Segmentation / Clustering
Get to know your existing and new clients better
Segmentation/clustering is an analysis processing customer characteristics, making segments with maximum internal homogeneity and external heterogeneity.
The first step to an efficient and successful sale of products and services and relationship with customers? Knowing your existing and potential customers. Customers have different consumer behaviour, use products and services differently, have different contact preferences and many other dissimilarities. Segmentation allows you to find groups with similar characteristics in your customer portfolio and enable you to manage your relationship with them more efficiently and cost-effectively. If you focus on what customers really prefer and need, they will certainly be happier and more loyal. In addition, customer segmentation can be beneficial and useful for other departments of your company as well.
The Advanced Analytics Deloitte Team has experience with creating and deploying segmentation in the B2C as well as B2B customer environment. Segmentation can work independently or it can serve as a predictor in other analytical models focused more deeply on specific business problems, such as retention, acquisition, cross-sell etc. Whether the solution to your request will be segmentation on its own or if it will be part of another solution always depends on an analysis of the specific request and the problem at hand. We will always recommend, create, deploy and test the best solution with the best result. We make sure that the selected segmentation method is transparent and its result brings maximum internal homogeneity and external heterogeneity of the segments.
Using behavioural segmentation, we have improved service models for exiting corporate clients of a major Polish bank and determined the most suitable service for new clients. Financial impact of EUR 4 million per year.
Areas of segmentation use:
We apply non-client segmentation developed on external data on clients and measure profitability, loyalty and growth potential for each segment. This allows us to assess the profitability, loyalty and growth potential also for non-clients and adapt the acquisition campaign accordingly.
We use the segment-of-one approach to cross-sell. This means that a customer represents an independent unit. We achieve this by estimating PtB models in rough segments, but each customer has an individual score from the PtB model.
In the retention programme we segment customers based on their value, propensity to churn and propensity to save. These three key indicators create a business case determining which retention strategy and which offer will be communicated to the customer.
Customer segments with different revenue potential and different communication channel preferences receive different messages via different channels at different frequencies. The number of segments is given by a compromise between the requirement of highly individual service and overall service costs.
Customer Lifetime Value
In modelling CLV using Markow chains, the key input is a sufficiently fine segmentation explaining short-term income. The probabilities of transfer between segments then determine future scenarios from which we infer long-term income.
The dynamic pricing solution is based on price elasticity. Price elasticity is individual for each customer, but at the same time it can be estimated accurately only using a statistical set of many customers. Customer segmentation represents a good compromise. The dynamic pricing solution includes one more segmentation – segmentation of types of goods based on their price and the shape of the demand curve.