Analytical CRM (aCRM) examines the purchasing behaviour of customers and/or customer groups. As part of this process, new, relevant information is gathered from existing customer information and patterns uncovered using mathematical statistical procedures (data mining).
- aCRM allows you to sharpen your target audience profile by forecasting certain characteristics, behaviour and added-value possibilities. For example, by identifying particularly profitable customers or customers who are likely to leave, as well as particularly profitable products or services.
- Detailed descriptions of these segments with extensive data from the B2B and B2C Schober marketing databases: a range of different analytical procedures provides you with a precise profile of your customers for your database marketing and gives you concrete starting points for the development of your CRM measures.
The knowledge gained serves as an important basis for both operative and strategic sales and marketing decisions. You reap the rewards in three fields at the same time:
- More information on new customer acquisition: which potential is to be found in your interest groups, how would your newly acquired customers rate in more and fewer advertising relevant groups, or when and which channels should you use to best contact potential new customers?
- More information on existing customer optimisation: what do your top customers’ profiles look like, how can you make the most of existing customer relationships, how do you reactivate inactive customers or how do you recognise and gain the loyalty of customers who are at risk of leaving?
- More information about your markets: what is your position in your relevant markets, what is your level of market penetration and where is there fallow market potential?
The data that is analysed as part of the analytical CRM come from typical internet transactions (sales, contract and master data), which is effectively information from your own CRM system and/or your own customer data.
This is also enhanced with external data (for example, other purchasing habits, social stratum/purchasing power, geographical data or sales category, number of employees, industry, and so on). A holistic overview of the customers you have considered emerges through a range of different analytical methods. This makes it possible to draw conclusions about the features, behavioural characteristics and added-value possibilities.