Predictive Analytics and the Utility Industry

Published: 26/02/16

Winning consumers today, means predicting their needs tomorrow...

In today’s competitive world, winning over customers means predicting their future needs - and fulfilling them before they even have to ask! That’s a tall order. But the world’s most innovative companies manage to do this through the use of predictive analytics.

What is predictive analytics?

Predictive analytics is the use of data analysis, statistical algorithms and pattern recognition techniques to identify the likelihood of future outcomes based on historical data.

Its goal is to go beyond descriptive statistics and reporting on what has happened, to providing an assessment on what will happen in the future.

The end results can help refine and improve decision making and produce new insights that lead to better actions for both businesses and consumers.

Ever wondered how Amazon seems to know what you want, even before you do? The power of predictive analytics!

Predictive analytics and utilities

The opportunity for the utility industry is huge and must not be overlooked.

Utilities have access to comprehensive and detailed customer usage data, as well as traditional data sources like call centre and customer interaction histories. Combined with readily available postal and demographic data, an extremely sophisticated and complete understanding of customers starts to take shape.

With the right technology, utilities can integrate these data streams to form an accurate and informative profile of every home and business they serve.

Customer satisfaction

In the same way the likes of Amazon and Netflix couple their insights with personalised customer experiences, utilities can bring their data analytics to life.

By interpreting past behaviour and predicting future behaviour, they can provide a truly personalised service. Analytics insights can be used to automatically deliver timely, interactive and relevant communications - information, advice and product promotion - that generate consumer satisfaction and drive business goals.

Both utilities and customer stand to benefit from bringing this analytics data to life. Here’s how.

1. Improving supply

Predictive analytics can help utilities to provide a more consistent, stable supply. Analysing live network data can help with outage predictions, system failure predictions, accurate load forecasting for balancing supply and demand, optimising demand response programs (more below), and detecting early warnings of irregularities.

2. Optimise demand response programs

By taking multiple readings per day, smart meters give utilities a forensic knowledge of customers’ energy habits. Consequently new, more focussed segmentation is possible, such as targeting homes whose usage peaks during heavy load periods.

For example, family homes whose usage peaks in the late afternoon when they get home from the school run can be provided with specific ideas and personalised incentives for reducing energy use and costs during these peak hours.

The utility company benefits from a better balance of supply and demand, while customers are able to save significantly on their bills. 

3. Stop high bills and reduce costs to serve

Sending out high energy bills to only to be inundated with disgruntled calls querying them isn’t a great strategy! Instead, utility companies can use smart meter data to flag high energy use weeks before the customer is hit with a bill. 

Using this real time data, utility companies can deliver personalised alerts to notify the customer of these potentially high bills before they receive them. Providing insight into their energy usage and advice on reducing it allows people to make informed choices.

Companies that have adopted such approaches in the past have been able to cut call volumes and customer churn by as much as 10 percent*.

4. Team up with new movers

Research shows that people are more open to trying new products, services and tariffs when they move into a new home. If utilities engage with customers at that key moment and are able to establish themselves as trusted advisors from day one, they are likely to make those households more valuable and less likely to switch.

Combining historical data from the property with the movers’ customer profile information can provide invaluable guidance about energy use. Sharing such personalised, timely reports with new movers makes a hugely positive first impression, establishing good habits in the household from the start. 

Conclusion

Personalised experiences are the new norm. Whilst other industries have raised the bar on customer service, utilities have been slower to adopt such an approach.

However, with reams of meter, call centre and customer data at their fingertips, utilities have the tools and ability to deliver timely, interactive and relevant communications that satisfy consumers and drive their business objectives.

 

*Opower: Insights for the Innovative Utility

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