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Data Modeling
by Daniel Hopping

The cost of information technology has come down dramatically over the last ten years. Everything has become smaller, cheaper, faster. As a result of this, smaller retailers are now able to do things that only the largest chains were able to afford only a dozen years ago. To take advantage of this revolution in IT capabilities, retailers need to know what to do. The trick is to know which of the many opportunities available is the one that will give you a competitive advantage and make your customer want to drive past your competitor to get to your store.

One of the most valuable tools now affordable to smaller retailers is data modeling. This is a use of a data warehouse that can accelerate the development of new applications and processes. It can reduce the cost and risk of examining different alternatives. It can make raw data actionable.

Most retailers have a good deal of raw data from inventory, promotions, POS transactions and loyalty programs. Very few retailers can turn this data into the daily decisions that improve the business. Data modeling can help build targeted promotions based on loyalty information and evaluate the results of the promotion. It can improve the accuracy of forecasting and assortment planning.

I have recently been involved in a number of projects to define the next generation of the store. One of the defining components of the best of breed future store projects is the use of data modeling to evaluate the many different options that are available. Iíve seen projects that were designing store capabilities, which relied on decisions requiring unobtainable data. Easily available, understandable, actionable information seems to be the critical success factor for the next generation store. No matter how exciting the ambiance and merchandising layout, if you canít make good day to day decisions, a new format will not be successful enough to warrant the expense of the change.

Most future store projects will require a new business model to be successful. Data modeling can allow a retailer to find the best model and tweak that model to let it evolve with the customer and store format.

The first step is to get the enterprise data into a data warehouse that is compliant with the Association for Retail Technology Standards (ARTS) Data Model.

Open standards are important attributes that now make data modeling more accessible and affordable. A caveat is that you cannot make decisions from data that you donít have. It is important to have a good POS system that collects detail data by store, by customer, by SKU, by time frame, by payment method, etc. Security and privacy of the data is also a showstopper if you do not have it. See the article on Data Governance in an earlier issue of XR23.

There are data management tool kits available that have detailed retail content to reduce the development time significantly.

A data modeling project for a small retailer does not have to start out with every thing in place to get benefit. The project can focus on one aspect that will provide the highest return and add other capabilities as everyone becomes more familiar with the tools. This is also an area that benefits from outtasking. Outtasking is the outsourcing of a narrow function or task such as POS training or data model creation.

One of the biggest benefits of a retail data model comes from understanding the customer. The data model can provide: campaign and promotion information with cross purchase behavior along with customer attrition, complaints, credit risk, delinquency, interaction, lifetime value, loyalty, profile movement, profitability and market basket analysis.

Daniel Hopping is a global technology futurist, author, consultant and speaker. With four decades of hands-on experience, Danís area of expertise is forecasting the impact that technology will have on the retail industry and tomorrowís consumer.

Copyright © Daniel Hopping. All rights reserved.

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