Personalisation Powers Energy & Utilities Marketing

Geopolitical global turmoil is at its worst level for decades, much of it has driven the European Energy Crisis, where consumer prices are skyrocketing higher and faster than ever before.

When thinking about the world of Energy and Utilities marketing, you are unlikely to associate it with any particularly aggressive multi-wave, multi-channel customer journey management. Don’t get me wrong, there’s is actually plenty of innovation taking place, and some particularly talented marketers out there are pulling off some very clever things.

Personalisation in Energy & Utilities Campaigns

In the old days, the complexity of a Utilities campaign might often have been as uncomplicated as personalisation based on a simple triumvirate of segments:

1) Do you have gas?

2) Do you have electricity?

3) Do you have gas and electricity?

Nowadays, with deregulation, and with it the proliferation of rates, schemes and providers the landscape is very different, even before you bring in the price comparison websites disrupting the marketplace. And therein lies the problem when you compare the current landscape with the current ‘climate’, we have what we might knowingly call a perfect storm.

Taking steps to avoid customer churn

Why do consumers change their energy plan? Well, historically, the primary reason, was because the consumer was moving house. Like the retail banks, customer relationships could be measured in decades, providing nothing upset the apple cart. Now, with everything going on in the world having a tangible, biting impact on consumer wealth, a vast segment of people who would never have considered moving suppliers historically, are eyeing up providers, rates and plans like never before.

Utilities providers wishing to maintain, if not increase their share of the marketplace are going to have to start thinking outside the box. Whereas once a little personalisation (think ‘Dear Timothy’) went a long way in terms of client satisfaction, nowadays, cutting edge Machine Learning (ML) coupled with real-time data mapping of household energy usage against affluence models will be needed to make the difference.

More and more customers will end up moving into the retention / risk of churn groupings, and clever solutions will be needed if those customers are to be actually retained once they start looking around for competitive deals to bring down their household costs. Compelling solutions to customers requirements, will need to be timed to trigger before they make the decision to switch providers, as without careful monitoring, by the time you become aware of a customer’s intention, it can be too late to do anything about it.