For businesses, it is essential to be able to base decisions on facts. However, especially when it comes to better aligning with customer needs or optimising customer relationships, the facts are often intransparent and it is difficult to assess which measures could bring about the desired effect. This would require reliable answers to questions such as:
Fortunately, the answers to these questions are available in practically every company – albeit hidden and distributed in thousands of sources. The task is to find, structure and bundle them and to turn them into systemic, evidence-based information. Analytical CRM makes this possible (see box).
The task of analytical CRM (Customer Relationship Management) is the systematic processing and evaluation of the data collected in the operative systems, especially the data on customer contacts and customer reactions, with the goal of optimal customer knowledge and customer profit along the customer relationship phases of acquisition, loyalty and churn.
How does this work in concrete terms? Usable information can be found, for example, in website visit statistics, in the marketing system in connection with campaigns, in operative CRM, point-of-sales systems, order and warehouse management, customer care in aftersales, etc. From this, it is necessary to draw the relevant data for the corresponding questions.
Let's take the example of customer acquisition. Existing customer data over the entire customer journey – from brand awareness to purchase – are used and analysed: What patterns are discernible? Where do customers drop out? Which customers react to an offer? Which channels are successful and when?
Both simple statistics and more advanced pattern recognition algorithms help here. This way, a big picture finally emerges from the many snippets of information from the numerous sources with completely new insights and profound information. These can be used as a basis for decision-making to make customer acquisition as promising as possible.
In order for the information to actually be available in the quality and form to serve as a valuable basis for decision-making, it is necessary to take the right steps – one after the other. First of all, a company needs to fundamentally evaluate the possibilities offered by the its data and where the possible limits are. Then it has to be found out which data bring the greatest benefit for the corresponding question.
The aforeshown graphic shows broadly what this step-by-step approach can look like in an iterative design thinking approach. It usually begins with the definition of the objective and the business benefit. On this basis, the concept work takes place, i.e. among other things, the economic viability and feasibility are checked. This is the condition for finally implementing a first productive solution in the form of a minimum viable product (MVP), with which the desired questions can be reliably answered. Based on the MVP, the solution can be successively further developed and constantly adapted to current needs. It is scalable and opens up the possibility of including new questions and thus gaining even deeper insights into your own customers.
If you are also interested in getting to know your customers better, you can find out more about this topic and about the possibilities of working with the experts from Trivadis - Part of Accenture here.