How to Use Database-Driven Analytics to Flourish Despite Economic Crisis

 
 
By Bill Franks  |  Posted 2009-05-13 Email Print this article Print
 
 
 
 
 
 
 

Retail businesses rarely see the kind of economic stress that is facing them in their markets today. The economic crisis has forced consumers to be very selective about buying. The most successful retailers will be those who are adept at data optimization using advanced, database-driven analytics. Knowledge Center contributor Bill Franks explains how retailers can exploit every byte of information from their enormous data assets, which continuously flow through the databases they depend on for illumination.

Two fundamental shifts are occurring in the commercial world: the data environment is integrating and gravitating to the center of every business, and the roles of the analytics practitioner, the business analyst and the IT specialist are fusing. The emerging skill set is a multidimensional one, and the role might be described as that of "analytics power player" or one who knows how to best exploit a company's dynamic information assets for competitive advantage-while serving as a catalyst and bellwether for teams of his or her associates.

The new analytics power players are hard to find; there are not enough of them to go around. They have specialized knowledge that makes them as rare as Stradivarius violins and almost as valuable. They know that the best analytic solutions are integrated with sophisticated marketing communication tools to ensure that messages sent to customers are relevant, timely and arrive at the right moment. This engages customers with meaningful dialogues and helps to drive profitable relationships.

For these reasons, retailers must invest in data warehouse-driven analytics to uncover opportunities to reduce costs and drive revenue growth. Companies have uniquely valuable information assets which, when examined and leveraged through detailed analysis, can contribute dramatically to positive performance. So, specifically, on what type of analytics should retailers focus? The following are ten important types:

1. Frequent shopper programs

Current economic pressures are an opportunity to refocus the retailer's loyalty toward the customer, with targeted offers and benefits. Most retailers under-use the card database and leave opportunities "in the box", when they could be mining for opportunities that are most meaningful to their best shoppers today. For instance, retailers should be using analytics to ensure that promotional funds are directed at the right customers (the big spenders or most profitable) and not the wrong ones (the "cherry pickers" or least profitable).

2. Multi-channel marketing

Being able to engage the shopper with a consistent experience across all channels of business requires that all channels leverage the same customer information. For example, shopper card programs-where most shoppers see benefit when shopping in the store and getting discounts at the register-could also segue nicely into e-retailing, e-mail marketing and even mobile marketing. This could allow for targeted offers such as e-coupons to be directed inexpensively to shoppers based on their shopping behavior, demographics or need. Tracking these promotions electronically offers a wealth of insights that can be plowed back into subsequent promotional plans leveraged by all channels.



 
 
 
 
Bill Franks is Managing Partner of the Retail Advanced Business Analytics Practice for the Americas region at Teradata Corporation. In this role, Bill helps Teradata's clients implement advanced analytics and data mining techniques to improve their business and gain value from their corporate data. Bill has extensive background helping clients derive value through the use of data analysis and modeling, with a focus on making results accessible to the business community. Bill's work has spanned clients in a variety of industries, ranging in size from Fortune 100 companies to small non-profit organizations. He can be reached at Bill.Franks@teradata.com.
 
 
 
 
 
 
 

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