YP's Jason Uechi: We're Building Location-Based Profiles

Jason Uechi, director of engineering and data science, YP Mobile Labs, spoke with RTBlog about his predictions for 2016 and insights on the use of data. Uechi is responsible for managing and optimizing the performance of thousands of campaigns across mobile, online and display.

Uechi has an interesting background: He joined YP in January 2014 as part of the company’s acquisition of Sense Networks, and is more than a data scientist. He’s a pioneer in the field of mobile location data and holds a patent on location analytics that serve as the basis of location-based profiles used to determine the purchase intent of consumers based on previously visited locales. Now that’s a mouthful!

In fact, prior to YP Uechi co-founded the mobile application “Mologogo,” a free GPS tracking service designed to make location-based tracking more accessible to everyone.

Here are his thoughts on what's to come in 2016:

1. Location is primary. “YP Mobile Labs is based around location. Location is a signal for marketing — where people go. We’re interested in accurate location and how to build out profiles of where consumers have been so we can determine the brands and offers that are right for you. Location is a signal for marketing.”

 2.  We’re still in the early days for programmatic and mobile. “We do build profiles from the information in a privacy-safe way. We’re trying to take location data and get it into the form of a profile. For us, we want to see that you’ve been targeted once, twice or three times in the last month.

“We have test campaigns with brands, retailers, small businesses and service-oriented businesses like plumbers, locksmiths and lawyers). Getting brands on the mobile phone is low-hanging fruit. Now, we’re trying to find out how to target, do reach and achieve performance. What are the tactics that work that can roll up into a strategy that makes sense? That’s the question. For example, how is weather affecting sales? How do you build an audience? Are we reaching them at the right time of day? How do we build location-oriented profiles? Is a red ad better than a blue ad?”

 3.  Attribution will remain challenging. “We have a way of isolating variables and can see what’s working. The hardest thing is attribution. What is the metric for success in any given campaign? We have the store-visit report which involves the people who have seen your ad on their mobile device — so what can we tell about the impact marketing has on the people we see at the store?

“We can track your location once you’ve requested an ad and do a before-and-after test. You can take half the audience that’s qualified for receiving an ad for, say, Arby’s, then leave half as a control group. The other half is the target, and that group receives the ad impression. We measure what the level of the lift is.”

4. Conversion remains a big issue. “That’s a big question mark: syncing IDs. Retailers don’t know who has redeemed [their] coupon.”

We are working on:

  • Greater transparency to share with customers. What are the learnings we’re getting from control/vs. target audiences?
  • How do you interpret that data for a marketer to use? How do you make the data so it can be used to optimize the campaign?
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