Delivering the best value proposition using segmentation is a multi-step journey with 6 main steps and some critical differences from other analytic approaches:
- Define Segmentation Objectives
The first step – deciding why to build a segmentation scheme – is important but often overlooked. Reasons may include declining financial performance, changes in strategy or market trends – the current segmentation just does not meet the company’s goals. - Conceptualize Segments
Not using analytics yet – just using the overarching business objectives to identify the most useful segmentation approach. Time to engage domain experts to discuss possible approaches and come up with a few possible approaches that might work like demographic or geographic or customer value-based. - Gather Data
Data comes from lots of sources and once you pull it together and analyze it you must expect a feedback loop that changes the data you have or want. Constant improvement is the order of the day. You can’t build segmentation with data you don’t have. You need to also be sure the data is going to continue to be available and that it will be available at production. Data comes from all channels and might include transactional, behavior, demographic and attitudinal data. Consider complaints, customer service calls and more for attitudinal data. Multi-channel data matters as customers engaged in multiple channels tend to be much more loyal, for instance. - Apply Analytics to Develop Segments
Decision trees, regression and clustering are always the most popular techniques when data miners are polled. This is a very iterative process for clustering, just as it is for building trees or scorecards. In many ways clustering takes the same steps for loading data, exploring it and transforming variables for use. The challenge is reducing your candidate variables to a list that will drive good clustering and this is where the skill of a modeler and the engagement of business users are critical. Developing the segments, profiling them and getting the segmentation code at the end is also pretty familiar to most analysts. Xeno supports the whole process with a number of specific features like variable selection metrics, outlier handling, discrete and continuous variables and more. - Develop Marketing Applications for Segments
Name segments and profile them with characteristics. Size them, prioritize them and create a value proposition for each. Prioritization involves considering the size of the segment, the value of the segment (size, revenue, growth, profitability) and deployment potential (how compelling a value proposition, suitable deployment resources). Figure out what changes you need to your operational processes, resources etc. You should also consider building personas (from Alan Cooper’s work) for these segments (I blogged about using analytics and personas together before). Examples of companies using personas include Dell, Fedex and Best Buy. - Build Deployment Strategies
Last step is identify key marketing levers for segments and create unique value propositions/programs for each. Roll them out and track, track, track.
Flora also walked through some case studies. First was a services company offering residential services across multiple brands. Lots of data but challenged to cross-sell and up-sell. Using clustering to find customer segments and developing personas to give color to these segments helps clarify the motivators and potential home service purchases for each. Segments were based on income, home ownership and length of time in residence. Segments included “snug as a bug” families and “old timer” retired couples. A second case study was focused on collections and had segments like “struggling”, “don’t bother me” and “credit users” depending on internal balance, internal risk and external risk.
