TACTICAL Sales Guidance

with systematic recommendations


Let's turn your purchase history into

Actionable B2B sales alerts

Example situation

Customer loyalty tends to recede over time.

This can be prevented by a focused attention and individual approach.

Such approach should be systematic and based on accurate knowledge of each customer’s purchasing behavior.

This can be challenging for multiple salespeople responsible for many customers.

Customer at-risk situation

From your data, we can determine which customer is fine, who deserves VIP care, who suddenly became more likely to stop buying from you and much more.

We’re making it easier for you and your team. We notify each seller as soon as we notice a significant change in purchasing behavior – and suggest a suitable approach.

It is then the responsibility of everyone to follow the recommendations or only their intuition.

When you have experience, you do things right. With Insightee you will also easily do the right things.

Each salesperson gets a complete overview of their customer portfolio
with respect to specific customer segments
and important metrics that strongly influence customer loyalty.

And of course a daily updated to-do list – what to do with each customer – starting with the most and ending with the least important.

Case study
+66% Revenue from B2B sales


B2B Automotive trading company, Czech Republic

The customer is a recognized B2B car trading company with EU-wide operations. Its rapidly growing business generates valuable data that are being transformed to an undisputable competitive advantage.


Salespeople at the company used to have limited knowledge of the B2B customers they work with.  With few exceptions they mostly do remote sales calls. A rapidly changing environment and causes also frequent changes in the account management positions. Then they used to work with customers as per their best judgement, they achieved average revenue per sales call about 13.

Call center optimization


Salespeople started receiving our model-based recommendations on a daily basis. They started to work based on scored customers and recommended times to contact them.

Despite high inaccuracy of the contact list and thus contacting only every second or third recommendation – since then the average revenue per sales call changed to approx. 21.60.

The results have been so convincing that our models’ outputs were integrated to the company CRM system. That allowed the most efficient, consistent and personalized work with their B2B customers.