MM: Let’s quickly explore the notion of marketing operations. Let’s start with a few presuppositions. One, I think many marketing professionals can agree that marketing no longer entails just developing insights, messages, and program-putting lipstick on pigs.
MM: By that, I mean it’s no longer about, “Who can we sell this stuff to, and how have we got to tickle their consumption button, such that they want to buy it?”
MM: Increasingly, marketing now entails the development of interactive, on-demand, re-mixable customer self-services as well as the provisioning of these services to my customers and potential customers.
MM: So in many respects, the career path for tomorrow’s CMO rides the rail of digital service platforms and IT service provisioning.
MM: It’s going to require a completely different mindset. Now, in this interview we spent a lot of time already talking about the “analytic mindset.” Great stuff!
MM: It starts by working backwards from the customer’s preferences and cohort and into the product portfolio mix, as you said, “What do I have the right to sell them or the right to win?” Using that, then, to do media-mix optimization in terms of, “Where do I spend my scarce marketing dollars, to activate those consumers and those cohorts that I want to have the right to win?”
MM: Then I begin to see that it’s not just winning, but keeping them. How do I keep them? How do I keep them engaged? Then increasingly, that means that I’ve got to be remixable. To use another funny time, I have to be able to customerize content and services.
I would say another piece to that is something you mentioned before. “How do I or how can I intelligently leverage user-generated content?”
The age-old example of Amazon. Their recommendations and their ratings. That was a brilliant insight on their part, to realize that, “You know what? I can allow customers to generate content on my site that’s not only going to make that customer happy who got to rate it, but it’s going to inform the purchase decision of future customers. They’re more likely to come here as a result!”
MM: Well, back to this notion of a preference center or information preferenda. I’d like to have another social indexing of “who said what?” It’s one thing for a 22-year old college kid living in a dorm to say, “This is cool.” It’s quite another thing for a 55-year old business owner to say, “This stuff works!”
MM: So I’d like to be able to slice through a lot of the rating and say, “For people like me, what do they say?”
In fact, it brings to mind an idea that — unfortunately — we weren’t ever able to execute for one particular client. I think it was Move.com. They own Realtor.com, et cetera.
The idea that they had wasn’t necessarily a bad idea. It was, “Can I create neighborhood-level sites?” I would argue that the idea is a bit dated, now. This was a while ago.
But, “Can I create neighborhood-level sites, where we as a neighborhood — as neighbors — can exchange information and recommend restaurants in the area,” et cetera? To share information about the neighborhood.
The business model idea they had was that the thing would be funded by local advertisers. Local restaurants, local service providers, et cetera, would be interested in being there, from an ad perspective, et cetera. Because those are their local customers.
One of the things that we recommended and pushed back to them was the idea of… In terms of both profile and preference center — if an individual comes on and you understand who they are, then you can do a better job of matching them up with other individuals in the neighborhood or in the general area that look like them.
For example — to your point… If I’m looking at the average rating of a local Thai restaurant, then I can filter — if I want to — down to people who look more like me. So, people who like stuff that I like. Or people who…whatever the parameter is you want to choose. Those who are like me. What do they say about this restaurant?