Identifying Target customers for up-selling: the case of biogas

Research Highlights

- We propose a machine learning tool to identify customers that are likely to opt for a renewable energy product

- We were able to double the response rate of a mailing campaign

 

Challenge

In saturated markets, it is particularly difficult to acquire new customers. Consequently, a promising strategy is to increase the value of existing customers. We supported an up-selling campaign of an energy retailer in Switzerland in their attempt to turn customers of a conventional product to customers of a premium product. By using machine learning, we were able identify households with a high likelihood of switching from a standard gas tariff to a premium biogas tariff. A challenge in this project was that neither ground truth information about the purchase interest towards a biogas tariff, nor socioeconomic or demographic data was available for classifier training.

 

Approach

We used a semi-supervised learning approach to obtain up-selling scores for each standard gas customer as a proxy for the likelihood that this customer switches to the biogas tariff. The scores were obtained using Random Forest classification algorithms. For building the machine learning model, we first analyzed the available business data and developed empirical features in cooperation with data scientists and energy retail experts.

Results

In a pilot campaign, the utility company sent postal mailings to 1,000 gas customers, 500 of which were selected with the machine learning model and 500 who were selected randomly. Among the customers selected by the model, twice as many responded to the offer compared to the randomly selected customers.

Partners

BEN Energy AG, Switzerland

IB Aarau, Switzerland 

 

Funding

Funding: This project has been funded in parts by the European Union (EUROSTARS Grant number E!9859 - BENgine II).

Date: 2015-2016

 

Team

Konstantin Hopf, Ilya Kozlovskiy, Mariya Sodenkamp, Thorsten Staake


all projects on Machine Learning for Customer Insights

Harvesting Open Data for Household Classification    

Testing the Transferability of Household Classifiers

Identifying Target Customers for Cross Selling: The Case of Biogas


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