The Challenge
A business energy supplier serving SMEs across the UK sold both electricity and gas but held the large majority of its customers on single-product contracts. Cross-selling happened opportunistically, usually when a customer called for an unrelated query, as the account team had no way of knowing which customers were genuine gas prospects.
Broad cross-sell campaigns had underwhelmed with blanket email pushes and untargeted call blitzes annoyed customers with no interest in switching their gas supply, while the customers who might have said yes were buried in the same undifferentiated list.
The brief was to identify which existing electricity customers were most likely to add a gas contract, allowing the account team to focus conversations on customers ready to switch.
The Solution
We began our engagement with an audit of the client’s customer data, linking contract history, consumption patterns, billing behaviour, and past interactions at company level alongside the profile of existing dual-fuel accounts.
A propensity model was then trained on this dual-fuel history to score every single-product customer on their likelihood of adding a gas contract. Contract renewal timing proved the strongest signal, with customers approaching their electricity renewal far more open to consolidating both fuels, followed by business size and billing engagement.
The scores fed directly into the client’s CRM, where they reshaped the account team’s contact plan: high-scoring customers were approached in the weeks before their electricity renewal with a combined dual-fuel offer, while low-scoring customers were removed from cross-sell activity altogether.
The Impact
In the first year after deployment, the number of multi-product customers grew by 28%, with the dual-fuel offer at renewal converting at nearly three times the rate of the old blanket campaigns.
Average revenue per customer rose by 19% across the targeted group, and dual-fuel customers showed early signs of renewing at higher rates than their single-product peers, compounding the value of each conversion.
Removing low-scoring customers from cross-sell activity paid its own dividend, with complaint volumes from cross-sell contact falling sharply and the account team reporting warmer conversations across the board.
Key Takeaways
- Cross-Sell Propensity Modelling – Scored every single-product customer on their likelihood of adding a gas contract to eliminate untargeted call blitzes.
- Timing as the Strongest Signal – Pinpointed the electricity renewal window as the optimal time to present a consolidation offer, yielding three times the conversion rate.
- Focused Contact Plan – Pinpointed the electricity renewal window as the optimal time to present a consolidation offer, yielding three times the conversion rate.
- Business Results – Multi-product customers grew by 28%, revenue per customer rose by 19%, and cross-sell complaints dropped sharply.
Tools and Techniques
- Propensity modelling
- Exploratory data analysis – profiling customer billing behaviour, contract history, and dual-fuel benchmarks
- Logistic regression classification – executing statistical modeling to score product adoption likelihood
- SQL – data extraction, transformation, and multi-source data linking
- R – data handling and predictive model training
- Microsoft Dynamics – operational CRM integration and outreach activation
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