Churn Forecasting HLS


HLS is a major independent distributor of beverages and catering products in Belgium. It supplies around 1,800 products (such as beer, wine, spirits, soft drinks, water, coffee and dry products) to over 3,000 customers in the catering industry all over Belgium.

The problem

The sales team could not understand why customers would stop buying white beer and churn after six months when they had purchased white beer several times per month in the past. Calculating the churn rate involves formulas and interpretation and HLS struggled to do this using traditional spreadsheets and business intelligence methods.

HLS contacted ML2Grow to create a custom analysis, development and visualisation platform for customer churn which could become an integral part of their sales team.

With Machine Learning techniques, ML2Grow is able to find hidden relationships between customers and their behaviour and create risk profiles using limited data, such as a list of transactions
—  Ruben Delaet, Data Engineer ML2Grow


The START AI initiative aims to support companies in their intention to explore AI applications by valorising their data.

If your company is eligible, ML2Grow will guide your company for three days to explore the possibilities of AI/data to identify the most relevant applications for your organisation.

Together with our team:

  • Evaluate the value of the available company data (internal & external)
  • Identify if there are relevant AI/data projects within your business context
  • Determine which stakeholders and experts to involve in your potential projects
  • Get tips & tricks with which you can better estimate the added value of your possible AI projects
  • Map the next steps in your AI journey

ML2Grow’s services will cover three days and include preparation, analysis and reporting, and participation in meetings (onsite or remote). These services will be provided over a maximum of 4 months (between the beginning of June and the end of September 2022).

The intervention of ML2Grow could lead to the conclusion that AI applications are not relevant for the participating company. In this case, the guidance will mainly focus on coherent data management to promote potential data projects.

Who can participate?

Any SME (max. 250 employees), organisation or association, starting or established, regardless of its digital maturity or sector.


The cost for participation is €1625 excl. VAT

This corresponds to 30% of the fair value of the guidance.

Are you interested in participating?

Contact our team before the 15th of May if you are interested in this offer.

We will happily answer all of your questions and support the application procedure.



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Our solution

We used Machine Learning to recognise patterns in customers’ purchasing behaviour. We analysed huge volumes of data and identified the characteristics shared by different types of customer behaviour. This enabled us to identify the kinds of customer actions that led to churn. Sometimes these actions were very subtle, or difficult to measure, such as when customers purchased smaller volumes of certain products or left longer intervals between purchases.

This approach is beneficial for companies as it enables them to take action with that information. For instance, a company could choose to actively reach out to a customer and find out why that customer is about to change his or her purchasing behaviour (before they actually do this).

ML2Grow solved this problem by designing an algorithm that detects changes in the buying behaviour of every single customer by looking at their past purchases. The system produces a brief and readable Excel report for each sales manager so that sales teams can take the initiative to contact their customers and understand what is happening.

There are advantages to being able to predict customer churn. It helps you stay focused and avoid losing customer acquisition outlays. ML2Grow has extensive experience in the retail and wholesale sectors. In particular, it creates recommender systems (which recommend specific products to customers or recommend customers who are open to a particular product) and churn prediction systems (which identify customers with the highest risk of churn).

Although we cannot reveal too much about the secret sauce in our state-of-the-art solution, our approach uses churn cutoff calculations together with a classification algorithm. We combine several algorithms which creates more accurate results than current models available.

How we added value

  • HLS reduced its customer churn by around 10% annually
  • Insights gained from customer churn were seamlessly integrated into existing sales follow-up workflows
  • Weekly reports were produced showing customers with a risk of churn so that the sales team could take action
We see how the general churn prediction algorithms used in data platforms can do more harm than good. This is because they are generic by design and do not take into account the unique relationship you have with your customers.
—  Greg Scheirlinckx, Data Scientist ML2Grow


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