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 kunnen clusteranalyse gebruiken om bestaande kopers in verschillende sets of “clusters” te groeperen. Zodra sales managers klanten in groepen hebben gerangschikt, is het mogelijk om trends in elke groep vergelijken en op zoek gaan naar meer verkooppotentieel. Lastig om daar aan te beginnen, snappen we. Daarom hebben we met Trendskout een slimmere, eenvoudigere toe te passen tool gebouwd.

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 en zo nieuwe zakelijke kansen ontdekken en de kans op cross-selling vergroten. Daarnaast is het mogelijk om prijsinconsistenties bij klanten op te sporen. De AI-toepassingen ingebouwd in ERP-systemen zijn echter erg primair. Van heel wat van die bedrijven is het immers geen core business. Ze bouwen oppervlakkige modules in, maar het kan beter. Veel beter.

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 kunnen predictive analyse modellen toepassen door gebruik te maken van klantreacties en vervolgens passende acties ondernemen. Dit biedt niet alleen voordelen op het gebied van voorraadbeheer, maar kent ook toepassingen en kansen voor klantenloyaliteit, churn rate enzovoort.

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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