Guest commentary Prof. Dr. Andreas Moring, BiTS

Digital sales supervisors

Addressing customers in a targeted and appropriate way is possible – if Sales can comprehensively evaluate customer data and link it to key indicators. Digitization is one of the most important levers relevant for success in sales, if this structured data analysis goes hand-in-hand with meaningful, workable instructions.

Flexible, organized database population and data structures make it possible for Sales to use digital tools efficiently. Different filter algorithms and semantic database models form the inventory of a database to meet a certain objective with regard to findings or sales. With this analyzed and aggregated information, Sales can be more efficiently aligned with corporate goals and customer needs.

Clustering databases, relationship recognition, and sample analyses (predictive analytics) make it possible to predict customer behavior, evaluate different scenarios for customer segments and markets, and provide clear recommendations for appropriate NBAs (Next Best Actions) for Sales.


To ensure that this works in practice, the data and the results of its analyses must be provided in such a way that they are quickly understandable and usable, primarily for sales experts and not only for scientists that have specialized in that field. First and foremost, it is important that information design is based on the relevant sales targets, thus making it possible for data analyses to be used practically in the first place. Selection and definition of filing principles, cache technologies, and corresponding semantic data models are key to efficient data analysis and presentation of the results.

The graphical user interface that provides sales information is the critical point where digital-machine analysis meets human competence. Because optimally organizing sales measures and maintaining personal contact with customers are not technical issues, rather, they are part of the sales team’s competency.

The added value can be significantly increased in pre- and after-sales sectors. The analysis of customer data must be linked with key indicators to do this.


The aspect of evolving man-machine interaction is often overlooked in sales due to the fascinating abundance of technical options and “magic tricks”. Machines and technologies no longer only include the previously familiar tools and programs. Instead, they sometimes become autonomous assistants for the “Human Sales Force” in companies. Artificial intelligence and bots that act on the basis of databases and their evaluation will increasingly be used alongside sales employees. The “Technical Sales Force” does not simply define the preparation and legwork of active sales; it actively intervenes.

As a matter of fact, this is necessary: Because the volumes of data used for evaluation and analysis and the abundance of information that they produce can no longer be handled and put to good use by people alone. Humans become overwhelmed by larger volumes of information, while bots and computers become more effective and powerful the more data material they have.

Information and customer relationships will become increasingly individualized using these developments. General sales campaigns for one customer segment disintegrate into increasingly specific parts. Not only does that differentiate communication with customers, but also the paths used to gain customer information. The conclusion is that no sales team, no matter how large, is able to achieve this – not in terms of time, or quantity, or quality.


In contrast, if labor is redistributed between man and machine, the value chain structures in Sales can be improved. For example, in the pre-sales sector, the added value increases if they can individually address customers and make appropriate offers. The same goes for the after-sales sector: Individualized services and user-oriented communication can bind customers and foster their loyalty, which is often connected to an increased willingness to pay and thus new sources of revenue for companies.

is Head of the BiTS (Business and Information Technology School) in Hamburg, with courses in Digital Business & Data Science as well as Communication & Media Management. His specialties are innovation management and the development of online business models. Before his appointment at BiTS, Andreas Moring worked for the Axel Springer publishing company as a management consultant, and at a media holding company in Germany and Europe.

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