Duncan McCall is CEO & Co-Founder of PlaceIQ, a location-based data provider.
AdExchanger.com: Can you talk a little about how your experience “fed” the idea for PlaceIQ?
DM: I have always had a passion for the location space. I have travelled to many interesting places and many years ago went through the experience of downloading waypoints from the internet to load onto my GPS device, to aid navigation across the Sahara Desert – it was fascinating experience and ever since then I have been thinking non stop about the intersection of location and digital information.
Professionally I cut my teeth in the broad ‘location’ space while working in RFID and Universal positioning startups, and then went onto to found a consumer location content business. So it just seemed logical to me that as the amount of digitally created location information increased there would be some very compelling opportunities to extract meaning and context from this data, and apply it in making better business decisions.
What problem is PlaceIQ solving?
At a high level the problem we are solving and the opportunity we are addressing is creating a dynamic (meaning time sensitive) understanding of a hyper local location (down to 100M tile or about a city block) – the types of people, place type, social and digital activity, intent etc… so a detailed understanding of the actual dynamics of a location, and how these change over time. We do this in a privacy friendly way, as we are not targeting individual users through their personal information, but more so looking at the anonymous signals from a location, and inferring conclusions based upon these. Gaining this new level of insight and understanding into a location opens up a tremendous set of opportunities across a number of markets.
While we are actually engaged in a number of markets from the optimization of local search and online display through to digital out of home – the primary market we have ended up working closely with has been the mobile advertising ecosystem. At a high level with the ever increasing amount of location aware mobile inventory, by using PlaceIQ data a mobile ad network or similar, can now turn a sometimes meaningless set of location data, into meaningful context. This enables the ability to serve a more relevant ad, or empower the demand side to buy the right media that targets a specific type of users at the right time and right place.
A couple of quick examples:
A mobile ad network using PlaceIQ data can uncover additional context about impressions without mining any personal user data, and use this context to serve a more relevant ad. For example PlaceIQ data might reveal one impression to likely be a user with an affinity to outdoor recreation, (since their location might be a trail head or popular hiking spot) so a relevant advertisement to this user would likely be fitness or outdoor related. Another user might score very strongly towards tourism (due to their location scoring highly to tourism at this time), and PlaceIQ might uncover a strong intent in this location for hotels and rental cars at this time. Therefore this would suggest an advertisement of that type might be the most relevant and performant to a user in that location, at that time.
Another example might be an advertiser looking to reach a certain target market of consumers. By defining specific characteristics of the target market (demo or psychographics, lifestyle, interests, hobbies, intent etc…) PlaceIQ can create affinity scores from location to these specific characteristics, meaning specific locations will score higher or lower dependent on their propensity to contain the target audience at certain times. These resulting location scores can now be run through a mobile ad
network (whom has access to location inventory) and will enable the network to buy the right impressions to serve ads to the advertisers target market at the right time and right location – resulting in much more targeted and performant media spend.
Do you see any competitors in your space? Any thoughts on PlaceIQ’s differentiation?
It is a fairly new space, but growing rapidly and so we are sure we will see no shortage of competition in the near future. While the broader opportunity is very large and able to support a number of firms, we honestly feel in one way or another our whole team have been working on aspects of this problem for many years. It’s important to note a huge amount of work has gone into making sense of the large amounts of data we process, we’ve built a taxonomy with tens of thousands of mappings, and spent months tweaking it with inputs from various machine learning routines – and alot of manual effort! Presuming we continue to evolve our algorithms successfully with input from real world conversion metrics – once we hit enough critical mass there, the ability to build a very powerful ad recommendation system from the perspective of location, has the ability to be an extremely strong asset.
How would you label the company?
Fundamentally we are a data supplier. We have no plans or aspirations to become an ad network of any description. Our goal is focused but its application very broad – we want to be the leading supplier of intelligent and contextual hyper local data to empower businesses to make better decisions.
Is “real-time” a part of your plans? If so, how?
Our system is built to handle real-time data inputs, and indeed many of our datasets can be considered real-time, for example events happening in a particular location. However much of our data and conclusions are based upon historic data going back many months and often years – so we form a very good picture of what a particular location looks like on a Tuesday at 3.22pm (with seasonable adjustments in some areas), and then we are able to augment this with much more recent data – for example intent data, social data etc…
So right now we feel there’s a great deal of opportunity to apply our data without trying to paint a truly ‘real-time’ picture of the world (which clearly has some serious scale and even perhaps privacy issues) but as and when we can derive additional value by on-boarding real time data then we will certainly be in a good position to take advantage of it.
What are your thoughts about the Digital Out-Of-Home market? Is there an opportunity for PlaceIQ?
I am a big fan of the Digital Out Of Home market (DOOH on it’s own is such a great acronym!), I often think it doesn’t get the recognition it deserves from the broader mainstream technology media, as it’s a very large and growing market, with some truly great technology and media innovators in it. Given the fact that by it’s definition location plays a big role, and it is ‘out of home’ therefore existing in home demographic approaches don’t work so well, I think there are some fantastic opportunities for PlaceIQ to be able to help bring additional understanding to the context of a particular screen or unit, which can aid both buyers and sellers, and also at some point, by uncovering the dynamic changing context of the different types of people at a location, there’s clearly a great opportunity to be able to optimize the media to the right audience segment by location and time.
In terms of hurdles, the obvious challenge here is that it’s a market very much in transition from the conventional media approach, to the ‘digital’ way of doing things, and that presents some challenges, but I’m very optimistic for the future of the segment, and the role PlaceIQ can play there.
How does PIaceIQ capture data?
Our data inputs come from a large set of sources, but broadly fall into three categories: open data, partner data and commercially sourced data. We’ve spent a great deal of time building a normalization process, and a very complicated taxonomy that allows us to onboard disparate, fragmented datasets and run analytics on these and attach meta data and meaning to them – and thus effectively make sense out of, and across, a very large amount of location data and draw out some pretty powerful conclusions.
From a privacy stand point it is important to note that PlaceIQ isn’t tracking or identifying individual users, we have no applications, or anything to install on the user side, we analyze all the anonymous location data and signals within a location, and from this data infer the context of a location at a particular time period. PlaceIQ cannot and does not identify the specific unique identity of individual users, or any of their personal information.
How many cities or DMAs have you covered? Any plans for future coverage?
We are currently running pilots with a number of mobile ad networks, and advertisers. We’ve focused on 5 major metros for these pilots, but plan to build out the majority of the US population coverage this summer.
What about funding – seed round investors or Series A?
We raised a seed round last year from a great set of big data and ad tech focused folks, and we’re starting to think about our series A currently.
Finally, a year from now, what milestones would you like PlaceIQ to have accomplished?
Looking a year out, our goals are to have PlaceIQ data being the de-facto standard for the location based optimization of mobile advertising, spanning the sell, network and buy sides. While it may sound like a cliche, we do believe applied in the right way we can help to make location based advertising more relevant and meaningful for consumers, and more performant and targeted for advertisers.
We are also very focused on having our data adding value in some other very large markets such as local search, online advertising and Digital-out-of-home.
We have some aggressive plans, and it comes down to this for us: We firmly believe that the concept of ubiquitous location is only going to become more pervasive, and that it will play an ever increasing role in the way that products and services are digitally marketed to consumers, and there’s a huge opportunity for a company that provides the intelligent contextual data to empower businesses to take advantage of this growing shift – and we’re very focused on making PlaceIQ the leader in this space.
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