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Businesses and marketers inherently try to predict customer behavior. From a local shopkeeper intuitively reading a shopper’s intent to large enterprises leveraging the power of data analytics, Artificial Intelligence (AI), and machine learning (ML), the goal remains the same: understanding customers to drive better outcomes. In helping enterprises achieve this, Propensity Models play a large role.
First introduced by Paul Rosenbaum and Donald Rubin in 1983, propensity models are statistical frameworks designed to estimate the likelihood of specific customer actions. When integrated into advanced systems like Capillary, they evolve from theoretical tools into practical filters that empower loyalty marketers to craft smarter, more effective programs. Let’s explore how these models are reshaping the future of customer loyalty.
Propensity models are powerful tools that help businesses elevate their marketing efforts by making campaigns smarter, decisions sharper, and customers happier. These models use advanced statistical techniques to consider all the variables—independent and confounding—that influence customer behavior. Essentially, they predict the likelihood of a particular outcome, such as whether a lead will convert into a loyal customer, using Artificial Intelligence and Machine Learning.
Think of propensity models as the backbone of predictive marketing analytics. They uncover patterns in customer behavior through machine learning algorithms, enabling marketing teams to tackle business challenges with precision. From optimizing outbound sales processes to boosting sales volume, these data-driven insights empower businesses to make informed, impactful moves. In short, propensity models are the secret weapon for turning customer data into meaningful action.
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 adds to the complexity of the problem.
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.
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 & Coupon-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 will transact or when a customer will lapse.
Capillary’s advanced filters, powered by AI-driven aiRA technology, give loyalty marketers the edge they need to run highly targeted and impactful marketing programs. In today’s world of intense competition and information overload, having meaningful data is just the first step—turning it into actionable insights is where the magic happens. That’s exactly what Capillary’s filters deliver, empowering marketers to connect with customers in smarter, more precise ways and create truly memorable experiences.
Talk to our Loyalty Experts to learn more.
Propensity models use AI and machine learning to predict customer behavior, helping businesses create smarter campaigns, optimize decisions, and boost sales. As the backbone of predictive marketing, they uncover patterns in data to drive actionable insights and impactful results.
Propensity models help marketers enhance campaigns, target effectively, and predict behaviors like customer churn. By leveraging machine learning, they unlock greater accuracy and the full potential of personalized marketing.
Marketers face challenges like effective audience segmentation to avoid irrelevant messaging, managing overwhelming amounts of data to extract actionable insights, and dealing with customers having multiple or evolving identities, all of which complicate campaign precision.
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