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Harnessing the Power of Enterprise Decision Management To Drive Retail Growth

(Posted by Guest Blogger, Ian Turvill.)

Ray Boyle, our colleague at Fair Isaac, posted an entry to the CRMGuru.com blog today entitled: "Harnessing the Power of Enterprise Decision Management To Drive Retail Growth".  Ray is an expert on the factors that contribute to retail success, having worked at a senior, strategic level at large store chains such as Sam's Club.

In his posting, Ray makes the point that all retailers are awash with data.  But what differentiates the successful retailer from its peers is its ability to use that data to improve decisions-making.  Ray particulary focuses on the issue of "same-store sales growth" which is the holy grail for retailers who want to keep their investors happy.

An Enterprise Decision Management (EDM) approach can help retailers improve rates of same-store sales growth in two important ways.  First, advanced predictive analytics can generate relevant insights.  Second, EDM provides the capacity to imbed decisions based on these insights into operational systems.  EDM therefore give a retailer not only the ability to build a better strategy, but also the capacity to put that strategy directly into action.

Ray brings to his readers' attention four things that must be done to drive revenue growth:

  1. Drive store traffic:  Get more customers into the stores
  2. Increase conversion rate: Convert more store visitors from “shoppers” into “buyers”
  3. Improve basket size: Encourage buyers’ purchases to be as large as possible
  4. Maintain, or preferably lift, margins: Undertake positive ROI marketing programs

200701_graphic_optishopper_2

In the context of this CRMGuru.com posting, Ray is a little short on the details for achieving each of these things, but he does emphasize several core component of an EDM-type solution that will be very familiar to readers of this blog.  They include:

  1. Extensive extracts from an enterprise data warehouse, including purchase histories, customer segment information (demographics, lifestyles, estimated lifetime value), channel preferences, survey responses, etc.
  2. Advanced predictive analytics, including the use of data to create insightful customer segments (e.g., customers who not just look alike, but act alike) and techniques that analyze transaction data for insight into the "why" and "when" of purchase patterns.
  3. Advanced decision analytics, including advanced optimization technologies that go beyond predictions to build a model that considers all relevant factors, such as likelihood of response, potential value of customer, cost of offer) to find the "sweet spot" of the optimal offer.
  4. Business rules management systems incorporating rules and analytics that can operationalize marketing strategies at the point of customer interaction

I'll try to dig out other gems that Ray may have of relevance to the retail space.

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