Harnessing the Power of Enterprise Decision Management To Drive Retail Growth

Retailers today face the challenge of building customer-centric marketing programs that achieve one or more of the following three basic goals:

• Bring more traffic to the store
• Convert more store visitors from "shoppers" into "buyers"
• Encourage buyers’ purchases to be as large as possible

The most successful retailers will take into account not only these top-line goals—-i.e., traffic, conversion, basket size—-but also bottom-line profitability. They will ensure that building profit margin occurs not only at the net level, but also at the gross level—-that is, making sure that sales don’t grow solely because of discounting and other price promotion that rob from the bottom line.

What makes some retailers better than others at getting there faster? Data. In fact, the proliferation of data sources—-external data (behavior, lifestyle, etc.), as well as their own customer and transaction data--means that most retailers have access to a mountain of data on their customers.

The challenge, however, lies in using the data in a systematic and powerful way to improve decisions—to generate offers that are optimally targeted to customers, and that drive value that can be measured and used to further improve value in the next marketing campaign.

In fact, retailers have huge amounts of data available to them for marketing purposes, but very few of them are putting it to good use.

An Enterprise Decision Management (EDM) approach can help them overcome this challenge. It offers two important advantages. First, advanced predictive analytics that can generate relevant insights. And second, the capacity to imbed decisions based on these insights into operational systems.

Suddenly, retailers have not only the ability to decide what their strategy should be, but also the capacity to put that strategy into action in their stores and other channels.

The steps in an EDM-based approach to measurably building customer value begin with an analysis of the retailer’s existing programs—measuring their agility, consistency, and precision, as well as speed and cost.

Key components of an EDM solution include:

1. Extensive extracts from the client’s 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); or techniques that analyzes transaction data for insight into the "why" and "when" of purchase patterns.

3. Advanced decision analytics, including advanced optimization technology that goes beyond predictions to build a model that considers all relevant factors (e.g., likelihood of response, potential value of customer, cost of offer) to find the "sweet spot" of the optimal offer.

4. Business rules management systems, including incorporating rules (e.g., number of categories, program objectives, purchase triggers), and analytics into systems that can be operationalized to automate offers at customer touchpoints for any channel. These systems also allow for appropriate business users to easily change or adjust rule parameters to respond to new campaign strategies, without the involvement of IT.

The strength of the EDM approach is that a retailer can address any individual driver of profitable same-store sales growth. Or it can be used to improve all measures in tandem. It’s been shown over and over again to be a powerful, systematic approach for automating and improving decisions in all critical growth areas.

Ray Boyle, vice president with Fair Isaac, leads the Global Retail and Consumer Branded Goods Industry practice areas. Since 1990, he has worked with executive teams to build analytically driven decisioning organizations. Previously, he was with Wal-Mart's SAM’S CLUB Division. He holds an MBA from the University of Notre Dame.