Customer Segmentation at an Energy Company | Starschema
Gradient starschema cut 02

Customer Segmentation at an Energy Company

Turning transaction data into customer insights

Practice Area

  • Data Science
  • AI

Business Impact

  • Six-fold increase in targeting model effectiveness
  • 40% increase in conversions


  • High cost sales channel
  • Low conversion rates
  • Better target potential customers to reduce acquisition costs


Python, Tensorflow


Our client, a retail gas and electricity provider, operates a call center to facilitate customer requests and to offer complementary services, such as equipment maintenance. To boost sales of these services, the company conducted telesales campaigns.

While a logical channel to reach customers, offering these services through the contact center resulted in lower than expected conversion rates. The client reached out to Starschema for help segmenting its customer base to improve.

Introducing a segment view driven by advanced machine learning, our client was able to improve conversions and lower customer acquisition costs.


Call centers are an effective but costly engagement channel compared to online alternatives, such as email and e-commerce sites. In our client’s case, government regulations restricted the times and number of calls representatives can make, further increasing the opportunity cost of each unsuccessful conversion attempt.

Effectively targeting consumers that are more likely to purchase complementary services can lower customer acquisition costs and increase value added sales volumes. The sales and marketing leaders at our client found that customer acquisition costs were high in relation to lifetime customer value, especially for their value-added service offerings.

Our client used a simple model to segment its customer base for telesales campaigns, but their success rate was only 15%, which was not profitable. They reached out to Starschema to help turn their existing data into customer insights that enabled them to focus on the right customers and contact them with the right offer.


Our data science team worked with the client to identify what existing data they could provide that would be useful in developing a more sophisticated segmentation model. Our client was able to provide our team with:

  • Customer demographics including age, gender, location and mean monthly consumption
  • Customer transaction data consisting of payment and billing characteristics, transaction history and contact-center history (e-mail, phone, personal communications)
  • Past campaign results showing whether the client has been approached for previous campaigns, and the outcome

During the data preparation phase our team tested the data to extract the most meaningful variables for customers. These variables included total consumption, time since online registration, number of interactions with customer support, and more. Armed with the relevant data, our team built a sophisticated model incorporating tree-based methods, specifically a random forest classification algorithm to determine which factors drove conversion success.

The model segmented customers who responded positively to calls from those that responded negatively. Importantly, not only did the model have a very strong predictive potential but was also capable of explaining why an individual customer would be allocated to a particular likelihood of conversion category.


With the segmentation model designed, our data scientists validated it in simulations based on prior campaign data. The results produced a valuable insight that our client had two customer segments, in other words a bi-modal distribution, who would likely purchase complementary services when approached by the contact center.

The new segmentation model yielded a six-fold increase in the number of customers purchasing complementary services during simulations. When the sales and marketing team at the customer implemented the model, it helped the client increase its campaign success rate by 40%, significantly reducing customer acquisition costs and increasing profitability.

Demand Forecasting with Latent Matrix Factorization

For many businesses, demand planning is essential. Profitability, cash flow, and customer satisfaction and retention all hinge on getting this right. This white paper will introduce latent matrix factorization to model demand curves and discuss how it can be used to achieve these outcomes.

Introducing the Stack of the Future for Modern Data Leaders

Fast unobstructed access to data and time to insight matters more now than ever. In these quickly changing times, businesses must innovate and implement a ‘Stack of the Future’ to be able to make accurate, data-driven decisions in minutes, not hours or days. The potential value of data is well known but in the new environment, the ability to easily share and collaborate on data is a competitive differentiator that will be leveraged by forward-thinking companies

COVID–19 Data Set Modeling and Analytics

During times of crisis, companies must look at the available data — both internal and external— and try to understand how that data can be used to determine how the business is currently being impacted, how it is likely to be affected in the future, what are most likely scenarios that will play out, what can be done to counter those scenarios and take advantage of hidden opportunities in this rapidly changing environment. The Starschema COVID-19 dataset ingests reliable data from multiple sources and makes it analytics-ready so it can be easily accessed and used.

A DataOps Journey

Keeping your data platforms running with operational efficiency is both paramount and can be a costly and complicated endeavor. Join us and learn how to apply strategies, techniques, and tools to build a reliable and effective DataOps practice in your organization.


This website uses cookies

To provide you with the best possible experience on our website, we may use cookies, as described here. By clicking accept, closing this banner, or continuing to browse our websites, you consent to the use of such cookies.

I agree