Competition across European retail energy markets is so intense that knowing your customers more intimately than your competitors can be advantageous. Today, utilities that use data analytics will be capable of forming stronger relationships with customers that enable them to gain market share from their competitors and extract more value from their customers.
This is because these tools enable utilities to truly understand who their customers are and what they really need. The end result, if used effectively, enables utilities to develop compelling and differentiated offers, alone or in collaboration with partners.
Of course, gaining this knowledge is easier said than done. So how can utilities get closer to customers and provide offers that will improve retention and increase acquisition rates and customer value?
1) Are you truly listening?
There are plenty of opportunities to acquire and retain customers, win back former ones, and cross-sell new products and services, especially for energy providers who are serious about taking a consumer-centric approach.
The key lies in listening: what does existing customer data-analysis say compared with sourced-in data? By using information effectively, utilities can challenge the market status-quo and improve their competitive position.
Moreover, by using data and analytics, utilities can target consumers likely to respond to offers, segment a targeted population into like-minded groups living in homes with similar energy requirements for tailored messaging, and build trust through personalised offers and billing. A lot of people underestimate how much value is in data related to the physical characteristics of the home.
2) Are you acquiring and converting?
After you have listened and analysed data, you are usually in a good position to develop a customer acquisition and conversion strategy. While this process can sound daunting, it doesn’t have to be. Here are a few steps that can be taken.
Micro-targeting is often used in political campaigning to reach voters. A similar approach enables utilities to identify people with the highest propensities to respond to specific product or service offers. Doing this maximises and streamlines marketing efforts, resulting in increased response rates at lower costs.
Score individual consumers on the chances they will take action in response to marketing efforts. This depends on thorough analysis of demographics, energy usage, and contextual data. It typically considers whether customers are new or longtime homeowners; the year their home was built; how they heat it; the impact weather has on the home; whether they work from home; have they switched service providers recently; green energy preferences, and if they have previously responded to similar offers?
By combining this information with predictive analytics, utilities can narrow the pool of consumers designated for outreach and lower the cost of a campaign.
Segmenting messaging is critical. Narrowing audiences greatly increases the likelihood of acquiring new customers; and propensity scoring has a three-fold increase in customer sign-ups over traditional untargeted marketing efforts. But including segmented messaging, combined with an individual score or personal savings estimate, further increases the chances of customer acquisition, interest and engagement by around five percent.
3) Avoid off-the-shelf messaging
US utility companies often use off-the-shelf, lifestyle segmentation models to divide customers into similar broad groups. Those models don’t correlate well with energy consumer behavior. So some service providers have to do a bit more on-the-ground work.
They conduct primary research to collect data; and develop energy-centric segmentation models and messaging schemes customised to their offering and geographic territory, which resonate with their customer base.
Personalised recommendations, consumption information, individualised cross-sell offers, product bundling, proactive high bill alerts, weekly challenges employing gamification and context-driven notifications, as well as thermostat optimisation are a few methods energy providers use to build trust and customer loyalty post-acquisition. Whether its about comfort or cost savings, contextualisation is critical.
4) Energy simulation models
Home energy simulation models that are physics based (rather than regression based) are more powerful and effective for personalising energy products and services. Furthermore, in Europe, where meters are often read once per year, they’re not just the best option, they’re the only option.
Unlike statistical models (the alternative approach), physics-based models do not require historical consumption data. They can run without meter data and use the information available about the physical characteristics of the home and local weather data.
Today utility companies can no longer depend on traditional mass market marketing approaches with customers. Data is driving a whole new array of marketing, offers and ways of forming relationships with customers. Those utilities that realise this, and use their data to drive their product, marketing, offers and customers’ relationships will be the ones that succeed in competitive markets and achieve sustainable growth.