Case study: Jaeger uses data mining to reduce losses from crime and waste

Leg of lamb is the most stolen item at Iceland. Thieves also like cheese, bacon and coffee. With the UK in recession, shoplifters appear to be switching...

Leg of lamb is the most stolen item at Iceland. Thieves also like cheese, bacon and coffee. With the UK in recession, shoplifters appear to be switching their sights from alcohol, electric toothbrushes and perfume to food. Tesco, Marks & Spencer and Iceland have all reported an increase in shoplifting since the economy began...

to contract in the second quarter of 2008. Tesco alone caught some 43,000 would-be thieves in the first half of 2008, up 36% from the same period in 2007.

The impact of the recession on retailers is yet to be reflected in any of the major surveys of shoplifting. The Centre for Retail Research's Retail Theft Barometer only has figures up to the end of 2007. Those figures show that shrinkage - losses from crime and waste - cost retailers 1.3% of sales in 2007, down from 1.34% in 2006. Even though 2007 was the peak of the boom, the losses were still huge. Customers stole some £1.6bn and employees another £1.3bn. Suppliers took £209m fraudulently. Some £73m was lost through card fraud and another £39m through robberies or burglaries. Retailers lost £666m through waste. The systems and security guards intended to reduce losses cost £785m. The total bill was £4.6bn.

Retailers have used a variety of technologies to reduce their losses. Closed-circuit television (CCTV) and electronic article surveillance (EAS) - the tags attached to individual items - are all visible in stores. Some retailers, however, are using a different type of technology to reduce losses: data mining.

During the summer of 2008, British clothing chain Jaeger went live with a data mining application in an attempt to identify where it was losing money. The application, which is located centrally, interrogates data held on different systems throughout the business, including both the head office and the company's 90-plus stores and concessions.

In common with every other data mining application, Jaeger's LossManager system, from IDM Software, uses a feed from the company's electronic point of sale (Epos) system to spot potential fraud such as excessive discounting by a single member of staff.

"It's a centralised system, but every single store is feeding into it. We have got time and attendance feeding into it as well," says Steve Hearn, head of safety and security at Jaeger.

Jaeger had used a data mining application from SFR, a small British supplier, before it took the decision to implement the IDM system.

"We had SFR Storescan, but it had long since been defunct and we were not using it because our till architecture had changed. I started to wonder if that was the right piece of software for us. It was quite cumbersome," says Hearn.

Jaeger does not disclose its net profits because it is privately owned. However, it is a mid-sized clothing retailer lacking the colossal IT budgets available to, say, Tesco or Sainsbury's. Hearn's first choice supplier was too expensive for his budget.

"I would have gone with an IntelliQ product [another British software supplier], but for the price," he says. IDM Software is a start-up aimed at mid-sized retailers and Jaeger was its first retail customer.

Unlike CCTV and EAS, which are designed to catch thieving customers, data mining applications are supposed to catch thieving employees. "We do an awful lot of work on internal fraud," says Hearn.

"The data mining system was put in to separate the usual from the unusual," he adds.

Jaeger set up an audit team when the system went live in June. The team's job is to use the new application to identify losses wherever they occur - from dishonest employees to working practices that waste stock.

Like other data mining applications, LossManager generates exception reports. However, it would be misleading to rely solely on these reports, according to IDM's chief executive officer Khuram Kirmani.

"It's very easy to get swamped with false positives," Kirmani says.

The employees responsible for loss prevention (in Jaeger's case, the audit team) use their data mining application to generate exception reports as usual. Then they continue to use the application to ask more questions of the data so that they can understand whether the system is reporting a false positive or a genuine loss.

"Each question is based on the answer to the previous question," says David Snocken, IDM's commercial director.

Any project's success is limited by the user's willingness to extract as much value as possible.

"It depends on the amount of effort the retailer has put in," says Kirmani. IDM says its system has reduced losses as a percentage of sales below Global Retail Theft Barometer's 1.3% average for UK retailers.

Although Jaeger has only had the system since June, it already expects a return on investment in its first financial year. Hearn says, "Data mining is widely accepted as having one of the fastest returns on investment of any technology. We are still in the early days in terms of assessing the benefits, but we are almost double-counting our results to check they are right."

One of the earliest discoveries was that theft by employees was only a small part of total losses at Jaeger.

"We have not gone out en masse and started arresting staff members for fraud, but we have identified considerable numbers of erroneous transactions. That is not to say that they are all fraud," explains Hearn.

Data mining is helping the clothing retailer to manage its stock, thereby reducing the need for markdowns when items go out of season and reducing the number of items that go missing altogether.

In a recession that has already claimed the scalps of established retailers such as Woolworths and MFI, any initiative that helps a retailer conserve cash will receive management support.

"Data mining is even more important now in terms of being able to understand margin erosion. Shrinkage is the last free margin on the table. We have got to keep the stock current," says Hearn.

At the start of the data mining project, Jaeger forecast that it would make a return on investment within six to nine months of the project going live. That target will be met. Jaeger now expects both a significant improvement in margins and a substantial benefit to its net profits.

"The sheer opportunities to improve margin - it's not just about fraud, it's about putting the wrong stock in the wrong place at the wrong time. As a result, the decision to go with data mining was very quick. I had no resistance from Jaeger," Hearn says.

In Jaeger's case, the difficulty with implementing its data mining application did not come from the management it came from the complexity of setting up data feeds between Jaeger's existing store applications and its new centralised system. The company decided to buy a data mining application in the summer of 2007.

"It was nearly a year," says Hearn. "It was nothing to do with IDM, but to do with Jaeger. Our data was very complicated because we have had so much in-house development of our systems. For instance, at just one meeting, we had to review at line level the data we used in over 800 fields."

Jaeger's data mining project will make a positive contribution to profits at the most important part of the business cycle. As the recession worsens in 2009, retailers will need to develop similar projects that produce rapid returns on investment those that make sustained improvements to net profits year after year will stand the best chance of winning management approval. As money strains lead more customers and employees to steal from retailers, applications that can reduce theft will become increasingly important.

How data mining gathers information

A data mining application becomes more powerful if it uses a greater number of feeds from the retailer's other systems. LossManager was built in the Microsoft Development Environment and was written in C++ so it can be used to accept feeds from as many different systems as possible.

"We can take feeds from almost anything. We can use that information to ask if there is a correlation between a store that loses a lot of product and EAS deactivations and alarms. One of the departures from previous approaches is that for an application to be truly effective, we have to integrate multiple sets of data," says Hearn.

Several data mining applications already use video feed from CCTV cameras to make sense of Epos data. Many retailers would like to use the two technologies together, but they are unable to do so because their CCTV cameras use analogue rather than digital film. For most retailers, the cost of replacing analogue cameras with digital cameras far exceeds the financial benefits that they expect to gain from reducing their losses.

Retailers with radio frequency identification (RFID) projects could even use the information from tagged pallets or individually tagged items within their data mining applications. Unfortunately for advocates of RFID technology, the only retailer with a public RFID project in the UK is Marks & Spencer, which tags different ranges of clothing in most of its major stores.

Audit teams use a type of network theory called link analysis to understand the patterns between data on different systems. Auditors look for symmetric patterns between two sets of data, or more likely asymmetric patterns, to understand the relationships between different types of information.

Retailers are not the only organisations that use data mining to look for correlating information. Governments have used data mining to sift through huge amounts of data to identify potential terrorist attacks. In 2002, the Pentagon started a secret project called Total Information Awareness in an attempt to identify terrorists. Total Information Awareness was a data mining project on a massive scale. In 2003, it was cancelled after Congress removed funding over fears that it was too intrusive.

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