Ever since reading that article in The New York Times about Wall Street-like data analysis being applied to Madison Avenue marketing — what I would call computational marketing, as a nod to computational finance — I’ve been searching for more stories about that idea.
It turned into a bit of a nomenclature expedition.
A search for “computational marketing” in Google brings back ~1,000 results, with the first page being dominated by a single SEMPO job posting from January 2007 for a computational marketing internship. (“These opportunities are ideal for mathematics, computer science, operation[s] research, statistics or engineer[ing] students”.)
Most of the job description is grunt work that doesn’t seem particularly computational (“growing a list of reciprocal links”, “mailing list discovery and management”, “reverse word of mouth advertising”). What exactly is reverse word of mouth advertising? But then the very last bullet is:
Implementing computational marketing campaigns using distributed architecture
You know, in between rote data entry, have the intern kick that out. Oof. This is a classic argument for why marketing needs a technologist on the management team.
But clearly “computational marketing” has not been a hot term. Bummer, I kind of like it. (Any chance of a rally to make #compmktg a trending topic on Twitter?)
So next up I searched for “algorithmic marketing”. A mere 131 hits for that term, although the first page of results has a little more substance. One is remarks from a GigaOM interview with Gail Ennis, Omniture’s SVP of Marketing, where the interviewer asks if customers are ready for sites that optimize themselves. Ennis replies:
Some customers say, “I don’t want the system to do it. I want to control it.” And we love them to do that. Particularly as it gets to the keyword spend, it’s so complicated to try to optimize keyword rules so people are much more reluctant to say, “I’m going to let an algorithm take that over for me.” But some companies, and some marketers within companies, just get it right away: “We’re going to throw everything into that autoserve, let the creative serve up, let the algorithms build.” [At this year’s user conference, Omniture CEO Josh James in his remarks] opened up with this very fact, that algorithmic marketing is ripe for automation right now.
Another is from a Scandinavian (?) company called Poets + Plumbers — isn’t that a great name for a digital marketing agency? — that has a post on digital marketing “micro patterns”. Talking about the different factors that play a role in how people respond to marketing execution (product, price, positioning, message, method, media), especially in a social network, they point out:
One of the most important aspects of new-school marketers is their ability to analyze data and identify consumer response patterns. This will give the marketer the opportunity to analyze data and identify consumer response patterns. This will give the marketer the opportunity to constant[ly] update and [improve] the marketing process. In an online marketing context, this model can be taken to an extreme by integrating the analysis with an automated response system, thereby creating algorithmic marketing. In the algorithmic marketing setup, the choice of message, method and media is constantly switching, depending upon which combination gives the best effect.
Okay, “algorithmic marketing” isn’t yet a widespread term either — but the meaning seems to be heading in the right direction.
My final search was for “marketing engineering” — which somehow sounds less cool to me than the previous terms, so I wasn’t expecting much. Lo and behold, a whopping 210,000 results came back.
The top result is a site called Marketing Engineering, run by two Penn State management professors Gary L. Lilien, who apparently coined the term, and Arvind Rangaswamy. The pair have written a couple of textbooks on the subject, Principles of Marketing Engineering and Marketing Engineering: Computer-Assisted Marketing Analysis and Planning.
However, it appears that a lot of the focus of their vision of marketing engineering predates the web. It includes things like cluster analysis for market segmentation, multinomial logit analysis for customer targeting, conjoint analysis for new product design, etc. But the big picture concept that they’re advocating seems right on:
Several forces are transforming the nature, scope, and structure of the marketing profession. Marketers are seeing increasingly faster changes in the marketplace and are barraged with an ever increasing amount of information. While many view traditional marketing as art and some view it as science, the new marketing increasingly looks like engineering.
Lilien and Rangaswamy’s emphasis is on using software and techniques to help marketers “collect the right information and perform analysis to make better marketing plans, better product designs, and better decisions”. They position marketing engineering as decision support, in contrast to the automated execution implied by algorithmic marketing.
The authors make the first chapter of their Marketing Engineering book available for download free, The Marketing Engineering Approach. It’s a great read, although again, the application of this to state-of-the-art digital marketing is left as an exercise for the reader.
So I’d say “marketing engineering” has the most momentum behind it. But will it make the leap to the new new marketing, or will the term evolve with the discipline?
Are there other names that capture this intersection of marketing and computer science? (Be nice.)
Or is this simply marketing automation?