Updated on 09/01/2023

Predictive analytics tools in the utility industry

The Churn rate in the market of water, electricity and gas suppliers is constantly – and dangerous – increasing. The liberalisation of the market has made the market more competitive, while for users changing operator has become a breeze. On the one hand there is the proliferation of increasingly attractive offers, on the other the digital transformation within the industry has made the transition of supply much easier.

So the question is: how can this phenomenon be contained in this phase of great transformation? The first step to reducing its impact is to calculate its magnitude. Today it is possible, through big data and predictive analytics tools. That’s how!


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The increase of the Churn rate

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.

This explains the generalized increase of the Churn rate, the rate of loss of customers; a phenomenon that has invested a little all the companies in the sector, both at Italian and international level.

The percentage of Italian families that has gone from the so-called “Service to greater protection” to the “Free Market” is constantly increasing. From a report by ARERA, updated to September 2021, the following data emerge: 59.7% of Italian households chose the free market for the supply of electricity. This is a total growth of +2.4% over the last six months. Similar results also come for natural gas supplies. In this case, 62% of households have chosen the free market with an increase of +1.8% compared to previous surveys.

But this change was not limited to domestic customers. 70.4% of companies (+2.4%) chose the market for the electricity sector while 71.3% of condominiums (+1.4%) abandoned the protection for the gas sector (with consumption lower than 200 million cubic meters). In recent months, therefore, the growth recorded by the total number of customers who chose the free market has been considerable.

Well. Take all this data and consider this: a 5% increase in the customer Churn rate can lower profits from 25% to 95%, depending on the specific case. Frightening figures, focused in an in-depth study by Frederick Reicheld of Bain & Company, also taken from the Harvard Business Review. Another significant indicator is the constant increase, among Google Trends, of research related to the change of supplier of electricity, gas, water. This is the reality with which companies in the Utility and Energy sector have to reckon in the present. You can’t ignore it if you don’t want to lose market share, credibility, revenue and revenue.

It is also a reality that fits into a broader and global framework, in an international market characterized by extreme dynamism and accelerated liquidity: the industrial and productive context, in short, is certainly not that of 50 years ago, but not 30, 20, or even 10 years ago.

While the average life expectancy for Fortune 500 companies in the 1950s was 75 years, today it is 15 years. And all this is crossed with this other data, which emerges from a well-known study by Bain & Company: for a company to win a new customer costs 6 to 7 times more than to retain one.

It is clear, at this point, that the new challenges all play out here: lower the rate of customer Churn, increase the rate of loyalty and loyalty.

Those involved in marketing and customer care in the Utility Industry must keep this in mind. You should be aware that the first step in keeping the Churn rate at bay (or, even better, decreasing) is to keep it constantly monitored, through more advanced predictive analytics tools. Which are now available, and are effective for both the giants of the industry, both for new players who are appearing for the first time in this market in continuous and rapid change.


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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.

According to the most up-to-date estimates, in fact, 58% of utilities have started solutions for internal processes based on big data, machine learning, blockchain, cloud technologies and neural networks (source: zerounoweb). In addition, digital growth forecasts in the coming years are conditioned by the implementation of the PNRR, which provides for investments of about 50 billion by 2026 (source: dire.it).

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 widest amount of data, and being able to interpret them efficiently and functionally – we have seen – is fundamental and essential. But no concrete action will be effective if, on this basis, one does not know how to divide one’s audience of users into groups, in clusters that bring together individuals with the most homogeneous and coherent demographic, geographical and psycho-social characteristics. This in order to segment its general target into many smaller and specific targets, to be targeted more effectively, with actions as much as possible tailored.

This is why we prefer to talk about smart data (which can be defined as filtered big data, selected, made more meaningful and functional depending on the context in which they are inserted) or deep data.

For this same reason the frontier towards which all the most attentive and innovative companies are addressing is personalization. Know, therefore, each individual user, analyze the characteristics and individual behaviors, and turn to it with a one-to-one approach. It is the turning point of true customer-oriented business, towards which all companies in the Utility sector are addressing. Today this approach is made possible thanks to services such as those offered by Doxee, which for years has collaborated, in this direction, with giants such as Enel, Engie, A2A.

Knowing every single user, dialoguing and interacting with everyone in a different way, is undoubtedly the best method to win their attention, gain their trust, predict their possible behavior.

In short, it is the personalized analysis of data and behavior the best method to predict the customer Churn (and therefore the Churn rate), anticipate the signals; and – above all – run for cover, reversing the route.


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