The world of sales analytics (advanced analytics systems) is populated by vast and well-constructed technology. However, companies are not taking full advantage of this opportunity.

According to Gartner's research entitled the future of sales analytics, 53% of organizations surveyed attribute the inferior quality of sales information to inaccurate and incomplete data. In addition, only half of the sample set up a formal data governance body. Without such accurate data, it’s difficult to build sales analytics models to drive effective cross-selling and up-selling campaigns. 

A technology that provides reports and dashboards downstream is not enough. Without the upstream, it does not collect and harmonize work capable of extrapolating insights for the bulk of Big Data thanks to artificial intelligence (AI). Here are five tips to make the most out of your sales analytics:

1. Understand the potential of sales analytics 

The correct use of this technology does not mean that a Sales & Marketing director becomes a savvy tech wizard or a data scientist. But that they should understand the potential it has. From a more accurate understanding of customer behavior, one can find strategies for selling related products through cross-selling or products with added value, i.e., up-selling.

2. The separation between Machine and Human

The fact that the Sales & Marketing Director is not an expert in Big Data and AI coincides with the need that they must be helped to correctly interpret the suggestions that the system autonomously proposes to them. Proposals that would eventually drive business decisions that are not meant to be delegated to the AI. Even though the technology at hand does give deep insights into what decisions must be made in the future, the decision is ultimately left to the decision-maker.

3.  Statistical models in sales analytics 

Sales analytics are based on statistical engines that give you access to a "data ecosystem,” but this does not coincide with miraculous discoveries of universal and perfect statistical models. There are several simulations at a specific product, retail channel, and consumer cluster level. An artificial intelligence system captures fluctuations in demand to create statistical models. 

4. Demand planning e-business discovery 

Demand planning architectures allow you to anticipate market needs and support in defining reactive responses in line with increasingly sudden transformations. Unlike traditional business intelligence that adopts deterministic parameters (as one-factor changes, all the others change), sales analytics associated with modern demand systems planning results from a business discovery capacity that, in navigating the data, identifies much information to be considered relevant. 

5. The role of AI in sales analytics 

Talking about AI today is no longer science fiction. From machine learning, which continuously learns from the data that is analyzed, to recommendation engines that offer customers purchases in up-selling or cross-selling mode, the universe of artificial intelligence enriches sales analytics with information that comes not only from business systems but from heterogeneous sources, external to the organization.  Until you could recommend market niches that, for example, were not yet being explored or that had been neglected. 

These five tips risk having no impact if, together with technology, they are not accompanied by a new cultural approach. The sales analytics that can be obtained with refined statistical models and latest-generation platforms need managers ready to know how to read them and make them become the lever for the company's business.