The churn rate in the market for water, electricity and gas suppliers is constantly – and dangerously – increasing. Calculating your churn rate is the first step in reducing the impact. Big data and prediction analysis tools make it possible. Here’s how.
“Customer churn” is about customers who decide to leave stop doing business with your company, and it’s one of the main concerns for companies in the Utility industry today.
Customer churn is a problem that has become more urgent than ever before. The Utilities and Energy sector has seen a rapid and unprecedented growth where the players on the market have multiplied with ease. For users, a few clicks are all it takes to switch from one supplier to another.
This is one reason for the general increase in the churn rate, the rate of the loss of customers, a phenomenon that has affected most any company in the sector, both in Italy and internationally. In Europe, the annual average of users who change their gas, electricity or water supplier is between 12 and 15%, a trend that is constantly growing. In the United Kingdom, according to data from British electricity and gas market regulator Ofgem who found that the annual churn rate has increased from 13% in 2015 to 18% in 2017. In New Zealand, the percentage is already around 25%.
In 2017, the percentage of Italian households that moved from traditional to new providers increased by 4.4%, and this is only in the electricity sector. Today, this number is up to 39%. For the gas sector, on the other hand, as many as 44% of domestic customers are now supplied with gas from new market suppliers; for Condominium customers, it is up to 50% (data from Arera, the Regulatory Authority for Energy Networks and Environment).
Another significant indicator is the constant increase, according to Google Trends, of searches related to changing ones’ electricity, gas, or water supplier.
This is the reality that companies in the Utilities and Energy sector have to deal with, and it’s one that you can’t ignore if you don’t want to lose market share, credibility, or revenue.
It is, moreover, a reality that fits into a broader global framework in a market that is extremely dynamic and increasingly sensitive to competition, where the life of a business is no longer that of 50 years ago, and not even that of 30, 20, or just 10 years ago.
Just think about one thing: If in the 50s the average life expectancy for companies included in the Fortune 500 list was around 75 years, today we are around 15 years. All of this crosses with data from an in-depth study by Bain & Company: Winning a new customer costs 6 to 7 times more for a company than retaining one.
It is clear, at this point, that the new challenges are all played out here: to lower the rate of customer churn, companies need to increase those of trust and loyalty.
Those involved in marketing and customer care in the Utility Industry must keep this in mind. They must be aware that the first step to keep the churn rate at bay (or, even better, to decrease it) is to keep it constantly monitored, through more advanced prediction analysis tools. Such tools are now available and are effective both for the giants of the industry and for new players.
The big data revolution
“Big data” is a buzzword that emerged from the tech industry and is now nearly entered the mainstream. As a result, it’s a term that is often used disproportionately and with little knowledge of what actually drives it.
According to Gartner, big data can be defined as:“very high volume, very high speed and/or very high variety information assets that require innovative forms of analysis and interpretation capable of improving insights, decision making and process automation.”
In short, big data is the most advanced “tool” that companies can take advantage of to get to know your target customers in the best possible way.
Knowing as much as possible about the users you are addressing is, and has always been, the best way to communicate with them effectively, to keep them loyal, and to make sure that they do not turn to competitors. In other words, the analysis of big data is fundamental – and now indispensable – for every marketing operation to increase the quality and efficiency of its customer care services, to increase customer engagement and loyalty, and to reduce the rate of customer churn.
Returning to the specifics of the Utilities and Energy sector, GTM Research has estimated that economic investments related to data analysis will grow from $700 million in 2012 to about $3 billion in 2020. We are talking about an increase of more than 400% in this segment. This is something we need to think about.
Exploiting big data for prediction analysis tools
In our daily experience, we all use the enormous amount of data that our brain has stored to make sensible decisions in the most effective way possible. We decide what time to wake up in the morning through a sort of prediction analysis tools of the time it will take us to have a shower, get dressed, have breakfast, and how long it will take us to get to work depending on traffic, for example. It’s a complex analysis, in short, which is based on our past behaviors and it will, in turn, help us make future decisions.
Today, companies can do the same with their users; they can know their characteristics, needs, and behaviours. On the basis of this data, they can predict, with a good degree of certainty, how users will act in the future. The prediction analysis tools of the churn rate fit, therefore, in this context.
Of course, it is not enough to have a huge pile of data. You have to collect it with the utmost accuracy, in an approach that is as omni-channel as possible. And not all data is the same: some data is much more meaningful to your business than others: and this varies from business to business, from brand to brand.
Finally, this data must be interpreted in the best possible way. To do so, especially when talking about big data, requires artificial intelligence and machine learning tools.
Carefully analyzing the behaviour of customers who decide to “stay,” who interact positively with your business through diverse channels, is just as important as studying the behaviour and characteristics of those who decide to “leave.”
On the basis of these analyses, and only from here, can effective strategies be put in place to significantly increase retention and reduce the churn rate.
You can understand how vital this is to the health of any business, and to its growth. This is all the more true for companies that provide essential services that have a huge and pervasive impact on the daily lives of millions, such as water, electricity, gas, the internet or telephone services.
Going beyond big data – The frontier of personalization
Having the largest amount of data, and knowing how to interpret it in an efficient and functional way is essential.
But no concrete action will be effective if, on this basis, we don’t know how to divide our audience of users into groups and clusters that combine individuals based on similar personal, geographical, and psycho-social characteristics.
This is necessary in order to segment your general target customers into many smaller and more specific groups, to be targeted in a more focused and effective way, and with actions that are as customized as possible.
For this reason, we prefer to talk about smart data (which can be defined as filtered big data, which is selected, made more significant and functional depending on the context in which they are inserted) or deep data.
For this same reason, the frontier towards which all of the most attentive and innovative companies are turning is personalization: Knowing each individual user, analyzing their characteristics and individual behavior, and addressing it with a one-to-one approach. It is the turning point of the true customer-oriented business, the direction that all utility companies are moving in.
Today this approach is made possible thanks to services such as those offered by Doxee, who has been working with industry giants such as Enel, Engie, A2A on personalization for many years.
Knowing every single user, dialoging and interacting with each one in a different way, is undoubtedly the best way to win their attention, gain their trust, and predict their possible behavior.
In short, the personalized analysis of data and behaviors is the best way to predict customer churn (and therefore the churn rate), anticipate the signals, and – above all reverse the course of customers who may be considering a competitor.
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