My Coke Rewards

There has always been a rolling might to Coca Cola marketing and they are now stealing a march with the My Coke Rewards loyalty program as well. The enormous success of the program (see Promo) has created an equivalently enormous data store chock full of useful information. In awarding it the 2007 Interactive Marketing Award for "Best Loyalty Marketing,” Promo.com also noted that Coke has unleashed advanced technology to exploit this new information.

"Coke has invested in the collection and mining of consumer information. This data is already fueling customization on the site, and is also being used for e-mail and mobile promotions and other types of communication."

Coke is using Enterprise Decision Management (EDM) software with its new data store to automate operational decisions concerning promotional activities. Radan and Taylor (2008, ¶ 3) describe EDM as a new approach, that integrates Business Intelligence data analysis with business processes, combining operational and analytical processing. This is in contrast to the separation of data from business process inherent in data warehousing.

EDM is avant-garde and Coke is being applauded for its vision and mastery (see EDM Blog). Taylor (2007, ¶ 2) notes that data generated from loyalty programs can be infused with energy from an EDM “to improve marketing, store-layout and many other decisions.” One example he gives is its application to decide what rewards or rebates actually result in a change in customer behavior.

References
Radan, N. and J. Taylor (June 2008). Enterprise decision management uses BI to power up operational systems. Teradata Magazine. Retrieved on September 3, 2008 at (http://www.teradata.com/tdmo/v08n02/Viewpoints/EnterpriseView/Choices.aspx

Taylor, J (July 26, 2007). Growing your business with decision management. Retrieved on September 3, 2008 at http://edm.findtechblogs.com/default.asp?item=656748

Fan Traps and Chasm Traps

When consolidating numerous data sources, including databases and lists, care must be taken to issues besides finding proper keys or, alternatively, match codes. That is pretty daunting on its own. Worse comes, Chasm traps and Fan traps. They can mislead us into very wrongful and wasteful action in our direct marketing even if we have organized the database properly.

In many consolidated, operational data stores, we typically end up with a core record in a “fact” table that is associated with multiple “dimension” tables with characteristics for each record in the fact table. For example, a university Alumni table as our “fact”/core table, a Donations table and a Financial Aid table, among others that tell us more about the person in the fact table.

Someone in the Alumni table may have made 0, 1 or many donations and so have 0 to many associated records in the Donations table. Likewise, someone in the Alumni table may have received 0, 1 or many scholarships or loans to attend college. Many of our data associations in this type of consolidated customer database will have this 1 to many or 1-M quality.





This can result in very subtle errors and very misleading interpretations in the analysis we do on the database or in the more mundane operations such as mailings. Let’s say we want to test the hypothesis that Alumni who received financial aid are more likely to donate money than those who did not, a propensity to donate. The correct way to accomplish this is to run two queries, one a list of alumni and the sum of all amounts they donated, and the other a list of alumni and the sum of all financial aid they received. Then we merge the two results sets.

It is tempting instead to run one query that reads our alumni table and simultaneously sums both donations and financial aid in the related tables. In a SQL database we have just stepped into a chasm trap. Such a query will disproportionally count both the donations and the financial aid for those friends who have made more than 1 donation AND received more than 1 loans or scholarships. (see Business Intelligence Blog, p 1 or IDS, pp 1-3 or http://db.grussell.org/resources/pdf/co22001%20notes.pdf

References
International Documentation Solution (April 14, 2000). Recognizing and resolving Chasm and Fan traps when designing. Retrieved on August 28, 2008 from http://www.eagle.co.nz/businessobjects/pdfs/ttchasm.pdf

Napier University (August 2002). Database SystemsStudent Notes. Retrieved on August 28, 2008 from http://db.grussell.org/resources/pdf/co22001%20notes.pdf

Hallmark Crown Rewards

Duncan (2004, p 226) says that Hallmark does not rely much on demographics in its marketing analysis but instead “places much more emphasis on psychographics.” An artist with Hallmark explained that the relationship rather than the age is the essential element in their work. A program like Crown Rewards can help build an informative database of customer attributes and behavior patterns and add supersonic energy to their creative work, marketing communications, and strategic planning. It can even keep the company's market share intact, like it did for a Hallmark that was troubled in the early 90s.

They were hurting in the 1990s (see Hallmark History) because the world had changed and caught them unawares. They “had fallen victim to changing buying patterns in particular among women, who still bought 90 percent of all cards sold.”

Since implementing the program in 1994, the company has avoided the dire decline. Hallmark gains twice the revenue from Crown Rewards members than from general customers. Here is an internal study by Phillip Morris on Hallmark and its use of the consumer database and the uplifting effect the Crown Rewards program had on the Hallmark company (see Phillip Morris on Crown Rewards Database).

In addition to helping Hallmark, the Crown Rewards consumer database also supports the marketing efforts by Hallmark retail franchise stores, such as Mark’s Hallmark Stores (see iPass Case Study). Besides access to the Crown Rewards database, Hallmark also sells access to its high-speed data communications network named Hallmark/iPass. It is also useful for Hallmark subsidiaries such as Crayola.

When we are creating an account for the Crown Rewards Program, Hallmark asks if its affiliated companies can e-mail about special offers (see Hallmark Registration). This extends the psychographic profiling capabilities of Hallmark to companies such as Crayola, which probably could not afford to maintain such sophisticated data analytics functionality on their own. (see Crayola History)

Hallmark is a great study because it shows a hidden motive – the data motive- in loyalty programs.


References
Duncan, Tom (2005). Principles of Advertising and IMC. New York: McGraw-Hill Irwin.

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.

References

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

Phelps J., and M. Bunker (Winter 2001). DIRECT MARKETERS’ USE OF PUBLIC
RECORDS: CURRENT LEGAL ENVIRONMENT AND OUTLOOK FOR THE FUTURE. Journal of Interactive Marketing.

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

Speedpass, a Loyalty Gadget

Exxon-Mobile’s Speedpass Card (See Speedpass.Com) is a high tech loyalty program that Hoffman (2005, pp 312-314) calls Loyalty Gadgets. These loyalty devices are similar to the loyalty cards of traditional rewards programs but customers can load initial cash balances and reload cash as needed onto the card, or they involve a more convenient high-tech gadget than the traditional credit card.

In the case of Exxon, the Speedpass Card is a convenience for their customers, extending interactive social media to the gas pump. As an incentive for joining customers usually get a rebate on gasoline for the first 90 days of use. For business users, it also provides handy accounting of fuel purchases. Hoffman also notes (p 312) that these loyalty gadget cards create “a sense of belonging” to compliment the convenience and rewards. There is also a novelty to the experience that is “cool” adding to a sense of modernity.

According to Hoffman (p 313), the benefits to Exxon are that customers appear to “spend more when they don’t see the money.” ExxonMobile reports that Speedpass customers spend 15% more than non-Speedpass customers. Exxon now has in excess of five million members according to Hoffman (p 313). Covvenience is reflected in the bottom line.

In addition to use at Exxon Mobile affiliated locations, Speedpass is expanding into the fast food retailers with McDonalds. The customer database can be used by McDonalds to offer the same convenience provided at the pump. In addition to key fobs, Timex is now partnering with Exxon Mobile to have Speedpass enabled watches.

The downside is cost. Hoffman notes the following costs: 1.) Advertising; 2.) Card reload costs; 3.) Synchronizing provider’s inventory management system; and 4.) Scanning technology.

Reference
Hoffman, K.D. (2005). Loyalty Gadgets: The Marriage Between Technology and Loyalty Marketing. Marketing Principles & Best Practices 3e. Thomson/Southwestern.