Business Intelligence For The Retail Industry - 4298 Words

Business Intelligence for the Retail Industry:
Actionable Insights for Business Decision Makers by Don Tapscott

Brought to you by Business Objects (an SAP company), SAP and Intel.

Business Intelligence for the Retail Industry:
Actionable Insights for Business Decision Makers

Executive Summary
THE RETAIL SECTOR was one of the first sectors to make

significant investments in collecting and integrating customer data in data warehouses. Retailers have generally earned a significant return on their IT system investments by using business intelligence systems to analyze the data to improve business performance with a focus on reducing operating costs, without sacrificing the customer experience. The levers that a retailer can use to optimize performance include: price, promotion, markdown, assortment, space, allocation and replenishment. Data-driven decision making is key to successful decisions regarding all of these levers.
In the future, firms will need to continue to be cost effective but increasingly will need to focus on using data to drive revenue by better understanding their customers’ needs. Increasingly, this understanding will come from supplementing internally collected data with the vast quantities of external data generated (or made accessible) by the Internet. Organizations need a new generation of business intelligence (BI) tools and applications to integrate this cross-enterprise, inter-enterprise and external data in order to achieve insight and transparency, across all channels. Enterprises that effectively harness the vast quantities of information that IT systems generate⎯both within the corporation and outside its walls⎯are poised to gain competitive advantage.

1.0 Value Proposition
Competition in the retail sector is becoming increasingly fierce as the complexities of global expansion, rapid product cycles, currency fluctuation and changing customer preferences continue to transform many segments. In 2006, convenience store sales were up 15%
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in the U.S. yet profits were down 23.5%. The average
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supermarket makes less than 1% net on sales. Price pressure from Wal-Mart and other “big box” retailers, and eBay’s micro-retailers, be they from Chicago or
Zhangzhou, have squeezed prices to where many survive by the thinnest of margins, in what, according to the stock market indexes, is the best of times.
Putting retailers further at risk are macroeconomic
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issues such as low rates of consumer’s savings, high oil prices and the faltering U.S. housing market. Each of these phenomena is putting pressure on consumers’ purchasing
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power and by extension on retailers’ bottom-lines. In addition, disruptive new technologies are coming online that may inevitably commoditize retail sales even further.
For example, today in Japan, having your smart phone take a picture of a UPC code on a product in one store may offer you a more competitive price in another.
The historical comfort of 100%+ markups and traditional assurances of profitability for this industry have long passed. In these maturing markets it is not enough for retailers to understand what customers want; they must anticipate customers’ future needs in order to get in front of competitors with innovative, market-leading product assortments. Today, business intelligence is no longer limited to the traditional, narrow definition of “delivering reports to users.” BI now encompasses the use of data to derive insight and achieve competitive advantage by not only answering the question “what did customers want?” but by increasingly answering the questions “what do customers want now?” and “what will they want in the future?” The potential to do so now increasingly depends on the effective use of business intelligence systems to utilize available data to help create value for customers. As it has for the last 30 years, a portion of

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