Artificial Intelligence

Propensity Models: Capillary’s Secret Sauce for Marketers to Predict Consumer Behavior

Propensity Models are AI & ML algorithms that help marketers learn about the preferences of a consumer and devise loyalty programs accordingly.


Jubin Mehta

4 Min Read

November 16, 2022

Businesses and marketers inherently try to predict customer behavior. Be it a mom&pop store intuitively gauging shopper intent or a large enterprise using data analytics, Artificial Intelligence (AI), and machine learning (ML) to predict consumer behavior and achieve business goals. In helping enterprises achieve this, Propensity Models play a large role. The statistical technique was developed by Paul Rosenbaum and Donald Rubin in 1983. Propensity Models are statistical frameworks that try to estimate the likelihood of people behaving in a certain way. When coded into a system like Capillary, these become tools in the form of filters to help loyalty marketers run better programs.

Challenges for a Loyalty Marketer


For a marketer to run effective programs, knowing the consumer and targeting the right audience with the right message is the key. This is possible when you have a handful of customers but if we’re talking of thousands and millions of end customers across a nation or throughout the world, segmentation becomes a huge challenge.


Audience segmentation: Certain triggers like offers and promotions would only work in a particular context. For instance, if you send a “We miss you!” email to all customers in your database, it will create confusion for active customers. The system should be intelligent enough to give you this segmentation to run effective campaigns.


Too much data: Sometimes, data can get overwhelming. Different kinds of data (zero, first, second, third-party data) and the inability to make sense of it at scale can become a challenge for marketers. Data is useful only when there are systems that interpret the data and give actionable insights.


Customers with multiple identities: Many times, a customer may be associated with several identifiers, and marketers have to ensure that they have the latest data. To add to the problem, customers may change their accounts or alter their preferences which add to the complexity of the problem.

Capillary Audience Filters: A Primer

Based on Propensity Modeling, Capillary’s team of data scientists has built predictive models used as audience filters to help loyalty marketers overcome their challenges. Capillary’s audience filters provide a proven capability to target end customers based on their shopping behavior. These AI-powered filters are backed by Capillary’s artificial intelligence retail analytics (aiRA) technology which runs models on billions of transactional data points to generate accurate results.

Types of AI-based Audience Filters and Use Cases


Capillary provides a host of filters based on many business requirements. The results of the filters can be used to create an ideal audience list that can be targeted with campaigns, promotions, communications, loyalty benefits, and more. There are use cases across verticals– from retail and fashion to CPG to F&B/QSR and more.

If we consider fashion as a segment, a simple instance could be about a brand using the ‘gender’ audience. If there’s a men’s range of clothing that needs to be promoted, this filter would be called into action. Another widely prevalent use case would be to reward customers based on their average shopping amount. If a consumer has shopped above a certain threshold, they could be rewarded with a discount or a further offer. These are simple examples but all audience group filters can be classified into several types:


Loyalty stage filters: The first stage of audience segmentation will divide customers into- loyal, non-loyal, or customers that haven’t yet enrolled in the company’s loyalty program. Drilling further, the audience can be segmented into customers who have active points, customers whose points have expired, whose slab has recently changed, who are registered at a particular store or a zone, and such.


Transaction-based filters: Another key parameter on the basis of which the audience is segmented is transaction history. These filters can help you segment the audience on the basis of when they shopped, what they have shopped for, their visit count, number of transactions, total transaction amount, and several other parameters.


Campaigns & Coupons based filters: For any marketing program, coupons and vouchers are an important element for customer engagement. To optimize the use of this tactic, there are filters that let you build groups on the basis of- customers to whom coupons were issued during a specific period, customers who redeemed their coupons, customers who have responded to a campaign, and such.


User Profile-based filters: This is another major parameter on the basis of which audience lists are created. Depending on how rich and accurate your user profile is, the more effective this filter will be. Audiences can be segmented on the basis of- Subscription status (mobile number, email subscription preference), NDNC (National Do Not Disturb) status, demographic details, fraud propensity status of a customer, and many other parameters.


Purchase pattern filters: These are the most potent set of filters a loyalty marketer is most interested in. These filters include the ability to segment the audience according to a customer’s- average transaction value, most recent purchase, preferred day/time of shopping, the probability to be incentivized by discounts, average spend per unit item, and many more.


Artificially intelligent filters: aiRA (artificial intelligence retail analytics) powered filters are designed to target customers with a specific prediction. When you combine aiRA-powered filters with the above standard filters, the chances are much higher to achieve a better hit rate, incremental sales, and avenues to offer relevant products to customers at the right time. Some of these filters include capabilities through which one can predict when a customer would transact or when a customer will lapse.

Perfect Your Customer Outreach, Redefined with Propensity Modelling

Capillary’s advanced filters along with the aiRA-based filters give a loyalty marketer the superpower to execute precise marketing programs and deliver business results. In this age of competition and excess of everything, it is important for brands to have meaningful data and actionable insights to make sense of it. This is what Capillary’s filters do and help loyalty marketers perfect customer outreach and deliver a great end-customer experience.

Aauthor Name

Jubin Mehta

Jubin Mehta is the Associate Director of Marketing at Capillary Technologies. He is a full stack marketer with 10+ years of experience as a tech journalist and B2B SaaS marketer. Also a meditation instructor, he is a proponent of non-dual philosophy. He can be reached out on Twitter @jub_in.

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