Improve Customer Relations with Top Rate Data Warehousing

The battle is for supremacy in the grocery market, the battlefield is Texas, and the combatants are HEB (Howard E. Butt) and Walmart. I am HEB's newest marketing and information technology strategist (see disclaimer below). In the following pages, I will describe the current market environment of the two major combatants. Then, I shall lay out a strategy for HEB, through the effective use of data, to gain market share and become the leading grocer in Texas.

Currently, Walmart controls approximately 23.4% of the Texas grocery market. HEB is a close second with 23% (1). Walmart's success comes from an efficient distribution system and an outstanding pricing program derived from analysis of data from their extensive data warehouse (2). HEB also has a substantial data warehouse and marketing program, but those alone are not enough to keep them competitive with Walmart. HEB also performs what I would consider to be a form of Customer Relationship Management (CRM). There are numerous examples of HEB changing products or policies at a particular store based on the customers who frequent it (3). Many industry analysts believe store tailoring is necessary to gain some type of edge to compete with Walmart (4). Keep this idea in mind, because I plan to expand it as I explain the strategy for the future at HEB.

What do we (HEB) need to do to continue to compete with Walmart? My plan involves the use of technology and data to create an intelligent retailing experience that can be tailored to the individual. We will establish an active CRM program which will include a near real time data warehouse function. This program will result in the expansion of the current Teradata data warehouse to include customer specific data. Once data is gathered and properly analyzed, we will use other technologies and methods to complete the experience. How will we do all this?

First, I plan to start an 18 month prototype program. Company management will meet and decide on thirty stores spread throughout the state to participate. In those stores, I will install data servers for creation of local data marts. Also, each store will receive ten hand-held computers, two National Cash Register (NCR) self-checkout terminals, and a computer console for customer log-in. Those are the hardware requirements. What about the data?

To obtain the individual customer data required, the prototype stores will begin a loyalty card program. This program is essential to the effective use of the local data marts. Once a customer dimension has been created, a history of that customer can be recorded. After the first six months of data collection, we will use an analysis tool to mine the customer data mart to make marketing decisions. We will then use a software tool that is designed to analyze sales data to determine best and worst customers. Our marketing staff can analyze the buying patterns of our best customers and perform direct mailings pertaining to products of interest. This activity will help promote loyalty. They can also perform data analysis of middle customers to try to determine how to transform them into best customers. As for the worst customers, the ones who are unprofitable, the marketing staff can send them fliers from our competitors. Now that I've explained the source of the data and its' primary use, I will move the discussion in the direction of the shopping experience and the use of the local data mart as an active tool providing near real-time information.

What about the in-store experience? How can that be tailored to the individual? We can accomplish this by using data and new technology. Let's say, for example, that Jane Jones enters the store and decides to use one of our hand-held computers for faster checkout. Before she can take one of the computers, she must swipe her loyalty card through a console slot. At this point, Jane is now 'online' in the store (similar to being signed into a website, such as Amazon). All of Jane's personal shopping data has been loaded into the hand-held computer from the data mart. Now, Jane can have a unique shopping experience because the computer can communicate with her based on its' 'knowledge' of her. Sensors placed strategically throughout the store notify the computer of Jane's location. This allows the computer to alert Jane to nearby specials of interest to her. The hand-held computer is also a scanning device which allows Jane to scan items before placing them into bags inside her shopping cart. It keeps a running total of her order to allow for faster checkout. Upon completion of the shopping experience, Jane must insert the hand-held computer into an NCR terminal near the exit. She can now scan any coupons she has and complete the transaction by paying the balance using the method of her choosing. Since the items were scanned and bagged while shopping, the entire checkout process takes less than one minute. This fact may be key to increased business in the future, because industry surveys show 93% of customers list long wait lines as a major issue with the shopping experience (5). It is easy to see how my plan can build loyalty among our current customers.

In this document, I told you about the problems facing HEB and the potential of losing more market share. I then laid out a broad plan to increase customer loyalty by using technology and data marts. We will get to know our customers through the card programs and tailor our stores and marketing to them. Creating a faster and more individualized shopping experience is the only way to win the war.

Disclaimer: In the preceding paragraphs, I stated I work for HEB. In actuality, I have no affiliation with HEB. The purpose of writing the paper in this manner was to express the ideas from a hands-on viewpoint. Therefore, I took on the role of marketing head.

References:

1. http://www.kioskcom.com, Lottery Machines roll out at HEBs, M. S. Monroe

2. Art Conklin, UTSA, Advanced Business Information Systems class

3. http://www.reveries.com/coolnews/2004/december/dec_1.html

4. http://www.atkearney.com/shared_res/pdf/EA72_MeetCustomers_S.pdf

5. http://www.executivetechnology.com/xref.cfm?ID=5727&xref=H-E-B