In a nutshell, the role of analytical CRM (aCRM) systems is to analyze customer data collected through multiple sources and present that data in a way so that business managers can make more informed decisions, using techniques such as data mining, correlation and pattern recognition.
For years, marketing and CRM managers are fascinated by the prospect of precisely predicting the wishes and behavior of their customers. Because with the knowledge of who needs something at what time, the customer approach can reach a hitherto unknown target accuracy. Consequently, not only sales and revenue, but also the satisfaction of customers and brand loyalty rise. Anyway, most companies have understood by now the potential of analytical CRM, since it is a useful application for data and text mining techniques. Especially, since it is expected that customer requirements are getting even more fragmented and the approach of roughly sketched customer segments will no longer work.
At the same time, a rapidly growing amount of data is available from various sources. While this makes great demands on processing, it allows us to better understand individual customer requirements and to assess customer potentials properly.
Wish and reality
Nevertheless, the almost euphoric attitude towards the topic of analytical CRM in the context of Big Data and Predictive Analytics has so far hardly been reflected in practice. With regards to data integration and data analysis, most companies have still a lot to do, as they are often lacking in central databases, let alone interfaces at their disposal that allow the data exchange to take place smoothly.
Hence, it is also necessary to introduce technologies which allow to prepare information for further analysis and there is also a need for catching up on the ability to evaluate the collected information. Although most organizations today create a variety of reports based on historical data, hardly any is currently able to use the structured and unstructured data for data mining and text mining, because the methods used in this processes are significantly more demanding than the conventional approaches.
In addition to comprehensive data preparation, they require in fact an individual model development based on mathematical-statistical methods. In data mining, for instance, data are searched for patterns by means of various multivariate methods, e.g. to identify significant influencing factors for a purchase. The resulting models can then be applied to current data to formulate forecasts. When these are regularly matched with reality, the developed algorithm learns steadily and the predictions are becoming more and more accurate.
Roadmap to satisfied customers
If manufacturers and traders want to use this potential of aCRM, they must formulate their roadmap first. For their orientation and an exact definition of the purpose of the analysis it is helpful to differentiate between the customer group and the application scenarios:
In principle, a distinction can be made between potential, active, endangered, and lost customers along the customer relationship life cycle.
The application scenarios of the aCRM are divided into strategic and tactical cases: The customer value analysis, customer segmentation and customer characterization are part of the strategic dimension. That is why companies can use the findings gained here for the overall optimization of their CRM and communication strategy. Forecasts in these fields can be conveyed directly into concrete and person-specific measures.
That way, customers whose behavior hints at migration could be re-attached to the brand, for example, by means of a personal conversation or an attractive offer. Companies that are serious about analytical CRM not only need to become active in the two immediate areas of data integration and analysis. Substantial efforts are also needed to adapt the organization and processes to the new form of customer relationship management.