Is your sales data locked in all kinds of systems? Do not despair!

With predictive analytics, having lots of data becomes a great opportunity for B2B sales managers. However, to get the most out of it, it is essential to have a good understanding of the sales situation. Knowledge of mining models is not necessary. You can leave that to smart tools. Let these 3 examples inspire you.

Sales data does not necessarily have to be structured

1. Performing B2B marketing segmentation with a cluster method

Successful market segmentation is the key to matching your company's services and products with the needs and demands of the market. There are several methods - with enormous potential - to make segmentation much more efficient for a sales manager.

A clustering method groups customers based on a common thread. Since that common thread is often the sales history, successfully conducting a cluster analysis requires no better starting point than a company's ERP sales data.

Sales managers can use cluster analysis to group existing buyers into different sets or "clusters". Once sales managers have ranked customers in groups, it is possible to compare trends in each group and look for more sales potential. Tricky to get started, we understand. That's why we built a smarter, easier-to-use tool with Trendskout.

2. Using Apriori algorithm to develop a cross-selling strategy

Most popular ERP systems use transaction databases. This allows Apriori algorithms to easily provide valuable sales insights. For instance, the algorithm can detect associations. This is already widely used in B2C. For example, if several customers have bought products A and B together, the algorithm clusters them into a set. Sales managers can then compare these ranges and thus discover new business opportunities and increase the chances of cross-selling. In addition, it is possible to detect price inconsistencies among customers. The AI applications built into ERP systems are, however, very primary. After all, it is not a core business of many of these companies. They build in superficial modules, but they can do better. Much better.

3. Implementing the customer behaviour model for sales forecasting

Data-rich analysis should drive the right sales action at the right time to the right customer. Based on customer behaviour it is possible to improve sales forecasts. Sales managers can apply predictive analytics models using customer responses and then take appropriate action. This not only offers benefits in terms of inventory management, but also has applications and opportunities for customer loyalty, churn rate and so on.

We can do a lot with your sales data, let us prove it

Sales data is our raw material. Whether it is centralised or not. With our platform, which does not require a data scientist, we can get to work immediately. The platform will iterate between different models to deliver a workable outcome at lightning speed. What you and your team can work with.

So before you take even one further step, please book a demo. A personal demo that we can base on your real company data. Let us discuss it.

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