Traditionally, retail industry has lagged behind other industries in adopting new technologies, and this holds true in its acceptance of BI technology. Some industries, such as financial services, have become very sophisticated in using BI software for financial reporting and consolidation, customer intelligence, regulatory compliance, and risk management. However, retailers are quickly catching up and beginning to recognize many areas of BI that can be applied specifically to their businesses.
The competitive game is changing for retail. As industry continues to consolidate, retailers have begun to realize that using technology to better understand customer buying behavior, to drive sales and profitability, and to reduce operational costs is a necessity for long-term survival.
Retailers are now paying significant attention to BI software, specifically in areas of merchandise intelligence (including merchandise planning, assortment, size, space, price, promotion, and markdown optimization), customer intelligence (including marketing automation, marketing optimization, and market basket analysis), operational intelligence (including IT portfolio management, labor optimization, and real estate site selection), and competitive intelligence. There are many factors that have led retailers to adopt BI software: increased competition, need to squeeze more profitability out of less space, prevalent credit card usage, Internet's role as an alternative sales channel, popularity of loyalty cards, and soon, RFID (radio frequency identification). These milestones have created a wealth of data that retailers are now beginning to appreciate and use.
Within individual companies, we view history of BI in retail through a method that we devised to describe status of any company's evolution toward becoming an intelligent enterprise. We believe that organizations pass through five fundamental stages as they advance in their use of BI as a competitive differentiator:
Operate -- At this most basic level are companies rife with information mavericks: guys in basement offices hammering away on desktop spreadsheets. If they go, knowledge goes with them. There are no processes, and each request becomes an ad hoc data rebuild, resulting in multiple versions of truth, with likelihood of a different answer to any one question every time it is asked. Consolidate -- At this stage, a company has pulled together its data at departmental level. Here, a question gets same answer every time, at least within department. However, departmental interests and interdepartmental competition can skew integrity of output and result in multiple versions of truth. Integrate -- At this point in evolution, a company has adopted enterprise-wide data and bases its decisions on this more complete information. This company is beginning to have a true awareness of additional opportunities for use of BI to improve processes and profits. Optimize -- At this stage, company's knowledge workers are very focused on incremental process improvements and refining value-creation process. Everyone understands and uses analysis, trending, pattern analysis, and predictive results to increase efficiency and effectiveness. The extended value chain becomes increasingly critical to organization, including customers, suppliers, and partners who constitute intercompany communities. Innovate -- This level represents a major, quantum break with past. It exploits understanding of value-creation process acquired in optimize stage and replicates that efficiency with new products in new markets. Companies operating at this level understand what they do well and apply this expertise to new areas of opportunity, thus multiplying number of revenue streams flowing into enterprise. Armed with information and business process knowledge, organizations approaching innovate level will introduce truly innovative products and services that reflect their unique understanding of market, their internal strengths and weaknesses, and an unfailing flow of ideas from continuously engaged employees. We are finding that most large retailers have reached or are approaching integrate stage, with many making great strides toward optimize and innovate levels. There is an enormous opportunity for evolution to continue -- within every retail organization.
The Presence of BI in Retail IT Infrastructure
In typical retail IT infrastructure, there are two fundamental categories of systems: transactional/operational systems, such as POS and purchase order management systems; and analytic/BI systems.
Operational and transactional systems such as merchandise management, ERP (enterprise resource planning), and POS, are very good at what they do -- organizing huge amounts of operational data and transactions. These systems can tell retailers what has happened in their business and what their customers have done -- last week, last month, and last year.
It's critical, however, for retailers to understand what will happen: what demand will be for a select assortment of merchandise, what impact an incremental price change will have on demand, which floor plan will sell more designer shoes, which customers will respond to a direct mail or catalog offer.
Real value comes from systems that go beyond limitations of operational software alone, systems that can take operational data and create enterprise intelligence and predictive insights.
These BI systems must combine data management (consolidating, organizing, and cleansing huge amounts of disparate data from varying systems and platforms) with predictive analytics (data mining, forecasting, optimization). When they do, retailers can make sense of customer, product, supplier, and operational data and draw insights that will help them run their businesses better and more profitably.
Leading retailers around globe -- like Wal-Mart, Foot Locker, Staples, Williams-Sonoma, and Amazon.com and many others -- have begun using BI and analytics to make an array of strategic decisions. These include where to place retail outlets, how many of each size or color of an item to put in each store, and when and how much to discount. The effects of these decisions can save or generate millions of dollars for retailers.