Serving each customer right
Energy company Eni uses analytics to estimate customers’ long-term value on an individual basis
Gone are the days when companies simply want to collect as many customers as possible. Today, forward-looking companies deploy sophisticated data mining algorithms to determine which customers are valuable in the long run. After all, it is useful to identify customers who will be overly demanding in terms of service, who will seldom pay their invoices in a timely manner and who will hop from one supplier to the next.
We are working to build a future where all of our clients can access energy resources efficiently and sustainably.
Customer Insights Manager
Eni, an integrated energy company with activities in 69 countries on five continents around the world, is using SAS® Analytics to better understand its customers. Since entering the Belgian market in 2008, the company now offers power and gas services in all market segments: retail, SME and industrial. Today, Eni holds the No. 3 position in the Belgian energy market with more than 800,000 retail connections and 50,000 plus B2B connections.
Client behavior feeds strategic decisions
There is no question that the energy market is highly competitive, and it’s crucial for a company like Eni to build long-term relationships with its clients.
“We are working to build a future where all of our clients can access energy resources efficiently and sustainably,” says Zdravka Jevtimov, Customer Insights Manager at Eni. “My job is to monitor, analyze and understand the behavior of our entire client base, as well as each individual client. This is precisely the type of information that helps our management make the right strategic decisions.”
Predicting a customer’s long-term value
Every client is important to Eni, and the company wants to make sure it can offer each of them the best possible service. “On the one hand, we want to strengthen our relationship with customers who have a positive long-term value so that they will remain customers,” Jevtimov says. “On the other hand, we want to understand why certain customers have a low or negative estimated value, so we can do whatever’s in our means to try and turn them into profitable clients.
“We calculate how much the customer will spend with us (revenues) and how long they will stay (retention). We also predict when we might experience payment issues (credit losses) with them and how much it will cost us to serve their needs (service costs).
“This may all sound rather straightforward, but the development of a solid and trustworthy prediction model isn’t created overnight. In our case, we teamed up with Python Predictions. Together, we identified the essential components that define the long-term value, and we developed more than 700 parameters to build the predictive models. We took the time to discuss all of this extensively with every department within our company, as it was essential that we had everybody 100 percent on board.”
Countless possibilities for integrating rough data
Building robust predictive models efficiently requires a smart, powerful and flexible software solution. Wouter Buckinx, co-founder and managing partner at Python Predictions, explains how SAS came into the picture.
“We were a SAS partner right from the inception of our company in 2006, and our client Eni was a seasoned SAS user as well,” Buckinx says. “More importantly, SAS has shown that it knows how to really dig deep into data and locate all the richest veins. SAS solutions offer countless possibilities for integrating rough data, and this is equally true for building accurate formulas. We have found that the flexibility of the SAS software is beyond comparison in these areas.”
SAS Analytics not only offers Eni’s management valuable information about customers, it also helps them evaluate the future profitability of the company’s product portfolio, sales channels and customer segments. In addition, analytics enables management to run “what if” scenarios, allowing them to assess the impact of strategic decisions, such as changes in price or margin, reduced customer churn and more. These KPIs are monitored through user-friendly dashboards, so Eni is in a perfect position to make well-funded decisions that will help increase the long-term profitability of the entire customer base.
All set for the future
While Eni’s powerful SAS analysis tool brings the company all the information it needs, it’s also designed for future expansion. This is important because the amount of available data will rapidly grow in the foreseeable future.
“We intend to keep on investing in analytics to grow our business,” Jevtimov says. “In this respect, we welcome the introduction of smart energy solutions that will create new opportunities to understand and serve our clients even better. Therefore, we made sure that our SAS analytical systems and our people are armed and ready to cope with these developments and the challenges that come with them.”
- Determine the expected long-term value of each client and understand the drivers behind it.
- Better allocation of company efforts and investments.
- Strengthen customer relationships to nurture loyalty and long-term value.