Location data is everywhere, and being able to harness that data is one of the best ways to target customers. As Jon Lowen (pictured below), COO, SITO Mobile explains, using this data to create and measure marketing strategies, gives advertisers the ability to drive measurable in-store visitations in real time.

How does closed loop 1:1 in-store attribution work with location-based advertising?

Device-level 1:1 attribution works with location-based advertising to ensure we are optimally leveraging location data to target the most relevant audiences. SITO, for example, can measure the attribution of consumers based on device-level metrics and the location of the consumer. When we target consumers via location data, we have a much higher probability of being able to identify if this consumer then goes and visits a store or selected 'point of interest' and we can verify that walk-in event.

When the main goal for a marketer is to drive walk-ins, or measure in-store attribution, it is always best to use location targeting to drive the highest results. Targeting someone based on their current location, or previous location-based behaviour, is the best indicator of intent to buy and whether or not a consumer is likely to convert and walk-in to a specified location.

How does Verified Walk-In (VWI) technology work in big cities versus a smaller city? Is it actually harder to pinpoint a user in a crowded city like NYC versus a smaller city?

In some cases it is actually easier for us to target and attribute a walk-in in a crowded city like NYC! In cities that are very dense, you tend to see a lot more data and a lot more connectivity. This allows us to achieve deeper insights into a user and their location patterns. It also makes it much easier to identify location fraud based on irregularities in behaviour and outliers.

Another attribute of crowded cities is that a lot of the stores and locations are side-by-side or stacked on top of each other, like either malls or skyscrapers. When buildings or stores are connected or side-by-side, this does not pose a problem for our VWI technology, as our location data and tech has very accurate horizontal accuracy. Where we do run into issues, though, is vertically (in identifying if a consumer, for example, is on the second or first level of a mall) which could actually mean they could be in two different stores. In order to combat this, we work with a number of beacon companies and beacon aggregators that give us vertical accuracy and information on which store/level a consumer is entering and sometimes even can tell us the zone within a store.

With the integration of VWI with other platforms, how does this give advertisers different ways for better targeting?

Jon Lowen, COO, SITO Mobile

Now that VWI is available beyond just SITO’s own DSP and managed service, it opens our technology up to many other media partners so they can also tap into our location data and attribution capabilities at a very granular and transparent level. By providing other media platforms with this level of data insight, their technologies are able to better understand what strategies, creatives, locations, and other tactics are driving actual store visitation… and all in real time! This allows these other companies to start to optimise their efforts based on walk-in data, rather than standard engagement metrics – which is a big deal.

For example, one of the biggest changes we are seeing is in the OOH industry via their utilisation of this data and measurement. We have created a new form of planning and optimisation based on our foot traffic technology that allows OOH vendors to measure the success of their campaigns based on how effectively they drive foot traffic. Accordingly, we can also help them win business due to the proven success of their campaigns.

What are the top KPIs marketers measure with location-based services? What do you see as the percentage breakdown between the KPIs?

The top KPIs in location-based targeting are walk-ins (store visits), incremental walk-ins, and lift. We are also beginning to break this data down by day of week, time of day, and are starting to look at the audience demographics and behaviour traits of the converted audience.

In addition to the location-based metrics, marketers still treasure CTR, completed views, website engagement, and second click engagements.

How has the demand for transparency by brands/advertisers changed the way location-based advertising has grown? How are you addressing this need?

Transparency is, without a doubt, a good thing for our business. It uncovers the fraudulent data as much as it uncovers the fraudulent technology and companies. As more and more advertisers require transparency and granularity into their data, sources, methodologies, and results, this has forced a lot of the consolidation this industry needs. Advertisers want to work with companies that are actually building ad tech and marketing solutions and have the granularity to look at a large dataset or campaign and be truth tellers with the data and results.

Being able to accommodate multiple reporting requests, or different segmentations and skews, arms our clients and advertisers with the needed data and results that prove the success and value of a media campaign. Our focus on building intelligent tools that help plan, execute, and report on the value of marketers’ paid media has always been our priority and we continue to develop custom solutions that are a result of the agility of our technology and data.

What changes do you see coming down the pipeline in location-based advertising and attribution?

Everyone is focused on location data and, even more specifically, on persistent mobile app data. As more and more companies start tapping into the same apps, and publishers begin to monetise their location dataset, we will see prices for this location data drop drastically as it becomes commoditised. The companies that are able to best utilise location data via advertising, measurement, optimisation, and marketing insights will be able to best extract value from this emerging and growing dataset in the long run.