Effective data use is essential for customer centricity

Sep. 2 2009
By Bill Franks and Alexi Sarnevitz
Email: bill.franks@teradata.com, alexi.sarnevitz@sas.com

There are some industries where acquiring good data is a huge struggle. Not so for retailers. They’ve got the opposite problem: How to effectively use the tsunami of SKU and customer data flooding their data warehouses. This includes the management and analysis of the data, while also making it easily accessible for front-line operators. Getting the analytical results to the right people to take opportune action heightens the business value. Evidence suggests those retailers that do a good job of this are weathering the current recession much better and are primed for greater growth as the economy begins to turn firmly around.

These retail superstars have analyzed their data to place customer profitability at the center of their decision-making processes. Best Buy has garnered publicity for its Barry and Jill market segmentation work. Its chief competitor, Circuit City, is out of business. If your company wants to do more than survive the current economic climate, you need to become a more customer-centric enterprise by pursuing initiatives in the following areas now:

•  Develop detailed portraits of your most important customers. A sales associate with a box of index cards detailing customer preferences is being replaced or augmented by analytics that affect what is stocked at a store and which promotions go to these top customers. Capturing the contents of those index cards in the database is important -- it allows others access to that knowledge over time and stops the information from disappearing when the sales person leaves. That data can be accessed and mined for patterns and opportunities.
• Tailor assortments with more than store size in mind. It would seem intuitive that 50-lb. bags of dog food don’t sell well in urban stores, but many retailers still focus solely on store size in making assortment plans. Savvy retailers go beyond store size, looking at urban vs. rural setting, upscale vs. middle-class clientele, even the week in the spring when people in a specific region want charcoal for their barbecues. This is achieved by building assortments for unique store clusters identified on the basis of actual customer demand patterns.
• Target communications and promotions to loyalty card holders. Loyalty programs provide retailers with a lot of data that many companies sheepishly admit sits unused. The best retailers segment loyalty customers and offer rewards and promotions based on the customer’s shopping profile. With the latest automated CRM applications, retailers are applying deep analytics to enable the automation required to tailor communications for each shopping occasion, often in real time.

All of these initiatives revolve around the idea of segmenting customers to produce higher sales and stronger profit. In a report done in conjunction with Retail Systems Research, companies were asked whether they considered segmentation important and then compared their answers to their same-store sales. The research findings identified a strong correlation between above-average sales performance and a belief in the importance of customer segmentation.

Foundational to any of these efforts is well-organized data that can be easily analyzed. If it takes two weeks per month for your data team to pull and organize the data from the warehouse to be analyzed, you can’t make quick decisions. Savvy retailers use in-database analytics to reduce the time it takes to leverage the data to mere minutes. With less time devoted to preparing the data, companies have more time to analyze, build and execute models. And because in-database analytics involves less tinkering with the data to move it over to be analyzed, companies can feel more confident about the accuracy of the results.

The second building block is to make sure information on multiple sales channels is available in one view. Take the example of high-end customers whose spending puts them in the top quintile of your customer base. Sounds like the kind of customers that should get plenty of sneak-peek promotions and e-mail offers, right? But what if they return 50% of their online purchases to a store -- after the season is over? You need the analytical capability to discover the cherry pickers and focus on the big spenders who are most profitable. 

Once profitable customers are identified, information must then be used to target customers according to their preferences, with offers and promotions that really work. Have you ever received a checkout coupon or e-mail offer for something you had no interest in buying? What a waste of effort. There are other ways to turn each opportunity into a sale while building a more personal relationship with each customer. An online floral retailer has aggressively used analytics to tailor what items should appear on the Web site based on who is typing in the URL -- and which items should be promoted in e-mails. For example, rose buyers get rose offers; people who favor novel or expensive bouquets get the latest designs in that line. One side benefit to this is that if you are providing a more timely and relevant offer to your customer, you may be able to discount less and still get the sale. This further adds to your bottom line.

Assortment planning takes on greater sophistication when analytics come into play. Along with stocking to factor geography and local preferences, there are other dimensions. One retailer was able to calculate the Top 100 destination items that had to be on the store shelves (not in the stockroom or on a truck) to prevent shoppers from abandoning their carts and heading to a competing store. With tailored local-market assortments, retailers can deliver greater return per foot while also keeping target customers happy.

Using analytics to target and focus on high-end customers can yield some surprising results. One food retailer had been using analytics to purge its shelves of high-priced, slow-moving items. Then it took a deeper look at its most profitable customers and discovered there was value in keeping that $25 bottle of wine on the shelf a little longer. It turns out that bottle of wine was bought by customers who also grabbed some gourmet cheese. And these same customers ignored the weekly loss leaders typically relied on to generate store traffic.

These analytic concepts do not involve hugely expensive solutions with massive integration challenges and slow benefits realization. The data is already there and is already integrated, in most cases. Retailers can transform into customer-centric enterprises via a series of “bite-size” initiatives that generate return on investment (ROI) as you go. Significant ROI can be realized in as little as six months. In addition to improving current profitability, these programs position retailers to leapfrog the competition as we exit this recession.

Bill Franks is managing partner of Teradata Advanced Business Analytics, where he oversees the retail advanced business analytics practice for the Americas region. Franks can be reached at bill.franks@teradata.com.

Alexi Sarnevitz is SAS senior director of global retail strategy. He can be reached at alexi.sarnevitz@sas.com.