Smart Data Smart Decisions Smart Profits

Smart data smart decisions smart profits 9 mcki29 ret

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Unformatted text preview: ative behavioral data sources. Smart data, smart decisions, smart profits 9 MCKI29 - Ret. Smart Des.7 8/9/00 11:08 AM Page 10 How To Build a Smart Database Retailers building a customer information database should include: • Customer behavior metrics, off-line metrics include total gross/net sales, sales by department, number of trips, number of departments purchased in, number of items purchased, and loyalty program participation. On-line metrics expand to include browsing behavior as well as purchases (e.g., visits per month, time per visit, average pages viewed per visit, average time per page, shopping cart abandonment, previous/next site visited). • Promotion history data, especially for high-low retailers, to assess what percent of net sales and items were purchased at discount or markdown (e.g., regular, permanent, clearance) prices or the percent of transactions with a coupon. These are solid starting proxies for promotion sensitivity; another key element to track is the number and response to direct marketing contacts (e.g., mail, e-mail). • Channel usage, comparison of on- and off-line shopping behavior patterns (e.g., overlap in categories purchased in/visited, type and timing of purchases, promotion sensitivity). • Lifestage demographics, profile of each customer’s age, gender, income, family and home ownership status to spotlight differences in life-stage needs and to help qualify high-value prospects who have characteristics similar to those of current high-sales/profit customers. purchase frequency, size of average purchase, and seasonality. For example, in grocery, three to six months of data can suffice, while a department or specialty apparel store would benefit from a 12- to 24-month view, given less frequent purchase cycles and sharp seasonal spending shifts. For categories such as furniture or large appliances with multi-year repurchase cycles, data needs can be truncated by integrating life-stage and demographic data to project a consumer’s propensity to purchase and using insights from current purchasers and this data to scour prospect lists. In the on-line world, where retailers can track browsing as well as purchase behavior, shorter time cycles (e.g., daily, weekly) are highly actionable for improving site content, layout, pricing and promotion decisions. 10 ©McKinsey & Company 2000 MCKI29 - Ret. Smart Des.7 8/9/00 11:08 AM Page 11 By combining both behavioral and attitudinal data at the individual level, retailers can understand why consumers do what they do and spotlight the best ways to get them to change their behavior to increase sales and profits. But beyond securing the right data sources, one major challenge is to build nimble and cost-effective systems and skills to effectively mine customer data. The sheer amount of information can be overwhelming, particularly when it includes Internet clickstream data that can often reach 100 gigabytes or more per day. We have found that retailers can get started quickly in seizing customer opportunities by leveraging simple PC or workstation based systems that focus on a limited set of customer behaviors t...
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This note was uploaded on 02/08/2014 for the course RCS 391 taught by Professor Jeanielim during the Fall '14 term at University of Tennessee.

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