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.