Case study: Jaeger uses data mining to reduce losses

The employees responsible for loss prevention (in Jaegers case, the audit team) use their data inning 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 Snooker, Dim’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 Karmic. DIM says its system has reduced losses as a percentage of ales below Global Retail Theft Barometer’s 1.

3% average for I-J retailers. Although Jaeger has only had the system since June, it already expects a return on investment in its first financial year. Hear 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.

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“We have not gone out en masses and darted 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 Hear. 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 Woolworth and MFC, 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 Hear. 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 ring time. As a result, the decision to go with data mining was very quick. I had no resistance from Jaeger,” Hear says. In Jaegers 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 Jaegers existing store applications and its new centralized system.

The company decided to buy a data mining application in the summer of 2007. “It was nearly a year,” says Hear.

“It was nothing to do with DIM, but to do with Jaeger. Our data was very complicated because we have had so such in-house development of our systems. For instance, at Just one meeting, we Ana to review at Ellen level ten data we uses In over Titles. ” Jaegers data milling 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 teal 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.

Loganberry 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 EASE deactivation’s 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 Hear. Several data mining applications already use video feed from CATV cameras to make sense of Eposes data.

Many retailers would like to use the two technologies together, but they are unable to do so because their CATV 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 (RIFF) projects could even use the information from tagged pallets or individually tagged items within their data mining applications.

Unfortunately for advocates of RIFF technology, the only retailer with a public RIFF project in the I-J 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 legislations between different types of information.

Retailers are not the only organizations 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 collateral 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.