SAS® helps EDF Energy understand customers, target products and build revenue

EDF Energy, the largest producer of low carbon electricity in the UK, uses SAS® to analyse its huge customer base and to evaluate the 'engagement levels' of specific customer groups with particular products, using the insights gained to drive product development, improve targeting and campaigns, build loyalty – and ultimately increase customer value.

Part of EDF Group, one of Europe's largest power companies, EDF Energy employs around 15,000 people across the UK and is the biggest supplier of electricity by volume in the country, providing gas and electricity to more than 5.5 million business and residential customers. In today's complex energy marketplace, the key players want to find new ways to engage more effectively with their customers - to increase loyalty, improve retention, cross and up-sell, and ideally pre-empt switching. EDF Energy is now using SAS® for product modelling and optimisation to help maintain a more satisfied, more engaged and more profitable customer base.

Offering consumers more informed choice – not complexity

Clifford Budge, Customer Insight Manager, B2C Energy Sourcing and Customer Supply, EDF Energy said: "What we're doing at EDF Energy is trying to provide consumers with real breadth of choice." EDF Energy offers a wide range of products incorporating fixed-price, variable-rate and capped offerings – and wants a good understanding of which types of customers are most engaged with each product set. The company can look at examples of all different products offered in the past and pinpoint which customers were offered each one, and who went on to take each product.

To achieve this, the in-house analytics team uses SAS. This includes the strategic analysis of customer data to gain actionable insight into what drives and motivates how customers interact with EDF Energy, and the products they are likely to engage with successfully. Customer relationships can then be managed more proactively and appropriately. "Our Customer Insight Team is a uniquely qualified group of specialists who use SAS to translate data into information that's essential for EDF Energy to acquire, develop and retain the right customers," Budge adds. "SAS helps us to manage customers in more effective ways by enabling us to learn how they think, what they buy, what they use, and how they want to relate. This, in turn, means we have more opportunities to build sustainable relationships with customers - and deliver better financial returns."

Why SAS?

When the Customer Insight team was tasked with finding a solution best suited to meeting these needs and those of other analytics projects including churn reduction, its search led to the SAS® Analytics suite - not least because a major requirement was the ability to analyse large data volumes, with data sets running into hundreds of millions of rows, along with the provision powerful tools to support product modelling and optimisation. The SAS® Analytics solution at EDF Energy delivers an integrated environment for predictive and descriptive modelling, along with data mining, forecasting, optimisation, simulation, experimental design and more. Within that, SAS provides techniques and processes to collect, classify, analyse and help interpret data to reveal patterns, anomalies, key variables and relationships - leading to new insights and better answers faster.

For EDF Energy, the key benefits of SAS include its ability to incorporate a wide array of modelling techniques, including logistics regression, and its ability to process large data volumes to deliver higher quality outputs. "It's all very well being able to manage two million records," Budge says. "It's when you have 400 different variables to test against that some solutions struggle – but SAS continues to perform well."

Model and optimise
                           
The first stage of the product modelling and optimisation process is ensuring the team has as much relevant data as possible. Consequently, the company buys third party attitudinal data to better understand customer attitudes, and lifestyle data for demographic information; bringing all this data together is critical to help optimise the SAS modelling toolset. The next stage is to understand which customers are most likely to take which existing products, based primarily on analyses of how each customer type has reacted to those products in the past. Typically, at the end of the process, a group of customers is 'left over' that does not easily fit into any of the existing product categories. SAS is also used for supplementary research to examine what products or areas these customers are most likely to become engaged with; these insights are sent to product development teams to inform new offers designed to meet these needs. Once products are developed and tested on relevant target groups, and the targeting model passed to the marketing team for campaigns to help drive customer uptake, the analytics team again uses SAS to track how well the product line performs when deployed.

On the product optimisation side, SAS is used to analyse all customer value and propensity to take up different products, first through customer insight models to determine customer groups most likely to leave, and then with models to assess the potential lifetime value of those groups, categorising them as gold, silver and bronze. This process helps EDF Energy to ensure it targets the right value product at the right individuals: giving the consumer what he or she wants while simultaneously ensuring the business can maximise revenues and profit.

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