EDF Energy keeps eye on bad debt

Data mining system cuts potential bad debt by 60 per cent

EDF Energy has reduced customer bad debt by 60 per cent after using data mining software to analyse its customers’ financial performance.

It has also used the technology to identify 1.7 million potential clients, by evaluating its criteria for new subscribers against data on UK householders.

Clifford Budge, EDF Energy customer insight manager, says the Clementine data mining software from specialist vendor SPSS has become an essential tool for examining data.

‘There is definitely demand to have more insight into the business,’ said Budge. ‘We use Clementine to look at customer retention, product information and customer acquisition.’

The customer insight team was most recently asked to re-examine the rules governing the system, which scours individual customer records for common occurrences of bad debt.

‘With any piece of work like this, we are looking at a problem spread over five million customer records,’ said Budge.

‘When we were asked to re-evaluate these rules, the model we came up with was 60 per cent better than it was before.’

By predicting which geographical areas are most likely to have a high concentration of unpaid bills, the new rules are helping EDF to cut 60 per cent of the bad debt that would otherwise be wiped from its balance sheet. The system also makes the process of collecting outstanding payments easier.

Similar work to improve the targeting capabilities of the sales and marketing department has increased the potential sales pool by more than 25 per cent.

The Clementine software operates by trawling database records and graphically representing its findings, which are then applied to the company’s strategic planning activities.

‘The work we do is equivalent to creating 10 models a year at about £14,000 per model, where other companies can pay as much as hundreds of thousands of pounds for just one,’ said Budge.

The insight team also looks at trends appearing from its work on the day-to-day data created in running the business, which other departments may not have sufficient time or tools to notice.