Leveraging Business Intelligence to Mitigate Risk

The goal of the retail industry is to generate revenue through the sale of products and services to consumers while minimizing operational costs. In an environment characterized by economic uncertainties and diminished consumer spending compounded by consistently slim profit margins, retailers must focus on preventing unanticipated losses that negatively affect the bottom line. The tools, practices and methods used to mitigate the effects of loss are collectively known as loss prevention.

In this article, we will investigate how best-in-class retailers are using business intelligence (BI) to enhance their loss prevention efforts by leveraging both detailed historical transaction records (for detecting, quantifying and characterizing loss events) and integrated enterprise information (data gathered at the point of sale and throughout the supply chain). Integrating business intelligence with other loss prevention techniques facilitates:

• The Timely access to key information,
• Use of benchmark indicators to automatically isolate significant events,
• Greater security and control over data,
• The Reusability of integrated data sets by other teams within the organization,
• A more tightly integrated evidence chain, and
• Ease of analysis across multiple presentation channels.

Thus, retailers leveraging business intelligence for loss prevention can significantly improve their ability to protect their assets, uncover the root causes of shrink and detect fraud.

Benchmarks of Success

Retailers can realize a competitive advantage by proactively identifying and combating preventable losses across their enterprise, including shrinkage of their inventory investments from shoplifting, embezzlement, human error and breakage during the procurement, transportation, storage and distribution of goods, all of which cost retailers 1.57% of total annual sales at retail or approximately $40.5 billion in 2006.1 In addition, tender fraud occurring in payment transactions may involve refunds; online purchases; or checks, cash, credit cards or gift cards; costing retailers an estimated $89 billion at retail or 3.45% of total revenues.2 Based on operational data captured across distribution centers, freight vehicles, stores, checkout lanes, websites and more, organizations can guide systematic closed-loop loss prevention programs and create tangible improvements for their bottom line.

Currently, most loss prevention revolves around deterrence and surveillance. Store managers and security personnel monitor customer and employee activities in person or using video technology. However, any retailer operating numerous stores faces enormous labor costs to track the activities of large numbers of individuals in various locations over a long period of time using these techniques alone.

Leading retailers are using business intelligence to implement enterprise-wide loss prevention solutions that take into account the wide variety of factors that contribute to economic loss. These solutions enable employees from all relevant departments to analyze and monitor the complete picture of their data using a holistic approach incorporating multiple techniques. First, they develop benchmarks or reference levels for each relevant business measure used to signal abnormal conditions in operations. Central management of the threshold levels for each corporate measure ensures that business rules are systematically observed and updated across the enterprise. Next, specialized automated queries are created to detect exception conditions and produce custom alerts to prompt further analysis and potential corrective action. Finally, retailers apply business intelligence to identify previously undetected sources of loss, such as organized crime rings targeting multiple locations or misuse of manager override keys when the associated employee is not present.

Unifying the Data

The most valuable business intelligence-based loss prevention initiatives integrate four major data sets from disparate systems across the enterprise into a common warehouse for reporting purposes encompassing:

• The Point-of-Sale Data: including detailed transaction records as well as specific tender activity including returns, exchanges and voids.
• Inventory and Supply Chain Data: representing item movement through the supply chain; vendor deals and allowance agreements; and quantities ordered, shipped, received, stocked and sold.
• Employee Data: comprising hours scheduled and worked; transactions posted by store employees across locations and amongst management and non-managerial positions; inventory movement by distribution center and warehouse employees; various discount plans; and tenure.
• Customer Data: identifying patterns in purchasing behaviors and returned items, bad check files and gift card redemption history.

Implementing Analytics

Once the required information is consolidated into the data warehouse, shared business intelligence reports are developed to help analysts understand the operational state of the business and uncover suspicious activities. By tapping into the large amounts of high-quality cross-functional data captured in the data warehouse, analysts can discover problems pertaining to fraud or to inventory shrinkage and extract actionable insights. These reports can be categorized into the following three main groups:

• Employee Fraud – Employees and store managers may use their position to profit from fraudulent activities performed while on the job. In order to develop cases of fraud within the corporation, retailers can use business intelligence to routinely monitor specific giveaways, purchases, returns and refunds executed by employees, and identify exceptions to expected activities. To accomplish this task, they bring together point-of-sale and employee data, track each transaction by employee and type (such as sale, return, exchange, etc.), and investigate inappropriate behaviors.
• Customer Fraud – Over half of all retailers surveyed by the National Retail Federation have experienced shoppers returning merchandise obtained through unlawful methods, returning items using counterfeit receipts, asking for refunds for products or services already used (also known as “wardrobing”) or paying for purchases using fraudulent tender. Retailers in the United States alone lost $10.8 billion to return fraud in 2007, up from $9.6 billion the previous year, according to the National Retail Federation.3 Merging customer databases with point-of-sale data allows retailers to characterize customer shopping behavior based on the types of products they frequently purchase, patterns to their merchandise returns, and their preferred payment methods in order to identify and prevent fraud. Additionally, because the data is joined at the transaction level, collusion between particular customers and employees can be uncovered and a case for fraud developed as necessary.
• Asset Protection – Business intelligence can be applied to develop knowledge about inventory shrink or fraud, not only at points of sale but throughout every link of the supply chain. Tightly monitoring the receipt of shipments, product manipulation and storage in distribution centers, warehouses and retail stores gives retailers opportunities to detect losses from both internal and external sources, and prevent their future occurrence. Depending on the retail segment and negotiated shipping and damage allowances for particular vendors, the acceptable threshold for incomplete deliveries, breakage and loss will vary, so it is critical that benchmarks be established that can help identify which incidents are expected and which are suspect. Business intelligence technology further enables retailers to ensure vendors properly fulfill contract terms, delivering within acceptable parameters of quality, quantity and timeliness.

The Value of Business Intelligence

Leading retailers today are capitalizing on the use of business intelligence for loss prevention by analyzing data across their entire enterprise. They use this information to hold responsible parties accountable for their actions and to implement measures to mitigate risk by discouraging and preventing future abuse. Because identification of abnormalities can be automated, retailers can repurpose resources formerly devoted to extracting, integrating and manipulating large amounts of data. These resources can instead focus on identifying new methods used to defraud the company or core activities that produce tangible value, contribute to margin growth and improve the organization’s competitive position. By applying the principles outlined in this article, retailers can jumpstart their implementation of business intelligence for loss prevention and begin to realize the benefits that can be obtained.

References:

1. Hollinger, Richard C. and Amanda Adams. 2006 National Retail Security Survey: Final Report. University of Florida, 2007.

2. Ibid.

3. Grannis, Kathy and Ellen Davis. Return Fraud to Cost Retailers $3.5 Billion This Holiday Season, According to NRF Survey, National Retail Federation, November 2006; and Grannis, Kathy and Scott Krugman, Return Fraud To Hit $3.7 Billion This Holiday Season, According to NRF Survey, National Retail Federation, November 2007.

SOURCE: Loss Prevention in the Retail Environment