The locus of the location based advertising industry with Thinknear founder Eli Portnoy

Can you imagine paying for a billboard, television or newspaper ad to target an audience in Los Angeles only to have it run exclusively in New York? How about wanting to target an audience sitting in a stadium in Toronto only to actually have your message seen in Kansas City? Welcome to the tumultuous and challenging world of location based marketing.

This challenge – the accuracy of location data – effects every single advertiser trying to grow their business via mobile. This challenge is also one that Eli Portnoy and his team at Thinknear have been trying to solve since their inception.

Since the launch of the Location Score Index, Eli has made it his mission to create complete transparency in the location-based advertising world. Doing this requires asking hard questions of our established norms and sometimes these questions make us a little bit uncomfortable but they are necessary – as is the service the Thinknear team is providing.

If you rely on mobile ads to help your business, this is required viewing…unless your goal is to fritter your cash away in New York as your company goes bankrupt in LA.


Key takeaways from this episode. Click on the link and the video will take you to that clip

1. What is the most surprising thing you found in researching the location score data? 2:20
2. What is the big issue you uncovered? 3:30
3. What are the expectations from location based marketing? 4:45
4. what are the variables to get great location based marketing? 6:40
5. Why did Thinknear build this? 8:00
6. Are advertisers selling snack oil? 10:10
7. What is the difference between “good” data and “great” data 13:30
8. What is the “dirty little secret” in mobile advertising? 15:20
9. Why will “click thru” rates become valuable to mobile advertisers? 18:15
10. How did you come to the overall location score average number? 19:45
11. What are the characteristics of apps that do it well? 21:10
12. What are the 5 different ways to capture location 22:55
13. Why does the data suck? 26:25
14. What questions SHOULD we be asking to ensure the location is accurate? 27:35
15. What was the response to the Location Score Index? 29:20
16. How is Thinknear helping to solve the issues that were discovered with the Location score Index? 32:30
17. How much has this data changed Thinknear’s business? 35:00


Raw Transcript

Rob: This show is brought to by our sponsor, bitHeads. They are a staple in the tech community I come from and have done incredible work over the past 18 years with some of the largest brands in the world, including The Simpsons Tapped Out, Box, Optimal Payments, The New York Times, among many, many, many others. All told, they’ve built over 500 solutions from enterprise to entertainment. I’m proud to have them as a part of Please support us by supporting them. Go to
Hello everybody and welcome to I’m your host and founder, Rob Woodbridge. Context, oh, it’s the hope of the marketing industry right now. Context, with it you can serve customers what they want, when they want it, and where it is convenient for them. You see, at the crux of context is this thing called location. It seems easy enough. Pinpoint where people are and send them an ad that is relevant to them at that moment, but as we’ll see here it isn’t that easy. And in fact, our current offerings aren’t faring very well at all.
Today we have Eli Portnoy who is the president and GM of Thinknear, a company you guys who are longtime listeners should be very familiar with. He’s been on a number of times before this. His firm has released the first tool to measure location accuracy in mobile advertising, the location score index, and the results are, well, how would you say, appropriate for the industry still finding its way.
He is here to arm you with the information you need to know to help you make the right decisions about bringing location-based marketing into your business. Eli, welcome back. I guess I haven’t scared you away because you came back again.

Eli: No, I love it. You can’t scare me away. This is amazing.

Rob: Well, thanks for joining us. You know, I think industry, this report, and the clarity that you guys have put into this report, and the understanding of where we are in this industry, I think we all owe you a great deal of thanks and from advertisers and marketers. And I think that after this if you’ve never heard of what Eli’s been up to with this, you will thank him as well. You’ll be forever indebted to him, but I’ve got to ask. What’s the most surprising thing you found out while doing this location score index?

Eli: So to me the most surprising thing is just how little transparency there was in the marketplace. So we’ve been talking about the issues in location accuracy for a while, but because we hadn’t quantified it for people I don’t think marketers and advertisers really understood the level and just how big of an issue it was.
I think marketers in general are starved for transparency. There’s so much room for manipulation and all sorts of other stuff when it comes to the ad tech industry. And so when we came out and said, “Hey, we’re going to totally open up the curtains and show you exactly what’s going on and create this location score index and give people really quantifiable and real data about the magnitude of the issue.” I think people were just really excited, and we didn’t expect how much transparency was missing and also how much excitement there would be for this information.

Rob: Well, the big problem is that the ads that are being served that are supposed to be targeted to a location are all over the place, aren’t they? How do you classify the big issue with what you guys have uncovered here?

Eli: Yeah, I mean, it’s exactly that. Advertisers are paying money to target someone because they think that someone is in location A and that person can be very, very far away, We categorize the location scoring to five components and everywhere from accurate within 100 meters, which is fantastic, all the way to accurate within 100,000 meters and more.
We have examples where an advertiser thought they were paying to target someone in a sports stadium in Los Angeles, and when we run verification tactics to figure out where they’re actually running, they’re all the way out in New York.

Rob: What?

Eli: Let alone not in a sports stadium, but in a totally different side of the United States. So it’s a pretty significant chunk of the inventory. When we actually broke it out, the total industry came out at a location score of 49 which basically means that close to 70%. It’s actually 64% of all inventory was not accurate within 100 meters, and that’s just really, really bad.

Rob: Is there kind of a written service level agreement for these advertisers, or is this just understood that when you say location-based ads that it would display within that 100 meter range? Or is there a SLA that we can adhere to at this moment?

Eli: Well, it really depends, and it depends on what the advertiser is trying to do. So in many cases advertisers are trying to use location for proximity’s sake. They’re saying I’ve got a retail location, or I have a restaurant, or I have X location that I want to target people around. They’re using location to say okay, anyone who’s within two miles or three miles, and that’s essentially the SLA. The advertiser is asking for targeting within three miles of that location, and that’s what the advertiser is promising to deliver.
When it comes to geo-audience or this idea that by understanding where someone is you can understand a whole bunch of things about what kind of person they are and what they might be interested in, that’s when the granularity of the location becomes even more important. Because all of a sudden you’re saying I want to target someone in that sports stadium because I want sports enthusiasts, or I want to target someone in a car dealership because I want auto intenders.
At that point 100 meters is really the least granular you can get, because anything outside of that and the person is no longer in that location. That’s why the 100 meter example is so important.

Rob: You know, there was a term I was just trying to remember. It was like geo-hacking or geo-targeting. It’s not geo-targeting. It’s geo- hacking or something to that extent.
There was this trend going around where retailers would target somebody in their competitor’s store with an ad, or at least that’s what a lot of companies were selling. In your opinion, with a 49 out of 100 score for location targeting, would that be possible 100% of the time? It seems to me right now based on this conversation it’s impossible to target somebody accurately all the time, somebody in a business across the street when you’re trying to woo them over to your business.

Eli: Yeah, it is possible. The great thing about location is that when you do it and you do it well it can have outsized returns for advertisers. It’s incredibly impactful to actually fulfill the promise of right person, right time, right location.
The problem is it’s not easy at all. There are so many variables that go into getting location right. Those variables include the phone the user is on, the OS it has installed, the app they’re using, the settings they have on their phone, whether they have clear line of sight to a GPS satellite.
There are lots and lots of things that go into getting really good location. The first step in ensuring that you’re getting that good location is to understand those components and to build in the tools to be able to make sure that you’re actually delivering on that great location.
When we looked at the industry we started to see that there’s a whole bunch of data that’s not good. By the way, that data didn’t just happen automatically from one day to the next. It’s actually been a progression as the industry has evolved. When we saw that progressing and we saw it was becoming a real problem we started building in all these tools internally so that we could deliver good location versus bad location and make sure we were doing the right thing by our customers.
What we found was hey, we’ve got these tools internally that we built. Let’s productize them and basically add transparency to the market and let everyone see what we’re seeing. That’s sort of how location score came to be.

Rob: Is this a pink elephant? I mean was this something that people talked about, or was this something that was just hidden or that was in plain sight but nobody wanted to talk about? Why you guys? Why did you guys have to come up with this in order to be able to shine a light on it?

Eli: Yeah. I do think that folks understood that this was an issue, and I think different players in the space dealt with it differently. Some of them came out and tried to build products and tools to do the best that they could to deliver good location. Others tried to ignore it.
I think ultimately the reality is that we made a really concerted effort in solving this. Based on our background, the reason why we started Thinknear was we initially wanted to do a totally different business that would plug into the location based ad networks. When we plugged into them and we started buying inventory from them we realized that we were not getting this precise location that we wanted.
The whole reason we started Thinknear was to deliver on precise location. We start doing that and we start seeing some issues, and then on top of that we get acquired by Telenav that has been building GPS solutions for the last 14 years.
By the way, when you build GPS solutions for consumers the threshold, the quality bar, is so much higher than in ad tech, because you can’t tell someone who wants to get to a restaurant that it’s on 50-ish avenue. You actually have to get them there.

Rob: Did you say on 50-ish avenue?

Eli: 50-ish, yeah.

Rob: That’s great.

Eli: You can’t do that.

Rob: Unless you’re Apple . . .

Eli: [inaudible 0:09:25]

Rob: . . . first generation map. Kind of approximate where you’re supposed to be. It could be in the middle of a field. You’ll get somewhere. It might not be where you wanted to get originally. You’re right. Doing consumer location, especially for directions, is not easy, as we found out very, very often.

Eli: Yeah, exactly. Having Telenav behind us, and looking at the data they were seeing, and having sort of grown up trying to solve this problem, it just became our core focus through and through. Knowing that we’re spending all of our resources on this, and that we’re really building sophisticated tools to solve this, and that the market is just not aware that this is an issue, it felt like, hey we’ve got to add transparency to this market and we’ve got the tools to do it. So, that’s sort of why we went out and did this.

Rob: But doesn’t it seem to you that the guys that are out there selling this, and I don’t mean to paint everybody with a single brush because there are companies that are doing this exceptionally well and then there are companies that are doing this exceptionally poorly, but is it kind of like snake oil?
When I read the report and I saw the numbers and I saw the accuracy numbers between, what was it, 60 meters and 60 miles? You’re supposed to be in LA in a stadium and you’re in New York somewhere. It sounds like they’re selling snake oil at that point. It leaves me feeling a little greasy, you know?

Eli: The thing that’s important to remember is that location actually is really, really hard and a lot of the folks that are playing in this space are not location experts. So, you’ve got publishers and you’ve got app developers and you’ve got guys who mean well and are trying to build the right tools and the right system for their core customer, which is not necessarily an advertiser. And then on top of that they’re selling advertising against it. They don’t understand the intricacies and the nuances of getting good location and so I think a lot of times it’s good players who just aren’t informed.
One of the things that we’re actually doing with location score is we’ve built a score card for all of the inventory sources we work with where we show them: this is your location score. This is what it looks like compared to the rest of the industry. This is what it looks like compared to your competitor set. On top of that we built a whitepaper that gives them all of the tools they need to figure out how to pull better location. So that’s one thing. I think there’s just a lot of missing information and education in the market place around how to do location right.
But then on top of that I do think that there is real money associated with location and when there’s real money there’s sometimes perverse incentives and I think we have seen a bunch of that. I would say that it’s not just snake oil. There is some of that; there is also misinformation and lack of education. Ultimately, probably the most important piece and the reason why this has persisted is because the lack of transparency. Advertisers and agencies didn’t know what questions to ask and so without people asking for the real thing, it just made it really easy for this to continue.

Rob: I like that answer. I think it’s safe. But there are those guys who shouldn’t be selling this. As you said, it’s not their business, they don’t understand it, they don’t have somebody like Telenav who has the history, the rich history of location and understands location data. When you hear something like this I think the key thing is exactly what you said which is that we weren’t asking the right questions, right?
So I asked early on in this whole very early on in this whole mobile marketing location based marketing world is that I don’t believe that a 1% or a 2% click-through rate, which is what people in the mobile industry were looking at mobile advertising at that point. They were looking at click-through rates the same thing as the web and they were saying “look, it’s a 30 times greater click-through rate then the web.”
And so a 1.5% or a 2% click-through rate seemed amazing for mobile and my argument was that no, it should be a 98% click-through rate if you’re getting context, location, everything absolutely accurate, you should be hitting 98 to 100% click-through rate. You might only get 9 people to click but they are the 9 customers that you want. People looked at me and said, “You’re out of your mind. 2% is great.”
But, I think that what you’re talking about here is that’s when you do it all right the impact should be absolutely amazing when you’re doing it right. So, what is the difference between good location data or subpar, maybe good, average and great location data? What can that mean to a business?

Eli: That’s a great question. That’s just such a phenomenal question because that really gets to the heart of whether this even matters. We could talk about doing location really, really well but if it doesn’t actually impact performance, then who cares.
One of the things that we did when we started talking about the location square index is we ran a whole bunch of different tests both with ourselves and with third parties to understand the impact of location score on campaign results. We were looking at four different metrics to understand the impact. We looked at click-through rate, we looked at time on site, so when someone clicks on a banner, how long do they actually stay on the landing page. The way we differentiated was anyone who stayed over five seconds meant they were interested in the content; anyone who stayed less than five seconds either bounced off, accidentally clicked, or just didn’t care.
Then we also looked at how many people actually went into the retail location. If it was a proximity-based campaign, how many people actually ended up going into the venue and we worked with Placed [SP] as a third party to actually run this campaign. The last thing we did was we used our own metric which we called Drive to Rate which is using our own GPS driving navigation solution to see how many people initiated and went on a route to get to the location.
The really interesting thing is that we saw huge lift on time on site so 45% more people stayed more than five seconds when a high location score was used, versus a low. We saw similar results across all the verticals when it came to in-store visits and drive-to rate.
The amazing thing was, we didn’t actually see much of a list on click- through rate. We started looking into. This brings us to the dirty little secret about click-through rate and mobile. That dirty little secret is that the highest influence on click- through rate is the placement of the banner in relation to the buttons.
You look at apps, and the highest difference in click-through rate happens, app by app, based on that placement of the button. That’s why marketers know accidental clicks are a huge component of mobile, and why, sometimes the click-through rate gets high or low. What I would encourage marketers, is to really look at deeper KPIs, because those are indicative of real value being delivered. We’ve seen that time and time again, and I think location score is another good indicator of that. That is an issue.

Rob: Click-through rate is not an attribution rate. I think that’s the key there. As you said, that’s interesting.
I’ve seen guys–and we’ve talked about this on a number of shows–put a banner ad with a little X in the corner. You think you can actually close it, but by clicking on it, you’re clicking through–which is terrible. They put it up above their navigation, as you say, and the accidental click-throughs when you’re above navigation or below navigation is astronomical, especially if you’ve got fat fingers. I think there’s nefarious activity with the click-through rate.
I love the idea that you’re talking about. You’ve got to understand the cycle, from the time they see an ad or hear an ad, to the time they walk into the store, and the impact of that ad in context to location, time of day, and all that stuff that we’re talking about here. Click-through is not an attribution rate. It should never be that. Maybe it’s one piece of it, but it’s a weighted piece. Is that accurate?

Eli: It’s a proxy. It’s an interesting proxy. I think we’ll get to a point where click-through rate becomes more meaningful in mobile. I just think that we have to find a way to get around this accidental click, as a way to measure it. The meta-point is that the location score really impacted performance metrics, and if you take out the accidental clicks, it impacts click-through rate as well. It’s just harder to see at a top-line level.

Rob: I love it. I interviewed, recently, a company called XAPPmedia. These guys do interactive radio ads, Internet radio ads, where it says, “Hey, if you’d like to download the app, say ‘Download right now.'” The human says “Download,” and then it automatically either sends you a link to the download or downloads the app, depending on the platform.
That’s an attribution rate. That is clearly . . . You can see action as a result. It’s not a click-through rate. It’s, “I have spoken, as a human, to this machine, which is my phone, to tell you to do something.” That is a really powerful way of attributing something to it. As we start to experiment in this world, attribution becomes much more clinical and clear than a click-through rate.
Click-through rates, I’ve hated them from the moment that they were on the web. I hate that now it’s on mobile. I’d be interested to have a longer conversation on why you think that it will actually, at some point, become a valuable component to this.
Let me ask you, why do you think that it will happen, where click- through rate will be valuable for mobile advertising?

Eli: From a brand’s perspective, when you’re running a branded type of campaign where you’re not actually trying to get someone to download something, or to go into a store, there needs to be some measure to understand how interested or excited someone was about seeing that message. There are brand lift studies, and there are a whole bunch of different ways to measure it, but click-through is a pretty clean way, if you can take away all the dirty data around accidental clicks and fraudulent clicks. It does mean that someone saw the ad and was interested enough to click on it.
Marketers really like it, because it’s a really easy number to understand. If we can get around some of the issues that currently exist in the marketplace, it’s not a terrible method, although I still love the attribution metrics a lot more, clearly.

Rob: What do you think is the . . . I don’t know if there’s an answer to this. Is there an industry-acceptable accidental click-through rate for mobile banner ads and mobile advertisement?

Eli: I don’t know of one.

Rob: I’ve been trying to find it. Maybe, is it 25%? Is it 10%? You can put this blanket over the top of it, and say, “Look, a third, a tenth, 25% of all click-throughs, by accident.” Then you know how to level-set or adjust your expectations. If anybody out there who’s listening or watching this has any data on that, I’d love to hear you. I’m sure that Eli would as well. It’s a baseline.
You said that the number that you gave overall was 49. That’s 49 on 100, in terms of accuracy and acceptable use. How did you come to that number, the 49?

Eli: We wanted to devise a methodology that was really simple to understand, and a counter for the two components that matter in location accuracy. So, the percent of inventory that’s accuracy, and the inventory that’s not accurate, how inaccurate is it?
Basically, you don’t want to penalize someone who had an impression that was 100,000 meters away the same way you would penalize someone that was 150 meters away, but doesn’t fall into the 100 meters. So, we basically came up with five categories. So, hyperlocal being 100 meters and the next category is 100 meters to 1,000 meters, and 1,000 to 10,000. Then 10,000 to 100,000, and then anything worse than 100,000.
We weighted them differently based on . . . If you’re within 100 meters, you get a full point. If you’re not, you get a little bit less. If you’re further, a little bit less. We wanted to come up with something that was easy to understand, easy to replicate, easy for everyone to use. When we built out this algorithm and applied all of the location data we had bought across all of the different inventory sources, we came out at a 49.

Rob: So, there’s definitely room for improvement, wouldn’t you say?

Eli: I would say there’s quite a bit of room for improvement. Yeah.

Rob: What are some of the companies? Do you feel comfortable naming some of the companies that are doing it very well? Or not doing it well? Or apps that are doing it well?

Eli: I could tell you some of the characteristics about the apps that are doing it well. So, the apps that are doing it well are using location to power part of the customer experience. So, they want good location so that they can create a better experience. And then, they’re also using that location for advertising purposes.

The reason those guys tend to do it really well is because they need good location, otherwise their consumer just doesn’t work. So, they’re actually becoming experts in how to get good location. So they’re doing all the right things to get that location well.
The folks that do it less well are where location is just an advertising piece of data, and they’re just doing it to generate revenue. So, for the most part, there’s just not nearly the same level of care and effort in getting that good location. I would say that would separate the great from the bad.

Rob: You know, it’s interesting. I love that. If location is a core piece of your business, that obviously makes you an expert in finding the right people for the business that you’re in. But, if it’s a tack-on, if it’s an add-on . . . Like, I know some of the newspapers here in Canada, their apps, location doesn’t . . . They call themselves location data, but it’s really not, right?
It’s just basically geofenced. Like, if I’m in Ottawa and I go to Calgary, then they’ll just show me Calgary ads instead of Ottawa ads. That’s lazy. Location isn’t their business. I see a lot of that in the industry. But it is the experts. I think you have to understand the multiple ways of capturing location, there? Because it’s not just about GPS. And it’s and just about one piece of this.
There are many ways to capture location. I saw in your paper, there’s maybe five different ways that location is captured? What are they?

Eli: Yeah, absolutely. So, there are five different ways, and each one is more accurate than the next. Probably the best way to think about it is like a waterfall. The most precise and accurate is assisted GPS. That’s basically using the GPS chipset and the Wi- Fi sensors. And the GPS chipset is looking for a triangulation of the GPS satellites. And then on top of that it’s using Wi-Fi and triangulating the Wi-Fi signals that it’s getting to get the most precise, quickest GPS readout.
Then GPS comes next. So, GPS in isolation without Wi-Fi. Then using just the Wi-Fi towers without GPS. Then if the app developer can’t do this, or it doesn’t have permission from the user, or it didn’t ask for permission, or a whole bunch of other reasons. Or it couldn’t get a good line of sight to the satellites, it can revert to cell tower triangulation, which is looking at the cell towers and trying to figure out place. And that’s not all that accurate.
If that doesn’t work, you can use an IP reversed geocode. So, looking at the IP address and trying to figure out where that IP is originating from. That’s really inaccurate. At a best case level, that’s 60% of the time accurate. And when it’s accurate, it’s accurate to within 1,000 or so meters, so not all that great.
The final thing that an app developer could do is it could use a reverse lookup of the ZIP code or address that was used to register. The problem with this is, at its most precise level, it’s precise within a ZIP code. But, far worse is the fact that it’s a static reading. I registered for a bunch of apps when I lived in Boston seven or eight years ago, and I still see ads from Boston whenever . . .
And so, I live in Los Angeles now. I haven’t been to Boston in a really long time. That’s highly inaccurate.

Rob: At least it’s a start, right? The location? Then it’s on them to make sure that your data is up to date. But that’s the stack. When you talk about location, that is the location stack, right? And from super accurate to not so accurate, and I think that I remember reading this great book called “Killing Pablo”, which was about killing Pablo Escobar, about how they managed to kill him, and they relied on cell triangulation. Right, but Escobar made the dumb mistake of calling somebody, and I think the call lasted for 15 seconds, and the America Troops descended on him and shot him to death, right?
I think that it’s this perception of cellular triangulation, or cell tower triangulation, is adequate enough to get to this point where if they can kill Pablo Escobar, who’s one of the most notorious drug leaders ever by cellular triangulation, or cell tower triangulation, that it would work for you know a retail outlet. I don’t even know how I can segue over here, but from Pablo Escobar to . . . but you know what I mean?
But I just want to demonstrate that there are levels here, and complexities here, and fallback measures here that have to be in place, and if you are out there trying to buy ads, location aware ads, contextually aware ads, you have to know that its, that this is the stack, and this is the sequence with which you have to follow in order to get the accuracy that you deserve for the money that you’re spending.
I don’t know how I brought Pablo Escobar; it was the first thing that came to my mind when you said, “Cellular triangulation,” or, “cell tower triangulation.”
So you know, but why does the data suck? Is it just, is it just the state of the industry, it’s early, or is it laziness, or is it do we not understand really what it means to triangulate down to the list of the complexities? Why does the data suck so bad?

Eli: Yeah, well I think it’s a bunch of different things. So I think it comes down to the education incentives and transparency. So on the incentive side when we first started doing the location-based mobile advertising only 10% of impressions had a lot long. It’s now 67%, and the reason it’s 67% is because advertisers are asking for it, they were paying more money for it, there’s incentive for publishers to pass it, and so the guys who had it who weren’t passing it are now passing it, which is fantastic, but it’s also created incentives for folks who didn’t have great location capabilities to still try and find whatever they could, and for location in any way that they could, and pass it, so that’s one thing.
The second thing is about education, a lot of publishers would just get the right location but they don’t know how, and the last thing is just around transparency. So advertisers don’t really know what to look for and what to ask for so they’re not asking the hard questions so they’re not making sure that they’re getting the right things and so all three things combine to make it a pretty big deal right now.

Rob: So what should, what should they be asking? Are the questions that they should be able to ask in order to be able to protect themselves, or to get the value for the dollar they’re spending?

Eli: Yeah there are definitely questions. So the first things that I want to clear up on the misconception. A lot of times when I talk to advertisers and agencies they say, “Well we know we’re getting good location, because we’re only using lot long for our advertising campaign so that’s what our network tells us.” Lat long does not mean it’s accurate. A lat long is just a representation of a point on a map, the, what matters is the source of the data, the source of the lat long, was that lat long derived from a zip code which was then converted into a lat long for the center point of that zip code, or was it actually gotten from a GPS readout with a high degree of confidence.
And so I would implore advertisers, and agencies, and marketers to stop asking about whether the data is in the form of a lat long and ask what is the source of the data, how are you getting that data, how are you cleaning it up, what are some of the instruments and tools that you’re using to make sure that you’re only buying great lat long for us, or using great lat long for us?
So source of the data is really important, how you’re cleaning up that data is really good, and what transparency can you offer for me. So how can you make me understand what was good and what was bad, because it’s not possible to get 100% totally accurate location, but it’s very possible to get in the 95, 90 percentiles. So the question for advertisers should be asking is, “How can you show me where I am and where I’m not?”

Rob: That’s a great question, and if they stutter, or stall, or don’t have an answer for that turn around, run away, right? But when you did these studies, and you provided this data, the grades basically, on some of these ad networks, what was the response?

Eli: So marketers and advertisers were really excited to see this data. I mean this is stuff that they, I think they sort of understood but they never really seen quantified so there was a lot of excitement. I think within the industry folks are asking me why I’m shaking the tree, you know . . . This is asking, causing people to ask hard questions.
And my answer to that is I’m not interested in seeing location based advertising be a fast growing industry, that’s not where I’m at. I want to see it become a huge, incredibly productive, high ROI industry that’s helping everyone, and the only way we get there is if we add transparency, and we build the right tools, and we make sure we do this right. There’s no reason not to actually go for that real thing.
Location actually matters in mobile advertising and the more we clean up the industry, the more everyone will realize that and the more money will flow in and the bigger we can build this thing. And so, we’re really trying to build a very, very large market here and the only way to do that is to do it right. And so that’s why we’re doing it.

Rob: People are asking me hard questions that I need to answer, why did you do this Eli, why did you guys? Wah, wah. I love that and I think that there was . . . I think that the way you did this was very adroit, was very politically correct. I think that in a couple of years if we’re still struggling with this industry, I would implore you, maybe in a year or six months, I would start to do the ad network index where you grade them from top to bottom. This is the best ad network, this is the worst.
And you give them all a grade and you make that all very public so that people are held accountable, because if companies are spending good money with these guys then they deserve a product that is actually in line with the money that they are spending.
So, if you’re cheap, go with a crappy network but you know what you’re getting, into. And I think that that buyer beware, the Better Business Bureau from ad networks is something that you guys could possibly do, but I love that fact that you’ve taken a Canadian approach, which is we’re going to send out 470 warning shots, but the 471st, we’re going to publish your name. And I think that this happened and you want to drive the johns and the pimps and the prostitutes out of this business.
So, here in Ottawa, and I think it’s across Canada now–and I’m not, I just want to clarify that–when you get busted for trying to pick up a prostitute, your name gets published in the newspaper, it gets published on websites. So, it is a permanent record for you guys and then you stop doing it. So, that I think the approach here, soft at first, maybe a little bit harder later. Is that something, did you consider that?

Eli: So, we definitely don’t want to call specific networks out just because I want to believe that everyone is trying to do the best that they can and is working hard to solve these issues. What we will do, and we feel like this is really important for advertisers and agencies and marketers is we’re launching a tool called Location Score Tags.
And what Location Score Tags is, it’s a verification tool that any advertiser can use across any of the campaigns they run, whether with us or with anyone else and they’ll get a location score out of their campaign. So, they’ll actually be able to use this to get that transparency and get that accountability. So, we won’t publicly name anyone but we will build the tools so that advertisers can get that level of transparency and insight.
And it’s a totally free tool; we’re not going to charge for it. it’s all on the same line of thinking that if we build transparency, we build the tools to give transparency, folks will be able to compare us to the rest and they’ll naturally come to us and if not, then at least it builds the entire ecosystem and helps make sure that everyone else is doing the right thing by customers. And I think ultimately everyone wins this way.
So, I’m really excited about the location Score Tags and my goal actually is that once we roll it out and we put it out there, a couple months after, I’d like to open source it or give it to a third party and let them take it. Because clearly there’s some inherent conflict of interest and I don’t want any of that clouding marketer’s ability to use it. So, we’ll, launch it and then give it to someone to run with it.

Rob: I love that and this is something that came as a result of the reports that you guys have been doing?

Eli: Yeah, absolutely. So, we had a lot of requests and questions about, “Okay, I get that there’s an issue, help us solve it.” And this is our way to solve it. It’s just, “Okay, well we can’t actually go in and solve it across everyone, we’ve been solving it this way, we’ll share that information, but we’ll also share some tools so that you can compare apples to apples.

Rob: I love that and this is something called Location Score Tags and they’d be able to find information about that just to go to

Eli: Yeah, they can go to or they can go to and they’ll find information there as well.

Rob: It’s so fascinating because this is how complex this industry is and I keep coming back to this, I keep harping on it. But, it takes a company like you and the sequence with which happened with Thinknear which is to build the business, evolve that business, become a part of something like Telenav, have the resources to be able to then start asking deep profound questions about the industry, which may not be able to happen with a startup when you’re always running at a million miles an hour.
So, you’ve had enough time to be able to sit back and then go through the sequence and then be able to take something like this and open source it and make it part of the community.
But I have to ask how much has this changed your business, the revelations that you guys have found from the location score index and then to kind of build something on location score tanks? How much has this changed Meers [SP] thinking around this industry?

Eli: Well it’s definitely highlighted for us the need to do the right thing. It’s highlighted for us the need to drive the levels of transparency that we think are appropriate. I personally just enjoy it more because it’s the way I like to operate and the way that . . . I don’t think . . . I think advertisers and marketers want us to be operating so it’s just . . . it’s also been a rallying cry inside our company, to take you inside the minds of the people who work at Thinknear. It’s just been really, really exciting for everyone to not just have a mission of growing a business but doing something right by the industry and that’s been really, really fun for all of us.

Rob: It sounds like you guys are shaping this industry. I mean, I don’t know anybody else who’s talking in this language. From the very first time that we covered you, I think Chuck Martin saw you speak at Aquino [SP]. You went to one of his OMA [SP] events and gave, he said, one of the best presentations he said he’s ever seen about this, and his jaw hit the floor because of what you guys did with the location score index.
And I think that evolves into something like location score tanks, and it can’t help but do good for this industry which is something that not a lot of people in this fast paced world are willing to do. I love the way that you put it is that we’d rather . . . we don’t want to grow fast, we want to grow a big industry.
And in order to do that you have to set the foundation. And that’s going to come back to the service level agreements, is that as you’re buying advertising and you’re trying to do some contextually aware location based marketing you have to feel comfortable, confident, that whatever it is that you’re spending your money on is capable of delivering the right message to the right person at the right time.
Because if you don’t then we’re going to be back at this ridiculous rate of 1% or 0.05% click through rates, and we’re going to be disappointed, and we are going to miss the opportunity that is mobile marketing. Is that a good way of summarizing it?

Eli: I think so. I think it’s exactly the right way to think about it. I love it.

Rob: Well all right, so we’re going to send people to

Eli: Yeah.

Rob: Or, one of those two? You’ll arrive at the right place? Because right now if you go to you can actually click through and register and get yourself a copy of the location score index. And if you’re spending money in location based advertising I would . . . I can’t even . . . stop listening right now and go do that, simply because it will be an eye opener for you.
Start asking those right questions to that . . . your provider and your advertising company and the company that’s doing the advertisement, the company that’s providing the ad slots. Just ask questions. Don’t take . . . don’t go on blind faith. And then even though it killed Pablo Escobar and I brought in prostitution in the same presentation you’ve been very accommodating and I appreciate that.

Eli: No, it was fantastic. Thank you. Thank you, Rob.

Rob: So go to or Once again we’ve been speaking with Eli Portnoy, who is the GM and President of Thinknear. He was the founder of Thinknear, acquired by Telenav a couple of years ago, and one of my most popular guests. Eli, I thank you for coming back one more time.

Eli: Thank you so much, Rob. Thanks for having me.

Rob: You folks out there, who are listening, watching, wherever you may be, physically, emotionally, wherever it is, hopefully you are driving in your car listening to this, or you’re at the gym doing something productive, thank you for coming by. Thank you for listening to this episode, and we’ll see you next time on

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About Eli Portnoy
eliportnoysmAs General Manager of Thinknear by Telenav, Eli Portnoy oversees the only mobile advertising network focused on using location more intelligently to deliver unique audiences, better consumer experiences and deeper location insights for advertisers.

A thought leader in the mobile advertising space and a vocal proponent of making location the driving factor in differentiating mobile from other digital advertising platforms, Portnoy has written for and been quoted in numerous publications including TechCrunch, Digiday, MediaPost, Street Fight Magazine, iMedia and Forbes. Portnoy is also a frequent industry panelist and focuses on evangelizing the use of intelligent location technologies in the mobile advertising space.

Portnoy co-founded Thinknear as part of TechStars, the elite technology incubator based in New York. In 2012, Portnoy oversaw Telenav’s acquisition and integration of Thinknear, the largest successful exit of any TechStars venture to date. The acquisition gave Thinknear access to more than 14 years of Telenav’s location data—something that no other mobile advertising company can match.
Prior to Thinknear, Eli was part of the senior product management team for Amazon’s digital video group, where he focused on connected devices and subscription products. Eli has been named a “Top Young Entrepreneurs” by Businessweek and ranked 21st “Coolest Young Entrepreneur” list. He earned a BA from the University of Pennsylvania and an MBA from Harvard Business School.

About the author

Rob Woodbridge

I'm Rob, the founder of and I've spent 14 years immersed in the mobile and pervasive computing world. During this great time I've helped some of the most innovative companies grow their business through mobile. If you are in need of a mobile business advisor or coach, connect with me here to get things rolling.

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