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CPG Innovations from Targetbase

MM: That’s perfect. Could you give me an example of some of the innovations that Targetbase has brought to the CPG area? Specifically where you’ve worked around the lack of POS sales data?

Yes. In fact, this gets specifically into what we refer to internally as our “pattern of engagement analysis.” Thinking that we actually pioneered for a direct commerce site — Southwest Airlines’ site. We then ported it over into a CPG environment, without the transactional data.

Really, at its core…

MM: Before we let that gem get away, I what you to expand on the strategy of first establishing a baseline pattern of engagement. Shall I assume that you first developed the data structure and approach using a very robust, high volume e-commerce site from Southwest?

That’s where the idea started.

MM: So, the idea of patterns of engagement came out of dealing with real customers, real services. Real content. Real transactions.

Exactly.

Patterns of engagement

MM: Again, shall I assume that you developed an evolving set of analytic themes and data-analysis patterns that you then brought back into an area, where you lacked one critical part of the overall analytic equation — real POS data. As a function of the analytic themes and patterns you developed where you did have it, did you find that ou could then infer or make really good educated guesses, in terms of what the other non-POS activity data was actually telling you?

Well, I would say yes from a methodology standpoint. The actual patterns of engagement themselves are unique to the separate clients.

But yes in the sense that the methodology that we developed — which I’ll describe in a second… We kind of cut our teeth in a very much hands-on transactional data sort of way, in order to move that approach over to other clients. Yes.

We tend to view engagement differently, I think, than most. Particularly webshops. But most, in general.

When you refer to “engagement,” or when you see it out there in the marketplace, most are talking in a very aggregated sense, and at a very point-in-time sense.

For example, I put a video on YouTube and I got a 125,000 people to view it over the last week. That’s my engagement metric.

Well, that’s not the way we think about engagement. That’s one of the reasons we refer to it as “pattern” of engagement at Targetbase and with our clients.

The way we view this idea is that each individual — known or inferred — as they come to our site or engage with an application on their phone… Whatever the case may be… as they visit a store… They leave clues. They leave evidence of their presence.

For example, if somebody logs onto the General Mills site for Betty Crocker. They go view their recipe box, where they’ve stored recipes that they’re a fan of. They go view a video on how to make macaroni-and-cheese or whatever the case may be. They engage with the site.

Each of those activities is tracked. And most importantly, each of those activities is then stored on a database at the individual level. So from a processing standpoint and from a data-capture-and-storage standpoint, it is sizable. But thankfully we have the technology to achieve that, now.

As you mentioned earlier in the call, we have the analytical tools to utilize that now, as well.

But each of those activities is stored at the individual level on a database. Then our approach is to basically — in the beginning — subjectively weight each of those activities. To categorize them in terms of their relation to our desired outcome. In this case, purchase.

For example, the fact that I open an e-mail is a much lower form of engagement than printing off a coupon. If the ultimate objective is that they’re going to purchase my product. So, e-mail opens are a much lower form of engagement.

The reason I say, “in the beginning,” it is subjective, is because a lot of times we have to work with our marketing partners to just have a starting point for what we think the impact of things is going to be. Then as we capture more data, we can actually model or regress in order to determine the actual impact of interaction with a particular piece of content on the ultimate outcome of purchase.

For example, there might actually be a certain class of coupons — or a certain class of e-mails, videos or what have you — that are more predictive or have a bigger impact on the likelihood of purchase.

Once we establish that paradigm — that weighting paradigm — then everything that every consumer does with this, that we can measure and track, is captured and stored and weighted and rolled up into this metric that we refer to as, “pattern of engagement.” It’s captured over time.

For example, in the case of General Mills, we have 3-plus years for those that have been there that long, of everyone on their database. Their pattern of engagement. In this case, we store it in monthly buckets. We roll it up into months. Then we’re able to perform time-series analysis on it. That allows us to trend it and to model it.

We can then say that at the individual consumer level, “Here’s somebody who is high in terms of their level of engagement, and they’re decreasing in activity from a directional standpoint.” That vector or that combination of volume and direction tells us how we need to interact with them from a relationship standpoint. “Here’s somebody who’s mid-level engaged, but they’re increasing in their engagement.” That again dictates how we interact with that person.

I think the uniqueness in the approach is the sophistication of the analytics, certainly, and the strategy and actionability it provides. But there is also a big technology component. Ensuring the proper tagging and tracking is in place, not to mention the sheer size of the proposition to capture all the engagements each individual consumer has with us, and to store them on a database, over time.

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