Duncan McCall is CEO & Co-Founder of PlaceIQ, a location-based data advertising technology company.
McCall discussed the latest regarding location-based advertising and his company, which announced a new round of funding in December.
Click below or scroll down for more:
- Location-based Services Beyond Mobile
- The Granularity Available In Location-based Advertising
- Sentiment and Location
- First-party Data Usage and A Use Case
- How PlaceIQ Generates Revenue
- "The Year of Mobile"
AdExchanger.com: Have location‑based services and targeting moved beyond a mobile advertising requirement or format? - I believe that's a goal you were talking about when we spoke last April.
DM: I'd spin that around and answer your question by saying what we've learned since we last talked is that our aspiration now is to be an audience company. And, we're already doing that. We're finding audience.
We’re extending that out from mobile into online, out‑of‑home and digital out‑of‑home. It begins with this concept that mobile is location‑aware and impressions often come with location on them. But, location is prevalent throughout many digital and analog advertising elements, whether it’s a billboard you're walking past or a digital billboard - be it TV, online, mobile or other elements. And, there's no real cross‑platform way to understand, segment and target audience across these different mediums.
If you're an agency, you have out‑of‑home, mobile and digital spend – plus, at least, three others. Then, you quantify an audience across these mediums. You know you’re hitting the same audience at home on TV, then out‑of‑home when they're commuting past a billboard. Maybe they get to work and you’re targeting them there as well - as in a privacy‑friendly, scalable fashion. The big goal we're working towards here is this concept of location because we all live in a physical world and so many of the digital elements we connect through are location‑aware now - and increasingly so as IPTV becomes more prevalent.
The big opportunity in location-based services is much broader than mobile. Mobile's a beautiful starting point, and there's heavy differentiation there, but there's a much bigger application of this throughout digital and offline mediums.
There's already single‑digit billions of hyper‑local ad impressions available right now. To address this concept of what granularity location comes in, if you look at mobile, there are two forms that we're interested in -but there are three or four forms that locations come in.
Starting at the most granular, or hyper‑local, is a smartphone, your typical Android or iPhone. You have a hyper‑local request on that phone. This is the pop-up which says, "Will you share your location?" That can be incredibly granular, so you can get down to city blocks and sometimes better. Sometimes it’s not so good, but really, that's the best data for us to use. That can come from the mobile web as well as a mobile app.
Also, from here, zip code can be captured in different ways. For example, triangulation, takes the hyper‑local request and changes it to zip code. Also, if a mobile ad impression comes in and it has an IP that lets me know that they're on wireless, I can take the IP address of that wireless and turn that into location using Geo IP - which brings us to the second piece of location-based advertising - online location. This is, historically, Geo IP, which was DMA-based and has slowly gotten better. Some people are claiming zip code [level accuracy], some people are claiming better.
There are some new companies that we're talking to and working with that are now starting to think about being able to get to hyper‑local, where applicable, if you're not coming through a proxy. If you're at home, connecting to Comcast or whatever the service is, there is the ability to reference you hyper‑locally in a number of different ways.
In the near future a large percentage of online impressions and IPs will become location‑aware to a much higher degree, which I personally feel is going to revolutionize online advertising. The demand-side platforms (DSPs) will be able to look at all these different IPs with real context and meaning, because someone like PlaceIQ can take an IP and say, "I can tell you the exact context of this user, in terms of where they live, or their neighborhood." I can assign a bunch of in‑home demographics, types of cars they own, the likely places they spend their time, if they have kids or not, etc. - all in a scalable and privacy‑friendly way because you're not necessarily doing it about an individual. You're still doing it at a slightly aggregate level.
So to summarize – from your view, what are the four parts to the datasets for location-aware advertising?
The way I think about it is the degrees with which granularity of location are made available to companies like PlaceIQ. There's hyper‑local, which includes mobile and online here. There's zip code. Then, there's DMA. In fact, it might be easier to stick with three, but there's one in-between where you can get somewhat accurate where you're getting toward “triangulation.”
The simple and easy example is Twitter. Even though the geo‑coded tweets are still a small percentage, it's now big enough to look at on a daily basis. Now on its own, that's not that interesting; however, if you start to look at the perspective and prism of location, I can say, "Let's look at people coming out of a rock concert." I know the location of the rock concert and I can ask, "What kind of sentiment are they expressing? What are they talking about? Are they're talking about brand? Maybe they're searching for certain things beyond Twitter?” To the latter, we can ask, "Let's look at mobile searches that are being done as well," which expresses a form of sentiment. Let's look at the kind of apps people are using as they come out. Are they searching for taxis? Are they playing games? That's what we're thinking of in terms of looking at sentiment with location.
So, if you start to look through location, you can start to add a tremendous amount of context, be it events, places of work or places of recreation. If you start to look at social sentiment on top of that, you can start to learn a tremendous amount in terms of the digital demographics of how people are reacting based upon certain concerts, events, activities, etc. We're starting to do some interesting things there, but we haven't applied that in a major way yet. Nevertheless, it’s on our roadmap.
Isn't “sentiment” almost out of scope for PlaceIQ?
It's funny you should say that because when I presented our road map to our new board just before Christmas, they asked, "Why are you doing social? It sounds like you are now undertaking social monitoring."
For us, it's just feedback into our core “place context” product. We are able to connect to an audience and a location in real-time with a message that we know is meaningful.
First, we don't deal with cookies. However, the concept of first‑party data in terms of, "These are our customers," or "These are some of the attributes that we know about our customers that you should input," we've certainly already worked with that.
Again, we work to drive it back to location, so even in our McDonald's campaigns, they told us about the types of users they wanted to be able to target. Everybody comes to us and says, "These are the types of users," and they may bring some first‑party data that we can ingest into our system.
In terms of how do we work with a BlueKai or a DataLogix, we don't do that because that's the world of cookies and - especially because of mobile - we don't want to go into that world right now.
Can you talk about a use case?
Sure. So in mobile, you would come to us and say, "These are our objectives. We want to target these types of people."
We would then translate that into a proposal that gives locations and time periods where there is a target audience. It might be events, in‑home, out‑of‑home and so on. It's times and locations, essentially. Then, we can go and buy that inventory on mobile ourselves, or we can work with an existing network to tell them what type of inventory to buy. That's how we identify audience.
If you think of the corollary for that – such as a BlueKai or DataLogix - those guys tell you: "You need to buy these cookies because these are the users who are the intenders for the new Toyota Camry" - or whatever. We say, "You need to buy these lat‑longs (GPS - latitudes and longitudes) at these times because these are the people who are the intenders for a Toyota Camry. These are the people who are in this location, who have a high propensity to that," Our cookie replacement, is location.
I just want to be clear that we haven't got to that stage “online,” yet. We've been mostly focusing on “mobile.”
The way to make money here can be put in three big buckets from our perspective.
One is we can run campaigns. We can buy media, mobile and online. Mobile is much easier because it is new, so we carved out a niche for [targeting in mobile advertising]. Somebody comes to us and says, "We don't see a lot of targeted ways to buy mobile." Instead of trying to go through a network and have us jump in, they can do it through us, and we'll buy it through a network or through ourselves through RTB. Then, we go and run it. So, that's the first way to make money; we will do less of that moving forward. That's one of those things you do to start with to prove you can do this, prove your data works or even own your own destiny.
The bigger opportunity for us is to enable others with this powerful targeting. So we hope to announce in the near future a couple of big network partnerships that we've been working on. We want to be able to integrate our data and systems into some big networks, as well as some big publishers. We want to empower them to sell audience instead of selling impressions and media. We want to be the best company in the world at turning location into context and everything that goes along with that. We don't necessarily want to be the best company in the world of running campaigns. Those are two different skill sets.
And the third way for us to to make money – and it’s early yet, but there's a clear opportunity - is by selling access to our data, tools, and insights. We've already done a pilot for somebody who had a lot of bar codes scans, for example, and they told us: "Literally, I've got these 3D bar code scans from the backs of magazines. One is for a cigarette brand and one's for a beauty brand and because every single one of the scans is hyperlocal from a smartphone, what can you tell me about my users?" So we can provide this hyper local context around bar code scanning.
I love those quotes. “The year of mobile” is a misnomer. What does the year of mobile mean? It was the year of mobile in 1997 - people started using mobile phones. Then, when AT&T came out with Internet mode, it was the year of mobile in 2001 because suddenly you could access the Internet through your phone in the US. That brought a ton of revenue and services.
When the Treo came out, boy, that was the year of mobile. When Blackberries came out that was the year of mobile because you got your email. The iPhone came out, well that was the year of mobile because it was a user‑friendly computing platform. Then last year, 2011, was the year of mobile because mobile advertising finally took off.
No doubt in my mind 2011 was the year of mobile advertising. We're now at that stage in mobile advertising where everybody understands it. Like any new medium, especially a medium for advertising, it takes a long time to mature and for those dollars to come in. But, in terms of performance, targeting and accountability - it's not there yet. That's not stopping people, but it's stopping some.
My final piece to that is admittedly self-serving, but it’s the reason I’m excited about mobile: it is about location and it’s personal. You have the device with you all the time. There's an opportunity to create an advertising ecosystem and an interaction with the consumer that's better than anything else in digital even with all that exists there. Yet still, right now, we get these horrific banner ads that are paid on a CPM basis.
My big concern is that we will train consumers to think about mobile advertising in the same way as online, which is to ignore it. However, mobile provides this opportunity to have a unique one-to-one experience. If we can get beyond the CPM baseline and show consumers something meaningful, we can start to raise the quality of advertising interactions so consumers are trained differently in mobile -that would be my aspiration.
By John Ebbert