Tuesday, August 19, 2008

Requiem for a Rolodex

Denise Schoenbachler, Geoff Gordon, Dawn Foley and Linda Spellman published attractive and informative guidance (see http://web.cba.neu.edu/~fsultan/Database%20Marketing.pdf ) for creating a customer database useful in Database Marketing. As a first step, they recommend that a vision document be prepared to explain the corporate need, to record user profiles, and to designate a project sponsor.

If the primary corporate use is list management, they suggest that a service bureau, such as All Media (see http://www.allmediainc.com/index.html) would be the most cost effective approach. On the other hand, if the corporate need is for segmenting customers, correlating customer traits with purchasing behavior, or which customer personas are the most profitable then a database development effort is necessary.

Sources of Feed Data
To build such a repository both internal and external sources of data must be filtered and merged. What types of information are typically stored and where can we get them? Spiller and Baier (2005, p73) list the usual suspects:

  • Demographics
  • Psychographics
  • Financial history
  • Prospect interactions
  • Prospect Interaction Dates
  • Address and phone number
  • Profitability or net financial value

Psychographics is synonymous with life-style and personality. Such a profile can add supersonic energy to a direct marketing campaign by segmenting prospects for more effective communications (see Spiller and Baier, 2005, p 39). It may be derived from the appearance of a customer on various lists from different publications of note. A fine and handy source, however, remains The Lifestyle Selector by Equifax. It is fed by responses to surveys and completed product registration cards.

Spiller and Baier (pp 37-39 ) further disclose other sources of powerful information. These include the CensusCD Neighborhood Change Database. Census data (p 38) is a good source of demographic data about our prospects. This is the usual necessary demographics: age, gender, education level, income level, occupation, and type of housing. They (p 13) suggest that customer lists from prominent publications may have relevance not only as data to overlay the customer database but as channels for direct response print advertising.

There is also (p 13) the implication that web site audit logs are useful sources for data mining to see where visitors to the web site sourced from, as well as what landing pages they reviewed and for how long at they stayed at the web site. Their CGI headers will contain their IP address, and the Internet Service Provider (ISP) can be traced from that. It may be possible to purchase a list to get the demographic data and other monitoring data held by the ISP.

Additionally, there are compiled lists from third parties such as the Department of Motor Vehicles, Birth records, and other state and county court house data. Regarding information from governmental sources, Phelps and Bunker (2001, p 34) caution that

“Although the individual-level nature of public records information increases its utility for marketers, the use of such individual-specific information has contributed to consumer privacy concerns.”

Is all lost? Direct Marketers may be able to use access statutes to retrieve the desideratum. The Freedom of Information Act (FOIA) is a popular choice and there is no specific exclusion for commercial use of the information (p 35).

However, they summarize legal arguments (p 44) on the topic with the assertion that while FOIA may not restrict use based on motivation for obtaining the information, it does not guarantee access for commercial purposes. They conclude (p 46) that

“the creation of provisions that discriminate based upon the motivation of the requester seems a slippery slope that legislatures should avoid starting down. Marketers must realize, however, that public opinion, like gravity, can work to push legislation down this slope.”

How to Grow the Database
Spiller and Baier (2005, p 52) suggest starting with a simple name and address database and then incorporate geographic, demographic and psychographic data about each prospect. They explain (pp 75-79) how to calculate the Life Time Value (LTV aka PAR) of a customer and to decide if the cost of obtaining and maintaining the customer’s data in our database is a worthy exercise. The PAR Ratio is cost/PAR. If this ratio is less than one, then the customer’s record pays its freight.

According to them (p 7) the process should start with defining personas for the prospects themselves:

  • Who they are
  • What they need and want
  • How the college fit into that
  • Where they are located
  • When they are ready to interact with the college
  • Why they interact
  • Their level of commitment
  • What channels for distribution are most efficient.

Crucial facts from the databases would be the recency, frequency and monetary value of interactions with prospects (p 10). This will help profile an expected relationship with the prospect and the your company. It is vitally important the data obtained externally be guaranteed fresh (p 44). Stale data results not only in wastage but in antagonism to non-prospects getting spammed.

Lost Your Keys?
Keys are a critical part of joining data from different sources so you can be sure that you are adding the correct information from one source record to its corresponding record in another source (see Olson, 2003, p 176). Keys are fields or attributes that uniquely identify a record such as SSN but not like name. However, having such keys across all lists is a luxury, although Spiller and Baier have a remedy to take the place of unavailable keys (p 65) – a match code.

Match codes are formulated from Name and Address information and can be used to match records from different source so they can be properly merged (p 66) . They can also be used to purge duplicates. A counter of the duplicates discovered should be incremented on the retained record before the duplicate is deleted (p 68) because showing up on two or more lists is an indicator of the strength of interest for that prospect.

The database should also be updated with results from our own integrated communication campaigns to appealingly increase efficiencies. Schoenbackler, et al say that the different promotions or specific motivational actions and the responses to such, are

“perhaps, the most valuable benefit of the database. Marketers no longer have to
accept John Wanamaker’s lament that ‘half of what I spend on advertising is
wasted – the problem is which half?’”

They also recommend that a marketing analysis be conducted on the customer database to sub-serve two important functions: 1.) profitability; and 2.) Trends. Trends analysis is a dramatic synthesis of our most productive through least productive customer profiles – i.e. who are the whales.

What problems can confront us? Our old friend data quality is a usual suspect. See http://gmrwvu.blogspot.com/2008/07/impact-of-data-quality-on-new-media.html . Traditional project management failings can torpedo this type of effort but they can work their powerful wreckage on any type of information technology effort.

Schoenbachler, et al sum up that database marketing is a necessity not an option.


Olson, Jack (2003). Data Quality; Morgan Kaufmann Publishers.

Phelps J., and M. Bunker (Winter 2001). DIRECT MARKETERS’ USE OF PUBLIC

Spiller, L. and M. Baier (2005). Contemporary Direct Marketing. Pearson/Prentice-Hall.

1 comment:

Anonymous said...

Geoge....Why would you think I would be interested in this??? There is a Finnish word I could use: HULUU.

Guess who.