More turnover through optimized customer approach
Customer segmentation is always exciting from a marketing perspective. To form clusters of customer groups in order to be able to process them separately and in a targeted manner in terms of communication and sales is a must today.
Usually, an attempt is made to extract customer segments from within the company’s own customer relations management database. However, this has the disadvantage that these customer patterns are very self-referential.
INNOFACT has therefore further developed this approach by matching a segmentation against the background of the current market situation.
INNOFACT has direct access to over half a million consumers throughout Germany via its own panels and delivers results at an impressive depth, even with small samples. Clients from various industries have been using INNOFACT for years in order to research their target groups precisely and thus secure decisive competitive advantages in highly competitive markets.
1. Representative target group segmentation
First, a representative consumer survey is conducted, e.g. online. This forms the extensive data basis for representative target group segmentation. The actual segmentation is then carried out on the basis of factor and cluster analyses and a detailed description of the personas, including their buying behaviour, is created. Segmentation characteristics of the personas can be
- Ready to spend: Bargain vs. Premium
- Purchase frequency: frequent vs. rare
- Occasions: gift vs. personal use
Important: The segmentation characteristics of the representative survey must be compatible with the characteristics of your own CRM data. If a segmentation of buying behaviour is desired, CRM data consisting exclusively of age and gender will hardly lead to a targeted transfer. The CRM data is, so to speak, the “model” for the segmentation survey. A review of the CRM data base available in the company is absolutely necessary. An overview of the type and completeness of the data collected must be determined and any data that may be available separately or in multiple copies must be merged.
2. Exercise data for CRM
This is where the first merger takes place. The determined target group segmentation is linked to the company’s CRM data by first collecting the segmentation characteristics from step 1 for only a small representative sample of customers in the CRM database. This allows the surveyed customers to be assigned to the target group segments. This process step is important for first developing training data records for your own CRM.
3. Segmentation of the CRM data
In the final step of the linkage, classification rules are then trained on this initially small customer data record, which then enables an assignment (classification) of all customers based on your CRM data and independent of the variables used for the original segmentation. The aim is to check the determined segmentation to see whether rules can be developed that allow you to assign the customers to the corresponding segments in your own CRM database. Depending on the data situation, these rules are developed by using various modern classification methods such as discriminant analysis, neural networks, support vector machines or decision trees (random forests).
- By means of the three steps presented, large parts of the customers can be quickly and with comparatively little effort assigned to the target group segments by interviewing a fraction of the customers recorded in the CRM data.
- For the sales department, this means efficient use of CRM data, because the company’s own customers are addressed and processed in a much more targeted manner with a clean segmentation.
- And last but not least: Since a comparison with the market takes place, namely via the representative target group segmentation at the start of the process, companies can also see in which segments they are still underrepresented in their own customer base.