Use cases

Marketing Automation

Many of the customers we’ve worked with are either marketplaces or have a large number of customers. When you reach more than a few dozen customers, it gets difficult to keep track of each and every single one of them and carefully address their needs.

That is a point where you need an automated way to segment your customer base to ensure that you understand what segments they are a part of and what their current needs are. Imagine that the persons that manage supplier or customer relationships have access to a clear, daily updated view of your customer or supplier base and knows what to propose to each of them. This would give them more time to focus on nurturing relationships and will increase the relevancy of the proposals.

Smart Customer Segmentation Use Case

Here's how we can help you


This is a report that buckets either your customers or your suppliers to understand who your employees should focus on and invest their time and energy. With this report, we’ve discovered that 572 of the customers used to generate frequent, high value sales but they stopped coming back! These are the perfect candidates for a reactivation campaign!

After calling them and offering a one-time discount, they succeeded in bringing back 43% of them! Moreover, by collecting feedback from the rest of them, they understood how to improve the quality of their relationships to avoid this level of churn from happening in the future.

With an analysis like this (that refreshes daily, automatically), we can provide the person that manages the relationship with either your suppliers or customers with a list of persons they should call and try to bring back! Alternatively, if you manage your reactivation campaigns with an email marketing tool, we can connect to it and add each person to that campaign as soon as they become inactive. The quicker you react, the higher the chances to not lose that customer or supplier forever!

This is just one example of how we can use an automated RFM segmentation analysis and automated decision making. We encourage our customers to put in place scenarios and address each of the remaining segments with relevant propositions. For example, in the case of the customers that are active, frequent buyers but have an average order value, we can encourage them to buy more by suggesting relevant cross-sell proposals. These proposals can be served with a recommendation engine that learns from past consumer behavior to understand what products people usually buy together. Do you think this analysis can help you? 

Book a discovery meeting and let’s talk!