Blis And GroupM’s Mediacom On Uncovering ‘Smart Trends’ In Attribution

Location analytics platform Blis is rolling out the beta launch of Smart Trends, a new data and insights tool that allows marketers to analyze consumer behavior, from profiling to attribution, by capturing and activating mobile movement data.

Smart Trends gathers in-store consumer behavioral data and then matches it with in-store comparisons of multiple location types and brands, so that marketers can break down by demographic, contextual, time/day, and device type analysis, as well as compare behavior of user groups side by side.

Media buying shop Mediacom, which is part of WPP’s GroupM, is one of the first ad agency partners to take up Smart Trends with Blis. We checked in with Amy Fox, head of Product at Blis, and Ben Phillips, global head of Mobile at Mediacom, to get an overview of their partnership and what it means for brands.

GeoMarketing: What’s the nature of Blis’ consumer behavioral analytics within Smart Trends?

Amy Fox: Using mobile location data, Blis’ new Smart Trends tool unlocks consumer behavior insights on purchase intent, shopping patterns, and mobile consumption while shopping. Smart Trends provides in-store and inter-store brand analysis by breaking down audience demographic, contextual content, and foot traffic. This allows for side by side behavioral comparison of user groups to enable more effective campaign planning, delivery and attribution in order to deliver competitive advantage.

How does Smart Trends compare to more established behavioral analytics tools like PC-based cookies?

Amy Fox: The data feeding Smart Trends starts out as a string of otherwise arbitrary numbers, which once overlaid with the Blis Point of Interest Database becomes insight into the daily behaviors of devices in store.  Smart Trends layers these snapshots of information over time to provide brands a full overview of spatio-temporal behavior– looking at how people move between the residential, recreational and retail environments.

Ben Phillips: It adds another layer of data that enables us to cross reference existing tools that are available to us and our clients.  Mobile has always been able to provide vast amounts of data based around a consumer in the moment, what were now developing is how to not only understand where our audiences are now but where they have been and to enable predictive modelling for the future. Smart Trends helps us to better understand consumer journeys, attribution and engagement with digital and offline media.

If location is at the center of this tool, how do you regard the perennial question of whether “location data is the new cookie?”

Amy Fox: Location is the new cookie and more when it comes to targeting and engaging with audiences. Proximity is important but you’ve also got to look at location in a historical context. You can build up comprehensive consumer profiles looking at where their device IDs turn up– whether it’s an retail store, a hotel or a movie theater. This is vital to predicting future behavioral. It’s not about where people are, but where they’ve been and using those insights to know where they are going.

Ben Phillips: Location does afford us elements of personalization above and beyond the traditional desktop measurement solutions.  Mobile has developed ways and means such as device graphs, probabilistic and deterministic ID matching and behavioral modelling to determine its audience.  This goes a long way to conforming that the best solution in market are those designed for Mobile first, this approach negates a lot of the preconceived problems encountered when working with desktop platforms and methodologies and expecting them to work in a mobile world.

Location data quality from bidstream/programmatic, GPS, cell phone tower, wifi, and (to a certain extent) beacons/bluetooth IoT sources, offer varying value in terms of accuracy. What are the sources of Blis’ analytics tool and how does it deal with the questions of signal sources and accuracy?

Amy Fox: Smart Trends data is captured via movement data sources which includes GPS, wifi and beacons. Like all Blis-verified location data, it passes through our quality control technology to filter out inaccurate and fraudulent points so that we’re only working with sources we can trust.

Ben Phillips: With 50-70 percent of GPS data being inaccurate, fraud needs to be removed which unfortunately leaves the data sets at a fraction of the size with the need to be scaled up again in order to identify actionable insights. What Blis does is they use verified GPS data scaled out to public wifi, something we haven’t seen done with any other location partner.

Are there any particular kinds of clients that the new behavioral analytics tool benefits? (Retail and QSR versus automotive and banking/financial services? Or does it benefit all major categories?)

Amy Fox: The insights gained from Smart Trends are applicable to any category.  Brand marketers across all verticals have access to a wealth of information on their customers, but often the data is limited to engagement with their own properties. To get a more holistic perspective of their audience – looking at aspects such as behavior with your brand in the context of competitors and in different environments – is valuable in identifying lifestyle indicators to inform brand positioning or cross-vertical partnerships that will help convert target audiences.

Ben Phillips: Many clients, not just retailers with storefronts, are using location data to measure the amount of time spent with their brand– everything from footfall attribution to cross platform engagement. Blis took us through a pilot study looking at foot traffic across the national grocery market. Obviously this is an extremely competitive sector, with promotions and store openings constantly shifting as they fight to increase shopper frequency , which ultimately impacts revenue market share at a brand level.

This passively collected data can provide insight on the actual store-to-store behavioral of shoppers with a granularity that survey data simply can’t match. As we move further away from traditional buying proxies, I predict that location data will become a broad consideration in the coming months, accounting for a large percentage of campaigns in 2018.

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Does Location-Based Advertising Have A Viewability Problem?

Location-targeted mobile ad sales are expected to rise from $9.8 billion in 2015 to $29.5 billion by the end of 2019 — but with that rising demand comes greater expectations about ROI, attribution, and accuracy.

The issue of geo-data accuracy has always been a thorn within the great promise of location-based advertising to bridge online and offline, desktop and mobile (and we first outlined the problem here back in 2014).

Placed, which was acquired by Snap this summer to provide attribution services for Snapchat ad clients,  is the latest geo-data specialist to offer guidance on the matter of location analytics accuracy in a report, Accuracy & Bias In Ad Exchange-Derived Location Data.

The analysis for the report is gleaned from the company’s primary product, Placed Attribution, which is based on an audience of over 150 million device generated over 140 billion latitudes and longitudes on a monthly basis.

Thanks to its extensive alliances with dozens publishers, networks, and demand side platforms, including ncluding IPG MediabrandsDigitasLBiHorizon MediaTapadDataXuDrawbridge, among others, Placed’s data sources have grown to include first and third party data as well.

As Placed CEO David Shim describes it, location ad accuracy is a viewability problem.

  • Average location accuracy was 4 New York City blocks
  • Only 1 percent of locations were accurate enough to identify a store visit
  • 80 percent of bid requests with location occur when they are in-between visits, with a good portion of the visit based impressions occurring at home

“With location accuracy not in the forefront of the viewability conversation, bad players have been able to capitalize,” Shim said. “Placed’s recent research and findings arm advertisers with an understanding of the location landscape that has been missing to date, independent of media.”

The Viewability Issue

In the larger ad tech sense, viewability has been a considerable cause of mistrust between buyers (brand and agencies) and publishers. In essence, viewability refers to whether a display ad placement was seen by an actual person or if it fraudulent ad impressions were generated by the “visits” of bots.

“In the near future, marketers will require a viewability-like metric to gauge the accuracy of location used for media, targeting, attribution and analytics,” said Benjamin Bring, VP, Mobile Media Director at Ansible, in the Placed report. “To date, the siren’s song around the potential of location has been able to drive the early adoption, but scale won’t come until the industry delivers a standard verification solution.”

“Viewability in location is directly aligned with accuracy,” Shim told GeoMarketing. “In an ecosystem today, where anyone can claim any location, it is important to not take location at face value, and continually verify the source of the location.”

‘Not All Geo-Data Is Created Equal’

Among the most difficult issues for marketers who want to use location data comes down to the sourcing of that information. Those sources, or signals, can come from a variety of channels, including GPS/satellite, wifi, computer IP-addresses, cell phone towers when it comes to pinpointing the specific lat/long the device accessed. Panel-based check-in services  like Placed’s— the location-based ad equivalent of a Nielsen diary that contains what a viewer watched on TV — are another popular avenue for accessing location data.

As programmatic advertising has become mainstream, the general purpose for for location advertising is two-fold: there’s the desire to provide real-time ad targeting as well as developing a greater understanding of consumers according to the places they go that provides more actionable insights than mere demographics (age, gender, household income, etc…) can offer.

Placed also leverages a proprietary behaviorally-derived measure of store location that it calls ‘Survey Geometry.’ Similar to the store geometries that measure the outline of the building or parcel (i.e., car lot).

One of the problems with a source like bidstream data is that its not a persistent signal like wifi or GPS. Bidstream data often depends on a person opening an ad on their phone while they’re in a specific place. The publisher whose ad is opened in that moment receives the data and passes it on to the network or vendor that placed the ad. If that person who saw the placement then goes to a store that was advertised, that visit counts as being “attributed.”

Of course, a phone’s location services records signals from hundreds of places in a given day. The odds of the information being attributed coincidentally (i.e., incorrectly) is a challenge that comes with real-time data platforms.

Quality bidstream location data, like those coming directly from publishers, is generated from the mobile device native location-based services, which use a combination of GPS, wifi, and other signals. That data is then pulled by the app developer via the phone’s SDKs. The process is the same whether the app sends location data via the exchange (bidstream) or direct. Regardless of a location data’s origin, bidstream or direct from publishers, it’s important to filter data and curate sources as well as recognize and filter low-precision signals.

“Not all data is created equal,” said Joao Machado, director of Mobile at OMD, in the Placed report. “You have to be very diligent in determining the accuracy of location data coming off the exchanges.

“Quality beats scale all day long and 1st party data is the gold standard in quality of location data,”Machado added. “A horoscope app dumping location data into an SSP before landing in the exchanges is the example of what leads all of us to scratch our heads around lack of value around so much of what goes on in the market.”

As we noted above, the average location accuracy Placed found was within four blocks — and only 1 percent were able to identify a store visit. That all sounds pretty dismal and much less reliable than the image a user has when looking at their own location on a map and seeing the accuracy within 3- to 20 feet on average, according to industry sources we’ve spoken to.

While it’s a given that location accuracy is, all over the map (pardon the pun), is this a matter of the difference of location signal sources, such as GPS versus cell towers versus wifi versus bidstream data?

“It’s a combination of factors,” Shim said. “When measuring directly from the device, location signal can vary based on a number factors include environment.  This said, the primary source of location inaccuracy is bad players on the exchange that treat cell tower location the same as GPS, map IP address to latitude and longitude, or commit outright fraud by applying a latitude and longitude to a non-location based impression.”

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