By Jim Wheaton
Co-Founder, Daystar Wheaton Group
Original version of an article that appeared in the May 1, 2007 issue of “Multichannel Merchant”
There are Ten Commandments of marketing database content management. This first five were outlined in my February 1, 2007 column. This month, we will focus on the remaining five. But first, a synopsis of the February column:
Data mining is enhanced, and often dramatically, when the source data is improved. The ultimate goal is for data mining to be performed off a platform that we at Daystar Wheaton Group refer to as Best Practices Marketing Database Content. This, in turn, supports deep insight into the behavior patterns that form the foundation for data-driven decision-making.
Best Practices Marketing Database Content provides a consolidated view of all customers and inquirers across all channels. The complete history of transactional detail must be captured. Everything within reason must be kept, even if its value is not immediately apparent.
There are Ten Commandments that, if followed, will ensure Best Practices Marketing Database Content. The first five as discussed in the February column are:
- The data must be maintained at the atomic level.
- The data must not be archived or deleted.
- The data must be time-stamped.
- The semantics of the data must be consistent and accurate.
- The data must not be over-written.
The following are the balance of the Ten Commandments:
#6: Post-Demand Transaction Activity Must Be Kept
Post-demand transaction activity can include cancels, rebates, refunds, returns, exchanges, allowances and write-offs. These are essential for important exercises such as the identification of customers who will be less likely to make future purchases without remedial action. After all, customers who are disappointed by unavailable, ill-fitting or damaged merchandise, or poorly-conceived and improperly functioning services, will be less likely to purchase in the future. One common data mining application is attrition modeling.
The capture of post-demand activity is particularly
important in environments such as high fashion women’s
apparel where return rates can be as high as 40%. Often,
customers with similar gross purchase volume can have
very different return rates. This,
in turn, can make the difference between a profitable
customer and one who is a continuous money-loser. It
makes sense for predictive models to take such discrepancies
into account when rank-ordering customers on expected
behavior.
Tracking post-demand transactions can be a challenge
because it requires the transactions to be retained
by the underlying operational systems that feed the
marketing database. Unfortunately, many operational
systems are not equipped for this task. Instead,
post-demand transactions vanish subsequent to a change
in shipping status. For example, a “backorder” status
will disappear once the corresponding item has been
shipped. The following hypothetical sequence
of events illustrates why this is problematic:
Assume that an operational system feeds a marketing database update process on the first and fifteenth of every month. Also assume that a backorder is generated on June 2, and that the corresponding shipment takes place on June 14. By definition, the customer had to wait twelve days for the merchandise to shipped, which certainly is not ideal from a CRM perspective. If the operational system does not retain backorder statuses, then the June 1 and June 15 “snapshots” that feed the marketing database will fail to reflect the twelve-day wait. With only the June 12 shipment reflected, an important aspect of the customer relationship will have been lost!
#7: Ship-To/Bill-To Linkages Must Be Maintained
Often, these correspond to gift-giver/receiver relationships. Ship-to/bill-to linkages allow targeted promotions to extend the customer universe beyond those who made the original purchase. In fact, savvy database marketers look upon giftees as qualified prospects. In this way, customer databases can be used to drive targeted prospecting promotions, and often with formal data mining techniques.
#8: All Promotional History Must Be Kept
All promotional contacts across all available channels must be retained. This is necessary to rapidly and accurately create the past-point-in-time “views” required for most data mining projects, including predictive models. For multi-divisional firms, and especially those that have acquired other companies, it is important to appropriately handle different coding practices.
One marquee, multi-billion dollar retailer with a substantial catalog/e-commerce division learned the hard way the importance of including promotion history. Although it spends seven figures a year on its CRM system, the underlying marketing database does not contain promotion history. As a result, most data mining projects take a week longer than they should, because of the extraneous processing required to overcome the lack of promotion history when creating analysis files.
#9: Proper Linkages Across Multiple Database Levels Must Be Maintained
For Business-to-Consumer (“B-to-C”) environments, individuals must be properly linked to households. For Business-to-Business (“B-to-B”) and Business-to-Institution (“B-to-I”) environments, individuals must be linked to sites, and sites to organizations. This allows the calculation of accurate performance metrics such as promotional financials, and for understanding the true nature of multi-buyers.
Such links also enable the tracking of pass-along response, and for innovative targeting programs. For example, B-to-B and B-to-I direct marketers can monitor contract compliance across multiple sites within large client organizations. In such instances, discounted pricing is predicated on purchases not being made from the competition. With Best Practices Marketing Database Content, sites within client organizations can be identified that have not received any mission-critical merchandise. Such sites may be out of contract compliance.
#10: Overlay Data Must Be Included, As Appropriate
For B-to-C, overlay data can be appended to create a complete view of customers, inquirers and, when applicable, prospects. Likewise for B-to-B and B-to-I, “firmagraphics” can be added to create a complete view of customers, inquirers, sites and organizations.
One form of B-to-C overlay data is demographics for existing individuals and households on the marketing database, including date of birth, age, gender, marital status and presence of children. Another is the identity of additional adults within households on the database, along with their corresponding individual-level demographics.
For B-to-B and B-to-I, firmographics include SIC or NAICS Code, Number of Employees, and Revenue. Also, additional individuals can be appended to sites that are resident on the database, and additional sites to organizations.
One primary data mining application is the creation of profiles to “paint a picture” of customers and inquirers. However, the possibilities go far beyond that, and are limited only by the imagination. For example, date of birth can be employed to support birthday offers. Specifically, individuals with upcoming birthdays can be targeted with offers of special savings to “treat themselves.” Also, suitable gifts can be promoted to significant-others within the households. Such programs are especially lucrative for retailers.
A Case Study of What Not to Do
Last year, Daystar Wheaton Group was approached about a potential data mining project by a well-known gift-oriented, multi-billion dollar retail and direct marketing company that has been in decline. It soon became apparent that the firm’s marketing database content would support neither the project nor any other form of meaningful data mining. This is because:
Data is archived after 36 months and is difficult to resurrect. Some portions of the database are maintained at the surname (“last name”) level and others at the individual level. For surname-level database records, only one individual’s identity is retained. This means that if a husband orders the first time, and then the wife orders – say – five subsequent times, the database will reflect six orders from the husband. This is particularly problematic for a gift-oriented business. To complicate matters, the database does not track bill-to/ship-to linkages and the corresponding gift relationships that these imply, nor does it contain gender codes.
Often, the acquisition source is inaccurate, which renders problematic many worthwhile analyses such as long-term value. Also, merchandise coding discipline does not exist, the Website does not allow source codes to be entered, and customer records generally do not reflect post-demand transactions such as merchandise returns.
Promotion history is essentially unusable because the database tracks massive amounts of “spurious” activity; for example, “event occurrences” such as records that have been sent to the service bureau for National Change of Address (“NCOA”) processing. Also, there are significant problems with tying promotion history to specific names and addresses, and email promotions are not tracked at all.
Finally, on the Retail side, distance-to-store calculations are based on imprecise ZIP-to-ZIP Centroids. And, they reflect only the nearest store, not where the actual purchase activity has taken place.
Clearly, unless the company rectifies the appalling state of its marketing database content, it will have little chance of reversing its decline!
Final Thoughts
Consider whether you are working with Best Practices Marketing Database Content. The extent to which you are not is the extent to which you are artificially limiting the size of your firm’s revenues and profits. Also consider what methods you might employ to improve database content by enhancing the functionality of your operational systems. There are all sorts of ways to do this. But, that is the topic of a future article.
