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CRM and crap data

MM: Trae — you’ve kind of opened a can of worms. Perhaps by intent or perhaps unwittingly, but there it is. We’ve found in large, well-established brand-marketing organizations — specifically, where they have transaction data — that the CRM system is rudimentary in terms of its data model and its data collection.

Absolutely. I totally agree with that.

MM: One of the most interesting things I’ve found is that they almost make no distinction between the customer, the buyer and the stakeholder. The customer, in a consumer context, is the household. The buyer is the person who actually paid the bill. The stakeholder might be the consumer that influences the buyer — but not necessarily is part of the transaction record. In parallel, we have a similar thing with respect to B2B interactions. I had a conversation with a fellow who used to work at Compaq, and then became part of the DEC merger, in terms of the thing. He said, “You know, Compaq has almost no idea who their customers are.” All they had was a PO with the buyer’s name, and 100 boxes went there. Then over at Digital Equipment — because they were a service firm where they made most of their money from professional services — they not only knew all of the departmental managers and admins, but they knew the birthdays and anniversaries and favorite foods. The whole Harvey McKay 55 questions that you want to know about your customers.

Yes.

Customer DB master

MM: Can you speak to the notion of first of all getting the customer master, and then what you do with that and how use a customer master to support deeper, more meaningful insight about when, where, and how to engage consuming cohorts?

Well, like you say, that’s a very sticky and difficult question. I would say that in terms of knowing your customer, what you describe is very common. We are oftentimes — whether it’s a new business pitch or a new client or what have you — we’re coming to the table because somebody in the organization… probably somebody who just came in from outside… is realizing that, “Oh, my goodness. We don’t even know who our customer is.”

That’s why I mentioned before that we oftentimes begin with something like a segmentation approach. A segmentation project. So we can help that client just understand even in broad segmented terms who their customer is.

Again, from a heritage standpoint, Targetbase certainly began there, and continues to live there. In terms of identifying who your customers are, what they want from a content and a product standpoint — how they want to be interacted with from a channel standpoint, and then also when. When do you want to engage them? The who, what, when, where, how and why types of questions. That’s kind of where we live.

I do think, though, one of the big challenges is, when clients do have an existing database that in many cases they spent a lot of money on, oftentimes — like you said before — the data model itself and the data capture is really lacking. Many times, unfortunately, it just kind of lives off by itself in the IT department. Marketing knows it’s out there. They know that they’ve paid for it or helped to get it funded. But in terms of day-to-day usage and understanding of the consumer, there’s less than you’d hope.

I think, though, in terms of how we try to overcome that barrier — and I could speak at least broadly, in terms of our approach… back to that foundational principle of ever-improving approximation. You always have to start with what you do know, from a database standpoint. Maybe I’ve only got a buyer’s name and address in my database. But there are things that you can do.

For example, similar to the segmentation process, in terms of appended data at the household level. Syndicated data. Trying to map a consumer’s database into a richer data source.

When I say, “Map,” a lot of that again is where I think Targetbase has really broken some ground analytically — in being able to statistically model and identify key variables and key data points that do exist in a consumer database – that also exist in a richer source like a syndicated data source.

Then with a great deal of accuracy, being able to map individuals over. So we can then infer or at least guess what an individual’s attitudes might be — et cetera.

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