Maponics CEO: Why Point-Of-Interest Targeting Will Ultimately Fail

MaponicsAs mobile usage continues to skyrocket, geo-targeted ads have grown even more compelling for advertisers. Maponics, a 12-year-old location-based data provider that works with Twitter and JiWire, approaches location-based ads with an emphasis on context.

AdExchanger spoke with Maponics CEO Darrin Clement.

AdExchanger: Where does Maponics fit in the ad ecosystem?

DARRIN CLEMENT: Our basic offering is around defining local areas. We compile databases that describe the boundaries in latitude and longitude of things like neighborhoods, zip codes, shopping areas and stadiums and we then license that data out to other companies so that they can do better marketing and have better analytics.

Our products have evolved to be more than just the boundary itself, but to also include information about the area. Say you’ve got a definition of China Town and you can draw it on the map because you have a database of longitude and latitude.  Our customers want to know things like is the area safe? How likely is it to have a foreclosed home? Is it a walk-friendly place? All those things that go way beyond demographics have represented a growth path for Maponics.

Who are your customers?

Our customers are generally in four or five industries. Real estate is a big one. Others are companies like Foursquare, Twitter and ad players like JiWire, that use our data for social and mobile marketing purposes.

What are your thoughts on Twitter’s geo-targeted ads? Are you working with Twitter on their platform?

I can’t comment specifically on Twitter’s detailed plans but I can say that Twitter is a Maponics customer. I wouldn’t say that anyone’s gotten it [geotargeting] completely right, though. One thing that we have seen over the past couple years and continue to see, is that too many of these ad players are thinking of this as a POI [point-of-interest] radius.

For example, say there’s a coffee shop and I’ll draw a radius around it so anyone who enters that geofence is a target. That doesn’t represent a good return on the market because that’s not how people behave. People are more likely to frequent coffee shops or stores in neighborhoods they’re familiar with.

One coffee shop might be closer to you, but maybe it’s in a bad neighborhood and another one is further away but in the neighborhood you already go to. The point is, distance is not the only criteria. It’s the psychological boundary, which is often a neighborhood, that determines how users will respond. I think this applies whether you’re talking about Twitter, JiWire, or the individual brands like Coca-Cola—all these guys unfortunately are still doing too much of the point of radius because it’s easy to understand and relatively easy to deploy.

But as they’re analyzing their campaigns, they’re seeing that they’re getting just as much noise as they are getting quality. That approach is nowhere near as effective as what we call pre-defined geofences.

What kind of data are you collecting to provide that kind of insight or context?

Our product line falls into two categories: one is polygons, which are databases of those boundaries. The other is Maponics Context, which is all the information that’s going on inside those boundaries.

For the boundaries, we collect information about neighborhoods, residential subdivisions, school boundaries, shopping boundaries, college campuses, event and stadium and destination boundaries like train stations, airports and golf courses. All the places where massive amounts of people live, work or play. And the key to our offering is these things have to have a name.

We’re not just arbitrarily drawing boundaries around dense populations. The name might be Swarthmore college, SoHo, etc. On the context side, we have hundreds of demographics that we’ve projected into our polygons.  From there, we’ve had PhDs develop a model where we have a ranking of personal safety and property safety and things like walkability for areas in the country.

How do you address privacy concerns?

That’s one of the feathers in our cap because a lot of marketing solutions especially in mobile advertising, require the precise location of the user to be loaded into the app. Take Twitter for example.

You can log onto Twitter and send a tweet with your exact lat/long of where you are. We view that as creepy and not something people should be doing. Twitter lets you turn that off but maybe you want to let your friends know roughly where you are, in case you want to meet up later.

Twitter has employed our data to allow you to say I’m not going to disclose my lat/long but I’m going to let people know what neighborhood I’m in. That level of anonymity you can have when you do things at a local area level. Companies that use our data can still promote to an individual, who happens to be in the area, but that individual doesn’t have to cough up a whole bunch of private data.

Are you seeing an increase in clients asking about ways to protect their customers’ private data?

I’ll say this as a citizen, not as much as I wish they would. If consumers were driving this more, then the companies would drive it more. I don’t know if I’d say it’s reached a crescendo where everyone is moving towards a privacy protection type of platform. I think it’s something that consumers don’t really understand, how all this data is being collected and potentially used by various organizations. There are companies and consumers who are taking that into account, but it’s not enough in my opinion.

What mobile trends are you watching?

I’d say the idea of doing transactions on your mobile device. Whether it’s an NFC type of thing where you show your phone at the Gap and get charged against your PayPal account or other technologies, there’s a lot of money to be made there and to enhance the customer experience. There’s also a different set of opportunities around fraud protection, technology and policy-wise. As a company Maponics isn’t really playing in that space yet, but some of our customers are starting to go down that path and we’re talking about how data might be useful for fraud protection.

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