Clarify the data basis
We check which data on customers, purchases, contracts and interactions is available and how reliable it is.
Services / Customer Strategy / Segmentation
Not every customer is the same, and not every customer should be treated the same. Segmentation shows which target groups exist and what they are worth.
ProNexum develops segments that can be mapped in the CRM, measured and treated differently, and links them to customer value.
Segmentation divides the customer base and the market into clearly defined target groups by behaviour, needs and customer value. Demographics alone are rarely enough.
Customer value includes contribution margin, lifetime value and potential. It shows which segments contribute most today and in the future.
A segment only makes sense if it can be treated differently, is measurable and can be mapped in the CRM. Segments are also the basis for value proposition, customer portfolio and, where one exists, tiers in the loyalty programme.
Segmentation looks not only at the existing customer base but also at the market. The TAM, SAM and SOM model has proven useful here. It shows how large the overall market is, which part of it the company can reach, and what share it can realistically win.
This allows segments to be assessed by market potential, target values to be tested for plausibility, and growth goals to be derived from the market rather than from the existing base alone.
These patterns lead to segmentations that are not used in daily work.
Ten or more segments are hard to manage. A few clearly distinct segments have more impact.
Age and place of residence rarely explain behaviour and needs. Behaviour and customer value are more telling.
Segments that cannot be formed from existing data remain a concept on slides.
Looking only at current customers overlooks the market and target groups the company does not yet reach.
If offer, service and communication stay the same for everyone, segmentation changes nothing.
Five steps from the data basis to an anchored segmentation.
We check which data on customers, purchases, contracts and interactions is available and how reliable it is.
We define customer value, for example via contribution margin, lifetime value and potential, and calculate it for the customer base.
We form segments by behaviour, needs and value and name them so they are understood across the company.
We check with data and in conversation with sales and service whether the segments are distinct and can be treated differently.
We map the segment logic in the CRM and define who maintains it and how it feeds into steering and planning.
At the end you have a segmentation that is actually used in the company.
Illustrative example
A retailer has so far distinguished only by age and region. Segmentation by behaviour and customer value instead shows four groups: loyal regulars with high value, occasional buyers, price-driven bargain hunters and customers at risk of leaving who used to have high value. Each group gets different goals, a different offer and a different approach.
Segmentation builds on target picture and goals and provides the target groups for the value proposition.
Segments are managed differently in the Customer Lifecycle. They also form the basis for tiers and benefits when a loyalty ecosystem is built (Loyalty Consulting).
FAQ
Usually three to seven are enough. What matters is that each segment can be treated differently. If two segments would receive the same approach and offer, they belong together.
Both have their place. RFM (recency, frequency, monetary) is based on purchase behaviour and is quick to implement. Needs-based segments explain why customers behave as they do. A combination is often useful.
Yes. In B2B we often segment by industry, size, buying behaviour, needs and value of the business relationship, and take into account that several people are involved in purchasing.
To start, customer, revenue and contract data from CRM or ERP is usually enough. The better behavioural and interaction data is available, the more precise the segments. Missing data is not an obstacle but a finding that we put into context with you.
We would be glad to discuss your starting point, goals and a sensible first step.