Database Management: Get to Know Your Customers Even Better

Best-practices database content lays a foundation for sophisticated CRM.

In an ideal world, every cataloger would have access to a state-of-the-art customer relationship management (CRM) system, including Web-enabled business intelligence, campaign management and customer touch point capabilities. Every organization would enjoy the continuous, widespread internal dissemination of complete, accurate and compelling data. All data miners and marketers, and all employees interacting with customers, would have instant access to all the data required for them to excel at their jobs.

In such a world, smaller catalogers would operate with the same technological advantages as larger ones.

Unfortunately, many catalogers don’t have the budget to invest in cutting-edge CRM systems. But without best-practices content, sophisticated, data-driven CRM is impossible.

Best-practices Content Is ...

Best-practices database content provides a consolidated view of all customers and inquirers across all channels, including catalog, e-commerce and — when applicable — retail. It’s as robust as the underlying methods of data collection are capable of supporting. The complete history of transactional detail and relationships must be captured, because high-quality content supports deep insight into the behavior patterns that form the foundation for data-driven decision-making. Everything, within reason, must be kept, even if its value isn’t immediately apparent.

Best-practices content includes four characteristics:

1. Purchase data. All orders and items must be time stamped and at the atomic level. Robust purchase detail provides the necessary input for seminal data mining exercises such as product affinity analysis. You can always aggregate, but you can never disaggregate.

Don’t archive or eliminate data. For example, it’s difficult to do a product affinity analysis if orders and items are rolled off the file, say, every 36 months. Ideally, even ancient data will be retained. Unlike 10 or 20 years ago, disk space is cheap and you never know when you might need the data.

Data semantics must be consistent and accurate. For example, merchandise needs to be identifiable over time, despite any changes that have taken place in naming conventions and/or the numbering of inventory.

2. Retain post-demand activity, such as returns, exchanges, allowances and cancellations. These are essential for important exercises, such as identifying the customers who’ll be less likely to make future purchases without remedial action. After all, customers disappointed with unavailable, ill-fitting or damaged merchandise are less likely to order again.

3. Maintain ship-to/bill-to (often, gift-giver/receiver) relationships. These enable targeted promotions to extend the customer universe beyond those who placed the original order.

4. Keep promotion history across all available channels. This enables you to rapidly and accurately create the past-point-in-time views required for most analytical projects, including predictive models.

One multibillion-dollar cataloger- retailer (that can’t be named in this article) has learned the hard way the importance of including promotion history. Although it spends seven figures a year on its CRM system, the company’s underlying database doesn’t contain promotion history. As a result, most data mining projects take a week longer than they should.

Multiple Linked Levels

In consumer marketing, scrupulously de-dupe and properly link individuals to households. For business-to-business (B-to-B), link individuals to sites, and sites to companies. This way, you can calculate accurate performance metrics, including promotional financials.

Or, you can supplement database linkages with third-party overlay data to create a complete view of individual customers and inquirers, households, sites and companies. In consumer, you can append the identity of additional adults within customer and inquirer households. This includes descriptive demographics, such as date of birth, age and gender. For B-to-B, additional individuals can be appended to sites, and additional sites to companies, including “firmographics,” such as industry type and number of employees.

Past-point-in-time Views

Best-practices marketing database content must support the ability to easily and rapidly recreate past-point-in-time customer and inquirer views (“time-0” or “freeze” files). These, in turn, form the basis for virtually all meaningful direct marketing oriented analytics. For example, they allow you to analyze and validate files required for predictive models.

Case Study:
An Outsourced CRM Solution

John Craig is co-founder and principal of consulting firm Windward Group and a former multichannel B-to-B marketer. While he was a cataloger, he didn’t have the budget to build a CRM system with all the bells and whistles. So, he brainstormed with experts, asked plenty of questions, and arrived at an innovative, outsourced/in-house hybrid solution.

Craig outsourced the database construction and maintenance, and the ongoing housefile processing, including promotional selections and matchback reports. “No one in my IT department had the background to do this as well as I could get it done on the outside,” he recounts. “And even if I had the talent in house, I knew that this processing would always vie with other internal work for priority. A sudden resignation would be disastrous.”

Craig’s company had a tough time with content. “Our internal systems were not properly documented,” he says. “We didn’t have a consistent and unique coding scheme for our thousands of SKUs. Our operational systems made it difficult to extract back-order, cancel and returns transactions. And, as a B-to-B marketer, we had difficulty mapping the entity relationships and order tracking.”

As for the in-house portion of the solution, Craig took the $50,000 he had budgeted for an associate circulation manager, and invested it in hiring a technical professional who could program in SQL and leverage inexpensive reporting tools such as Crystal Reports. With each update, a copy of the database was transferred to the technician who loaded it onto her PC and then ran all of the necessary reports, ad hoc counts and queries. She manually pushed information through the entire organization on a regular basis.

“It was an unorthodox solution, but it worked,” Craig recounts. “By the end of the first year, I was able to show a very impressive ROI to the company president.”

Jim Wheaton is principal and co-founder of Wheaton Group, a data management, data mining and decision sciences consulting firm that focuses on strategic CRM. Contact: (919) 969-8859 or jim.wheaton@wheatongroup.com.