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Content optimization

MM: This gets to the idea of content optimization. And how content optimization really leverages or exploits three sets of metadata. There’s a set of metadata around the customer. You could call that a “customer object.” Right?

Yes.

MM: In that customer object is all this basic demographic and psychographic stuff. Plus, there’s a whole bunch of preference metadata, as well as my media-consumption profile. What you called your “patterns of engagement.”

Right.

MM: That would all be part of my customer object.

Right.

MM: Then I’ve got a content object. Around that content, I’ve got a smaller schema of metadata in terms of the meaning of this — the social context of it. Where it tends to get highly rated or highly used. What search arguments it tends to satisfy. What search arguments it doesn’t tend to satisfy — et cetera, et cetera.

Right.

MM: Then I’ve got a set of metadata around my ad inventory.

Yes.

MM: Also, what I have on my ad inventory is, “What kind of context does this ad have a particularly high lift and/or pull lift?” Are there keywords and phrases that are part of the topic map by which this thing got tagged? Are there particular keywords and phrases that it tends to activate or lift well on, as opposed to not well on?

Yes.

MM: The convergence of these three sets of metadata — the customer metadata, the ad inventory metadata and the content metadata — really allows me to then create highly activated or potentiated contexts for consumption.

Yes. That’s right.

MM: The idea is then that part of my content optimization… Inevitably, going back to what I introduced earlier in terms of our voice-of-customer content analytics… It’s taking that same technology — text-mining, semantic tagging and so on… Starting to tag — hypertag — my content in terms that allow me to create faceted search, dynamic navigation and personal tag clouds. So when I with this customer data model show up, it activates these particular content tags. It says, “Oh — customers like you also are interested in these related subjects or topics — related to that tagcloud that’s adjacent to the page that I’m on.

That’s right. Exactly. Yes.

Technology and analytics

MM: How do companies start doing that?

Well, I think that in my personal opinion, the two major components of that are obviously… There’s a technology component. What you’ve just described is a pretty wicked mashup of tables and fields, from a technology person’s perspective. How do you associate properly all those different data points? Or at least those three big conceptual areas?

Then, there’s the math. The analytics. How do you identify what belongs together? How do you identify the relationship between those different tags and/or data components and customers, et cetera?

I think that’s again — what’s so exciting about this conversation. I think philosophically, we’re in total alignment. Again, what I love about Alterian — they’re there, as well.

I think we’re taking steps to that ideal. I think we’re moving in that direction. I think the biggest problem we have at Targetbase in terms of our clients is, so many times clients have more fundamental issues than what we’re describing. So what we’re describing sounds like a pipedream.

I think that the underlying components are really technology and analytics. Again, that’s why I truly believe that whether by just pure dumb luck or some grander scheme I’m not aware of, Alterian and Targetbase are uniquely-positioned to take advantage of these trends that I think we’re all seeing.

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