Professor Tom Davenport Advises Retailers To Cultivate Potential Benefits of Analytics

Industry experts and retailers themselves agree that most retail companies have not yet realized the full potential of the breadth of analytics available to them today. Economic climate aside, retailers must strategize for long-term success, and using analytics effectively can go a long way to creating security for businesses on the other side of the economic recovery.

Recently SAS and Teradata commissioned a study on analytics along with Tom Davenport, professor at Babson College and co-author of the book “Competing on Analytics.” The original plan for the study was to focus on specific themes related to analytics in retail, but Davenport quickly found out that first the industry needed to take a step back and evaluate all possibilities and potential retail analytics could provide. The recently published study is titled “Realizing the Potential of Retail Analytics.”

“I was surprised by the huge potential of retail analytics and the breadth of opportunities retailers have to choose from,” says Davenport. “But I was equally surprised at the lack of coordinated approach coming from retail businesses. It was difficult to find one person in retail who could speak to the broad range of analytical activities.”

Learning from the Best
During interviews with 33 retailers and more than 25 retail analytics experts, Davenport noted that the same companies came up over and over again when discussing best practices. Companies like Best Buy, Walmart, Target, Kohl’s and JCPenney have taken the lead in developing successful processes using specific types of analytics. But, he notes, none could speak to the complete realm of analytics resources.

Overall, Davenport found that “the companies that have aspirations to be big and successful are focusing most closely on analytics, including department stores, online retailers, and probably most of the large discount retailers.” But most retailers today are realizing the potential benefits of analytics, and some have very recently changed their outlook regarding analytics. “A few years ago I did some work with TJX and at the time they said: ‘We are traditional merchants and are not sure analytics relate to kind of merchandising we do.’ But they are not saying that anymore.”


Making the Best Analytics Choices
Given the breadth of choices available, and the fact that most retailers don’t have a huge amount of money for investment today, making the right choices is critical, says Davenport. He suggests that the first key step is to understand your company’s business strategy.

Ask the question: “What are we trying to accomplish as an organization?” For example, a number of analytics applications relate directly to understanding the customer, knowing what products they buy and developing a detailed understanding of what they want to purchase in the future. “Those applications make the most sense for organizations that have customers who spend a lot with them, have relatively high margin products and can afford to invest a lot in learning more about their customers,” he notes.

Other companies might want to focus their analytics choices elsewhere. “A discount retailer might want to focus on assortment planning or pricing optimization – two applications that deliver a quicker ROI.”

Retailers also can learn from their peers when making decisions about implementing analytics. “Today, everybody in retail should consider themselves in multichannel environment, and therefore should look at the leaders in that area such as Amazon and eBay. Those organizations are doing extensive testing, using lots and lots of web analytics, and collecting product recommendations.”

Outside retail, retailers could look to other industries for best practices. “The credit card industry has done a lot in looking at customer behavior,” Davenport notes. “And banks, Capitol One for example, have been very aggressive in the use of analytics and testing. Additionally, the travel and transportation really pioneered a lot of the pricing analytics.”

The 18 Most Common Analytics Applications
Taking an almost encyclopedic approach, Davenport constructed a list of 18 common analytics applications in his report. In explaining each application, Davenport notes best-practice retail examples. The list is separated into three distinct categories:

Widely Adopted Analytics Processes

1. Assortment Optimization and Shelf Space Allocation  
2. Customer-Driven Marketing  
3. Fraud Detection and Prevention
4. Integrated Forecasting
5. Localization and Clustering  
6. Marketing Mix Modeling
7. Pricing Optimization
8. Product Recommendation
9. Real Estate Optimization
10. Supply Chain Analytics
11. Test and Learn
12. Workforce Analytics

Organizational Trends

13. Adoption and Use of Analytics
14. Analytical Ecosystems
15. Centralizing Analytics
16. Store-Level Empowerment

Strategic Initiatives

17.    Analytical Performance Management
18.    Multi-Channel Analytics

Looking to the Future
While many retailers are still getting their proverbial arms around the current analytics options, they also should keep their eyes on what’s coming down the road. Davenport notes five future analytics trends to watch:

  1. Clienteling. “I think if you’re Brooks Brothers, Nordstrom or Nieman Marcus, you already are doing clienteling to some degree, but you going to use analytics to determine product selection in the future. This could be a relatively minor application to add.”
  2. Video Analytics. If you’re really into fraud detection and shrinkage prevention, soon it will be possible to analyze video in that way, “but first the industry must wait for the vendors in this area to mature a bit.”
  3. Sentiment Analysis. “It could be cool to know what customers are thinking about a particular fashion line and could help retailers select styles for fashion-oriented goods. I don’t expect this application to take off soon, though.”
  4. Demand shaping analytics. “We are not that far away from this. Companies are already doing a lot of supply chain, pricing and assortment optimization. Once they get these systems connected they will be able to perform advanced demand-shaping analytics.”
  5. Real-Time Offers. “This is already happening a fair amount in online and people in other parts of world are doing it with mobile technology, but the U.S. is lagging the world here. More and more of mobile phones are capable of transmitting proximity information, so I think it’s probably not that far off. We could easily see that a particular carrier and technology manufacturer (AT&T and Apple, for example) could partner to ask customers if they would like to be notified of deals they would be interested in when they are physically within the proximity of a retailer.”

While Davenport began the study before the most severe economic downturn, he hopes that retailers will continue to invest in analytics. “It is pretty clear that the really great, well managed companies continue to invest in analytics, even in a down economy.”

To access the complete study, go to

Featured Event

Join the retail community as we come together for three days of strategic sessions, meaningful off-site networking events and interactive learning experiences.



Access The Media Kit


Access Our Editorial Calendar

If you are downloading this on behalf of a client, please provide the company name and website information below: