmentation in banking for high impact promotions and offers

By Suman Mandal, Vice President – Banking Solutions Group, VirtusaPolaris 

As banks take the digital route, sometimes even “digital only” approaches, traditional means of reaching out to the customers still remain relevant. Emails and SMS continue to be prominent modes of reaching out to the customers with specific offers and promotions. However, data shows over 77% of people never open emails sent by their bank. Further, fewer than 3% read and click on the links.

Banks usually run anywhere between two to five broadcast campaigns and promotions at any given point in time. The traditional approaches to figuring out what to offer and to whom, and the turnaround time regarding production and execution, easily takes a good two to four weeks.

While a specific bank may be running a few campaigns at a time, customers are being subjected to campaigns from multiple banks. As customers are flooded with choice, they pay attention only to those products or services that are most relevant to them, at that specific moment; especially when it comes to features, terms and conditions, pricing or availability. Staying relevant by identifying specific customer needs at a given moment is crucial. Micro-segmentation’s role is critical in terms of helping banks achieve this aim, rapidly find out what to offer and whom to offer; with the help of machine learning techniques applied on big data (comprising of customers transactions, interactions, demographic profile along with data from sources external to the bank).

Banking offers and promotions ripe for improvement with micro-segmentation

Consider how banks traditionally come up with offer and promotions: Other than the planned product launches, banks often, to boost revenue targets for specific products or categories, come up with promotions such as “festivity themed discounts”, “spend-more-save-more” offers, etc. These target customers based on demographics, income bracket, and sometimes aiming specific spending behaviour, such as somebody who recently transacted at a supermarket or an airline and so on. Usually IT or the analytics team then comes up with a list of customers to whom the promotional offers are sent.

When a bank runs time-bound promotions and reaches out to its customers on a one on one basis and gets above industry average outcomes (~ 6 % and more) consistently, it could mean:

  • The product / offer / promotion is unmatched with respect to its value proposition and is supported by great sales, distribution, marketing, brand visibility and reputation, or
  • The bank has an unusual way of measuring success and the success of a promotion need not necessarily have an impact on the revenues, or
  • The customer base is very homogeneous

A micro-segmentation approach can help in all the above cases. If the product is outstanding and well supported by sales, micro-segmentation can be used to find out – 1) Pockets of customers who are willing to pay a premium for customisation 2) Scope to reduce/redistribute marketing mix (e.g. from paid media to earned media) 3) Optimise sales and distribution strategy (e.g. tiered to flat or rewards programs).

To review the process involved in measuring success of promotions, defining success itself is extremely important. Success of a campaign can mean different things to different people: from an expression of intent to buy through an actual purchase. A quick check of the receivers of the offers (both those who bought into the offer and those who did not), as well as those who were not supposed to get the offer, can open up gaps in the execution process. Overestimating success in a promotion can be due to underreporting of the number of people who received the offer.

Consider this situation (and associated number of customers):

  • Offer made via SMS/Email                                                                 – 100
  • Customers who deleted the message without reading                  – 50
  • Customers who responded to unsubscribe                                      – 5
  • Customers who responded to know more                                       – 45
  • Customers who initiated discussion on T&C and pricing            – 25
  • Customers who eventually bought                                                   – 5

Depending on how success rate is reported, it could vary from 20% to 5%.

Finally, in case the customer base is in fact homogenous, the bank can launch a product category or brand targeting other types of customers. Micro-segmentation can help in this case extraordinarily well. Combining information on existing customers as well as “prospects” who approach the bank, but do no end up buying, reveal white spaces / opportunities. Micro-segmentation can assist in finding these pockets of growth and help during the process of experimentation to find the suitable “needs and products / services” matches.

Banks today have enough internal data as well as access to external data to micro-segment their customer base to make more personalised, customised and relevant products, offers and services. The technology is available to extract insights without writing elaborate queries. The impact and improvement that micro-segmentation can offer is equally high for banks enjoying high levels of success as well as the banks that find it difficult to beat industry averages.


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