Increasing Customer Engagement #2

This is the 2nd of a series of six articles which outlines a roadmap for companies to embrace a “Customer Engagement Model” for increasing customer engagement.

Step 2: Customer Preference Generation – Segmentation Analysis

Customer Preference Generation/SegmentationMarketing involves numerous decisions concerning what products, services, and support should be offered to which customers, at what price, through which channels, and when. For these decisions to lead to success (measured for example in terms of customer loyalty and customer profitability) requires the marketing professional to develop an understanding of large volumes of complex data (possibly “big data“).

As always, marketing efforts focus on making sense of customer needs. The natural tendency is to break the available macro-level data down into smaller pieces, in the belief that each of the smaller pieces will be easier to understand (segmentation). It is hoped that this will result in better-informed decisions to support the company’s effort to increase spending by their customers.

Customer segmentation is the process of breaking down a large group of customers into smaller groups. The idea is to partition a heterogeneous market into separate and distinct homogeneous segments, where a segment comprises a group of consumers that react in a similar way to a given set of marketing stimuli.

Once a market has been segmented, further analysis can reveal interesting patterns within individual segments, and it may then prove profitable to conduct further segmentation processes on the individual segments that resulted from the initial segmentation process.

The key to accessing more detailed and relevant data to support enhanced segmentation is the understanding of customer preference. Customers are willing to “opt-in” for personalized offers, on the basis that their preferences and needs are taken into consideration. They will only grant access to suppliers which offer items in which they are personally interested. This is a matter of TRUST. Once lost, it is hard to regain.

The best source of micro-level data is the customer. Many companies have customer rewards programs that enable the capture and storage of customer data. These companies have in-place ‘rewards programs’ that gather a base level of customer preference information. In most cases, it’s a very small amount of data, based on what is asked for and may or may not be completed by the customer upon enrollment in the program.

The starting point is a comprehensive customer preference engine with data stored securely in a CODB (customer oriented database). A properly designed CODB provides key data to the company’s marketing team, that enables them to create and manage relevant (personalized) offers based on the customers’ needs, but in support of the company’s marketing operation.