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Personalized customer communication seems to be already a known concept, at least judged on the basis of the amount of material, blogs, webinars on this topic. But to make this personalized communication reality - sending out emails with the right content to the right addressee inside an organization, guide the sales force with promising new contacts or leads - this still seems to be a long way to go.

Still often all customers are treated in the same way, one message for all, or only very limited customer segmentation. Mass distribution of generic messages is still common - with generic sales brochures, same offers in the webshop for all – independent of small or large customer, or to which segment the customer belongs to. Communication is often single-sided, and the customer reacts with buying a product – or not buying it.

How can a distributor get to a targeted 1:1 interaction with his customers? One of the important key factors is customer segmentation. It is the frequently underestimated basis for a personalized communication. Segmentation comprises tracking and analyzing of data to get a better understanding and addressing of target groups.

One approach of B2B customer segmentation* starts with a macro segmentation on company level, taking into consideration aspects such as demographic criterias (size, segment, purchasing volume), organizational criterias (purchasing politic, contribution margin, average order size) and also geographic criterias. It needs to continue on the individual level (micro segmentation) focusing on personal criterias such as risk disposition, style of decision taking but also demographic criteria (age, position in the company, department the purchaser is assigned to).

To enable this segmentation possibities the relevant data needs to be collected in one repository, which is able to store information on company and individual level and to relate them to each other. Such a repository needs to have open interfaces for various sources - ERP and CRM system, browsing data from web channel, social media posts, survey results and so on. A good customer data base might not be achived at once but a starting point can be the analysis of the ERP sales data and step by step data of other sources will be added to a many-faceted picture of customer organizations and their employees.

Flexible segmentation models use this data to create the perfect target groups for various campaigns and communications strategies - for the small and large companies, for the risk taking or more cautions purchasers, for the ones with high contribution margin or those with high order volume, even knowing their preferred communication channel. And beyond of this it is possible to detect sleeping customers and identify those who are about to churn - and adress them with the right communication to activate them again.


* as described by Peter Godefroid/Waldemar Pförtsch in their book on B2B Marketing