10 Dec 2025
Segmentation and clustering play a fundamental role in targeting your product, communication and interaction to the desired audience. After all you want your audience to pause for a moment and listen to what you have to say. Clustering techniques uncover organic patterns and through propensity and churn models, you can identify the probability of a customer taking a specific action or a customer stopping from using your product. Through agent-based modelling simulation each agent represents a specific customer type with rules, preferences and so on, and this is useful when exploring strategies which affect multi-channel behaviour. Read our article about understanding your customers through modelling & simulation.
Clustering and segmentation play an important role when it comes to grouping customers together. Clustering techniques uncover organic patterns, ranging from lifestyle, digital behaviour and purchasing patterns, just to name a few and with proper clustering you can tailor your product and communication in an adequate and targeted way so that you appeal to your audience, so that your potential customers "stop and listen" to you amidst the "noise" they are exposed to.
Propensity models help estimate what is the likelihood that a customer takes a specific action, which could be to buy a product, accept a new offer, renew a subscription and so on. On the other side, churn models focus on the opposite and so the likelihood of the customer stopping from doing something e.g. leave a subscription, change supplier etc.. Let's take fashion retailers for instance, they often use such models to identify customers which are most likely to respond to a particular promotion, after all marketeers would want to make sure that their budgets are used and used in an efficient manner. As an example of churn models, these would be used by companies such as insurance firms, or even telecoms, to know how many customers are at risk of switching providers and intervene early, maybe with a personalised offer.
If we talk about simulation techniques, Agent-Based Modelling or ABM for short, allows companies to recreate how consumers behave, in a virtual space. So in this case each "agent" represents a certain type of customer withe rules, preferences and interactions. ABM is especially useful when for instance a company is exploring strategies that affect behaviour across various channels. Strategies could include for instance how customers will respond to a change in price, in delivery times or also a new loyalty reward. So companies in this space can test the various scenarios before implementing them in the real physical world.
So, data science, modelling and simulation together are powerful tools to provide a comprehensive toolkit to decode customer actions, anticipate their needs and therefore create more meaningful products, communication and interactions with your customers.