Direct Marketing Educational Foundation Research Articles

On Research Methods in Interactive Marketing

After six generousandproductive years, Ed Malthousehasre-tiredasco-editorofthe JournalofInteractiveMarketing .Itisclearthat the journal has benefited immensely from Ed's stewardship.The Direct Marketing Educational Foundation, which sponsorsthe journal, and many others join me in expressing sincere grati-tude to him. I am personally grateful to have worked with himfor these past two years and I will miss him in his role. I learneda tremendous amount, both about being a journal editor andabout interactive marketing. I am very pleased with the directionthat he and the previous editors of JIM have taken the journalandtheaimsandscopeofJIMwillstayessentiallythesameasin-dicated in Malthouse and Hofacker (2010).One time when discussing the philosophy of the Journal, Edsaid that the Journal of Interactive Marketing was “methodo-logically agnostic.” Now this is not merely a clever turn ofthe phrase. A look at the table of contents of the Journal thenand now confirms this judgment and demonstrates a variedand resourceful use of many quantitative and qualitative tech-niques.OfcoursethevastmajorityofsubmissionstotheJour-nal consist of structural equation models (SEM) executed onsurvey data. One could hardly be surprised at this sinceSEM is the dominant methodological paradigm in academicmarketing. Despite this dominance, I thought that I woulduse this editorial opportunity to elaborate on agnosticism,and on research methods in interactive marketing.The End of the Great DifficultyOnceuponatime,longbeforetheinventionofelectronicin-teractive media, a very small subset of firms interacted withcustomers. These rare firms used the newly developing mailsystems in the United States and the United Kingdom to re-motely provide retail service to millions of people. Aftermanydecadesthetelephonewasaddedtothemix.Thesedirectmarketing firms, and those who studied them, made importantprogress in managing customer relationships. Meanwhile nu-merous other firms, firms that manufactured goods, could notseeortouchtheircustomersexcept with much difficulty andexpense. Data were difficult and expensive to collect forthese manufacturers. Since the economy was dominated bymanufacturing, a host of minds, many in academic marketingdepartments, worked to overcome The Great Difficulty ofToo Little Data. Numerous techniques were implemented toget around The Great Difficulty, many of them borrowedfromsocialpsychology.Pencilandpaperwereusedtocareful-ly coax data from the consumer.Purchase intention scales andother instruments were developed. Scenarios were used in thehope that research subjects could imagine what they mightfeel, think or do in hypothetical situations. Advertising re-sponse was estimated using purchase intention, unless thefirm was gigantic or willing to pay a lot of money for data.Now we come to a key goal of this editorial, which is to of-ficially proclaim that the era encompassing The Great Difficul-ty is over. By this proclamation I hope merely to further JIM'sgoal of publishing leading edge articles in direct and interactivemarketing. New direct distribution and communication channelsareappearingalmostmonthly.Todaytherearelaptops,netbooks,smart phones, tablets, e-readers and interactive games and toys.And I almost forgot, desktop computers. On top of that, eachyearasmallerportionoftheeconomyisdedicatedtomanufactur-ing. On the other hand, the portion dedicated to service, whichoften involves direct contact of some kind between the firm andits customers, is growing.So in my mind at least, The Great Difficulty of Too LittleData has been more than overcome. In fact The Great Difficultyhas been washed away down rivers of data, drowned as it were.Data are created and stored when we visit a Web site, when webuy, when we are exposed to an ad, when we click, when wemessage. More data are created when we simply move abouttown, or when we play games. Even little family businesseswith the odd, occasional Web visitor can generate megabytesof the stuff each month. And social networks? My how people

Predicting Donations from a Cohort Group of Donors to Charities

Charity fundraising organizers increasingly attempt to predict the donations to their causes to maximize the effectiveness of their expenditures and achieve their “social good” objectives. Much of the scholarly work in cause-related fundraising uses organization-specific demographic, geographic, psychographic and behavioral information about its donors to forecast donation amounts. Instead of distinguishing the potential donors, this study focuses on the prediction of the donations from existing donors. Specifically, a large dataset containing four years worth of transactional, appeals, source, and donor data related to a leading U.S. charitable organization was made available to the authors by the Direct Marketing Educational Foundation. The current paper contributes to the literature on donor lifetime value by documenting, in the context of a case study, the results of seven models for predicting future contributions using historic data over four years related to the cohort group of acquired donors.

Semi-Supervised Response Modeling

Response modeling is concerned with identifying potential customers who are likely to purchase a promoted product, based on customers' demographic and behavioral data. Constructing a response model requires a preliminary campaign result database. Customers who responded to the campaign are labeled as respondents while those who did not are labeled as non-respondents. Those customers who were not chosen for the preliminary campaign do not have labels, and thus are called unlabeled. Then, using only those labeled customer data, a classification model is built in the supervised learning framework to predict all existing customers. However, often in response modeling, only a small part of customers are labeled, and thus available for model building, while a large number of unlabeled data may give valuable information. As a method to exploit the unlabeled data, we introduce semi-supervised learning to the interactive marketing community. A case study on the CoIL Challenge 2000 and the Direct Marketing Educational Foundation data sets shows that the transductive support vector machine, one of widely used semi-supervised models, can identify more respondents than conventional supervised models, especially when a small number of data are labeled. Semi-supervised learning is a viable alternative and merits further investigation.

Teaching direct marketing and competing for the collegiate echo award

The purpose of this paper is to provide motivation and direction for marketing, journalism, and mass communication professors to consider offering a basic undergraduate Direct Marketing course and, more specifically, to encourage entry into the Direct Marketing Educational Foundation's Collegiate Echo Competition as a means of structuring the course design and content. The paper addresses the issues of adding a Direct Marketing course, promoting enrollment, and the benefits of the Collegiate Echo Competition to students, potential employers, and professors/instructors. Additional topics covered by the author include: (1) managing and organizing, (2) setting standards of quality, (3) measuring the quality, and (4) measuring the results of using the Collegiate Echo Competition.

Applying neural computing to target marketing

Database marketing uses the power of data and information technology in the pursuit of personal marketing of products and services to consumers, based on their preferences and needs. We explore the feasibility of using neural computing as a means for targeting audiences for promotion through the mail, from among a list of customers in a database, either as an alternative and/or as a supplement to discrete-choice logistic regression models. Detailed numerical examples involving realistic data are used throughout to support the analysis and demonstrate the results. It is shown that, at least for the data used in this study the fit achieved for both methods is approximately the same, but the process of configuring and setting up a neural network for a database marketing application is not straightforward and may require extensive experimentation and computer resources. The results are therefore not encouraging for the neural net approach. © 1997 John Wiley & Sons, Inc. and Direct Marketing Educational Foundation, Inc.

Applying neural computing to target marketing

Database marketing uses the power of data and information technology in the pursuit of personal marketing of products and services to consumers, based on their preferences and needs. We explore the feasibility of using neural computing as a means for targeting audiences for promotion through the mail, from among a list of customers in a database, either as an alternative and/or as a supplement to discrete choice logistic regression models. Detailed numerical examples involving realistic data are used throughout to support the analysis and demonstrate the results. It is shown that, at least for the data used in this study the fit achieved for both methods is approximately the same, but the process of configuring and setting up a neural network for a database marketing application is not straightforward and may require extensive experimentation and computer resources. The results are therefore not encouraging for the neural net approach. © 1997 John Wiley & Sons, Inc. and Direct Marketing Educational Foundation, Inc.

The convenience of catalog shopping: Is there more to it than time?

There is little doubt that one of the important motivations behind direct marketing patronage is convenience. Convenience has traditionally been conceptualized as a time-dominated phenomenon, yet much of the research that attempts to link demographic surrogates of time-constrained individuals/households with direct marketing patronage has shown only weak relationships. This paper reexamines the nature of convenience in the context of direct marketing patronage behavior. It reveals that an important oversight regarding convenience is that it consists not only of a time dimension, but also of space and effort dimensions. The qualitative data on which the study is based provide user-friendly data that offer rather literal suggestions as to how the convenience concerns of catalog patrons can be addressed. © 1996 John Wiley & Sons, Inc. and Direct Marketing Educational Foundation, Inc.

Scholarship program that links education to jobs in the marketplace

Every year since 1966, a scholarship program to place outstanding college seniors into the field of Direct Marketing from the university campus has reaped dramatic dividends. Aptly named the Collegiate Institute, these free, week-long courses have brought together more than 1,500 students giving them a taste of this exciting discipline called Direct Marketing. Most important, records indicate that nearly 50% of those attendees have entered a career in Direct Marketing. This article seeks to inform more professors and professionals about the opportunities that the program creates. This innovative approach to providing talent to what is truly a “niche market” is sponsored by The Direct Marketing Educational Foundation with support from local DM clubs and companies with a stake in this burgeoning way of doing business.