Additional energy retail services have long been upheld as the solution to recover falling electron revenues, but few energy companies are effectively monetising customer data to drive this change.
The advent of smart meters and the data they will produce is, however, opening up opportunities to invest in better understanding your customer, and therefore better know their preferences and needs. Still, that smart meter data is just one data set that a utility can leverage.
Just look at established retail players in other sectors, says Iain Stewart, International Practice Partner Utilities and Smart Cities at global analytics company Teradata. They have been gathering customer data for years and using it to develop more profitable relationships with customers, and develop more impactful propositions.
Teradata itself has been working with customer-based industries since the 1980s and has built up a cache of 600 predefined use cases for data analytics, of which about half are applicable to the utility retail domain, says Stewart.
He tells an Engerati audience during a webinar - Monetising data like any other retail player - learnings for energy retailers - that the company has a raft of business-outcome led scenarios prescribing the type of analytics to use as well as the monetisation achievable by running that type of analytics that can be adopted by utilities to drive transformation and become data-driven.
Utilities need not reinvent the wheel, nor risk regretting spending on data and analytics programmes that are untested.
Data-driven energy retail
Supply Chain Practice Lead John Malpass has seen retailers develop their data analytics skills. “Retailers used to have data warehouses for reporting what had happened. Now we're seeing more and more with the advent of new technologies, the ability to start predicting what is going to happen in order to become a more agile organisation.”
Becoming a data-driven retailer that understands how to monetise customer data, however, is not just about technology, says Malpass. He tells the webinar audience: “I could name two different retailers who have exactly the same technology and exactly the same data yet one is data-driven and the other isn't."
Malpass explains that the main difference between them is "a culture of analysis and having the right people in place to drive the analysis and who want to change the business”.
Transforming their business model and revenue streams is undoubtedly an aim of energy retailers and this is why Teradata became interested in the utility space. “With the arrival of smart meters, we saw mass data sets looming and decided to enter the market," says Stewart. "The next step is to move beyond that into retail analytics and the innovation within that retail sector.”
Integrating energy retail data sets
Stewart advises that energy retailers begin by combining data from smart meters, CRM systems, web and call logs, social channels, as well as data from price comparison sites and billing and "any other number of external data sets".
By integrating data and creating a central view, says Stewart, “anybody internal or external can run their own analytics and use it in any way they see fit.”
Product and service bundling is a good example of how utilities can monetise integrated data sets “and we’re seeing good progress to date in areas such as energy usage reduction" says Stewart.
“We have done a lot of work in the US helping utilities monetise data sets to reduce overall consumption through various engagement programmes that generate revenues by way of regulatory kickbacks.."
Single view of data
Stewart cautions however that a lot of these use cases examples are only possible if you integrate multiple data sets in a way that is reusable.
He says: "Within a lot of utilities, you'll still see marketing departments working on their own view of the data, and it'll be the same with billing departments, and elsewhere."
Even non-utility retailers struggle with having a single version of the truth.
Malpass recalls presenting at a supply chain conference where “virtually everyone in the audience was still using Excel spreadsheets to analyse data. “Part of the problem is analytics supported by Excel are one-off types of analytics,” he says. “Within six months, they are not enabled to drive sustainable change. In order to drive long term sustainable benefits and enable transformational change, analytics have to be integrated into the business processes.”