CRM in Practice: Powergenby
The utilities sector has undergone enormous changes over the past decade, with deregulation leading to far greater competition and an ever tougher fight to hold on to existing customers – let alone acquire new ones.
This has led, in the case of some providers, to an almost inexcusable reliance on cold call telemarketing to try and win over new households. Powergen, one of Britain's leading energy suppliers, and part of E.ON, the world’s largest investor-owned utility, is not such a company.
Powergen operates strict rules on the frequency of mailings. It also has a commitment not to send marketing material inappropriate to customers' needs. All of this is fine in theory, but it makes precise targeting not just a priority but an absolute essential.
The utilities sector is not like other competitive markets with high churn rates. Unlike financial services or mobile phones where customer contact is frequent and as a result customer knowledge is high, the energy sector relies on occasional contact and a largely legacy database of customer information.
Unlike others in the industry, Powergen has stayed highly focused on the supply of energy, resisting many of the ancillary add-on services offered by its competitors, hence the company strap line 'positive energy'. The Powergen brand is part of the retail arm of E.ON UK and has some 8.5 million electricity and gas customer accounts.
Powergen has adopted business analytics technology from KXEN to fine tune the way it targets customers and prospects with the most appropriate energy packages for their needs. The company is keen to maintain market place differentiation and it sees accurately targeted marketing as way of demonstrating to customers that it understands their needs better than the competition.
"The market dynamics are interesting," says Powergen's head of customer relationship management, Mark Perrett. "Where once the competition was very aggressive with a constant race to acquire customers, now it is maturing and causing all the suppliers to rethink their tactics. We're in a market where it's difficult to get competitive intelligence which makes it all the more important to understand and use customer data. And that really highlights the importance of modelling.
"It’s all about not antagonising the customers and prospects. We have a pre-determined contact frequency period, something like 70 days between each outbound contact. It’s not like the early days of deregulation when everybody scrambled around for every possible prospect. There are a few exceptions such as changes to pricing that need to be communicated.
"The first time we used a KXEN generated model to support campaign activity we saw a 20 per cent uplift in sales and a direct £150,000 saving in mailing costs. Those figures represent an excellent and speedy return on a software investment. More importantly we also retained 300,000 customer contact opportunities for future campaigns, having been told by KXEN that a successful sale was unlikely to result this time."
Although applications vendors have long targeted the utilities sector as a lucrative cash cow, Powergen has no packaged CRM solution. "We have got a customer facing system which is bespoke," says Perrett. "It’s been developed over a long time- in many respects it was before its time in terms of functionality."
Many of the packaged applications vendors argue that the embedded analytics functionality in their offerings is sufficient to meet the needs of most customers. Perrett however still sees the value of third party specialist technology. "One of the things that we’ve over the past couple of years is to build a model that can support our marketing campaigns," he notes. "Having a specialist tool is much better for our needs."
Measuring the ROI on its investment is of high importance to Powergen. "We will carry on on a case by case basis," says Perrett. "There are different ways of measuring the return. For example, we can cut down the numbers of contacts that you need to make to get the same level of response. If you’re tackling a population where the response is likely to be high, you can also tackle whether the value is likely to be high."
New models are built in co-operation with the business side of the organisation. "It’s a test and learn process," explains Perrett. "We ask whether it is worth building models. In theory, we can build anything, but some things are clearly going to be of less use. We need to look at the propensity of customers to buy particular things. We work with the businesss side and deploy test models."
The first campaign was for a dual-fuel deal where customers who opt to buy both electricity and gas from Powergen qualified for even lower prices. The company built a propensity model using KXEN's software.
From what this model told the marketing team they were able to exclude 70 percent of the initial prospect list, targeting only the remainder based on their higher propensity to buy. The subsequent response and conversion rate was ample proof that the model was robust in its predictions and provided a realistic, real-world view.
Traditional modelling demands the formation of a large team of highly skilled, highly paid analysts to generate effective results, but this is not the case at Powergen. "Our experiences are based on a team of only three or four analysts," explains Perrett. "A traditional modelling tool would need a much bigger team than that. I've heard of organisations where it's commonplace to have many tens of analysts but here we've proved that you can build good marketing propensity models with only three or four people."
Traditional modelling can also take months to build, but in a fast moving, competitive sector this is not viable. "With KXEN it takes minutes to build a model and it can be applied to several million database records in a couple of hours whereas building models in other solutions and getting the data formatted to suit can take weeks," says Perrett. "It gives you a choice of outputs and it writes code for you that you can apply straight away to your database."
KXEN exploits the customer knowledge held in Powergen's Oracle customer database. "It allows us to look at the data we have got without any preconceptions," explains Perrett. "The problem is when you have a lot of data it's easy to jump to conclusions but we’d be guessing if we just made simple predictions. We can still go with gut feel but now it's possible to validate that intuition with hard data."
A case in point was when a particular energy proposition had been targeted to potential customers without any real understanding of what types of customer had previously purchased the proposition. "The model we built actually revealed that the number of people in the household was a significant driver," recalls Perrett. "The proposition would have most appeal for families and we'd effectively be wasting our time offering it to singles. The results provided some very valuable insight into ways in which we could change our approach to marketing this product - for example, by using marketing material that was more relevant to a family audience than single people."