Location based advertising reaches half its potential... here's what happens next!

When I set up Rippll after leaving a Media Agency I had a vision for the Mobile Ad Landscape that would include a type of "Google Adwords for the Real World" - a platform that would enable Advertisers to buy mobile ads with location targeting that would drive consumers in store, measure the visit or purchase, calculate the ROI and then re-invest that return to spend even more month on month.

This is exactly what I had done on desktop internet myself for brands like Tesco and Vauxhall when Google first launched its Adwords interface in the UK and I could see what a money printing machine it was.

As an industry we are now halfway there for the Location story in as much as we can target almost any mobile ad by location (albeit with very small reach outside of Facebook and Google if you want very fine grain targeting on legit inventory) and use Location Based Segments (albeit with very small reach if you want people who were 'actually' in a specific place for more than 15 minutes and not just walking past!).

The other half of that equation then is that of measuring the in-store conversions which for years was restricted to voucher codes on campaigns, however now the second half of this story begins and it may well lead to a compelling performance based programmatic model for the real world which is where Mobile will finally deliver on all its promises for Advertisers and Investors who backed the Mobile Demand Side players over the last few years.

The primary reason this measurement has become possible is another accidental side effect of Apple's obsession with User Experience and their invention of the IDFA.

Apple got so sick of consumers opening apps and being bounced in and out of web pages for conversion pixels to fire that they banned such an experience and offered Developers and Advertisers a unified tracking ID called the "Identifier For Advertising" ("IDFA" or simple "Advertising Identifier" - see above).

The great thing about the IDFA is that it is not PII (Personally Identifiable Information) and the consumer can reset it at any time, also by bundling reports of more than 1,000 IDFAs conversion reports are privacy compliant and there is also an Android equivalent called the GAID (Google Advertising ID).

The secondary reason that Location Attribution has become possible is still a bit tricky to explain and thats because Location Data Quality is still slightly fuzzy and confusing.

We have two types of GeoData in circulation which are both very valuable in general, however they can be thought of as good data and bad data when it comes to Attribution due to the need for a higher level of quality and accuracy when deciding to Attribute success to a campaign.

1. Ad Exchange GeoData - this is a millisecond snapshot of data suggesting the Location of a user being passed in an ad request from an app or Publisher who has attached the Location information in order to get a higher CPM for the ad slot. This data is great because there is so much of it (c. 19 million UK Users for example) however it can't really be used for Attribution because (a) we dont know if its real or has been spoofed as it is passed through the exchange stack (over 50% of GeoData in the exchange has been shown to be inaccurate or fraudulent) and (b) we have no timespan for the user being at that location hence they may have just walked past it.
2. SDK GeoData - this is 'always-on' data showing exactly where a handset has been and for how long. It is data that comes straight from Apps that carry an SDK (Standard Development Kit - A plugin that an app would use for Location Services) and into a Database so is 100% accurate, it is not linked to advertising as it primary reason for being and hence has no potential to be fraudulent or inaccurate. the only downside is that there is significantly less of this data opted into usage for Advertising Attribution outside of Facebook, Google and Apple apps (c. 1.9 million UK Users in 2016).

There are many companies supplying the Exchange data as you can imagine however there are only a handful of companies worldwide offering the SDK data due to the requirement for Data Capture... which is the need for a Consumer app to be pulling that data for the purposes of App Functionality (likely GeoFenced Push Notifications).

Foursquare in particular have done such a good job with their Conusmer apps that they have SDK data for 1 million users that they recently combined with Neilsen services to offer In-Store attribution in the US.

1 million would seem to be a magic number for any market given the limited number of apps that can actually offer Location Based Features to its users when the app isnt open (health and fitness apps etc) outside of Facebook, Google and Apple apps.

Here in Europe Rippll have amassed a similar number of users by supplying Location Based Notifications with a service called Geowave for many years.

So with the IDFA in place and rich SDK GeoData available how exactly does In-Store attribtion work?

1. The campaign is served to thousands or millions of handsets that have the desired targeting criteria (typically an audience segment of say "football fans" or "business travelers") so we know the IDFAs of those exposed to the advertising
2. This list of handsets is matched to a Reporting Vendor's DataPool. So for the Foursquare example their 1 million handsets, creating a match set of say 20,000 devices that both parties have in common.
3. The reporting Vendor then looks at the historic GeoData from those handsets along with the Data during the campaign and also after the campaign to see if those handsets that were exposed to advertising went in store (by uploading the POIs of store locations from the advertiser) and if they went in more or less often than usual.

There is also usually a control group created and deeper insights generated covering the type of consumers that went in store most often etc.

So what happens next in this space? Well the final part of the puzzle is "spend data" because we may see Visits as Conversions right now but we really want spend data for us to be on a par with Desktop Internet ROI models.

There are a couple of Credit Card companies offering some spend analysis services by Store Location but the big shift is about to come in 2018 with the PSD2 EU Directive which seeks to standardise and make interoperable card, internet and mobile payments though banking APIs. The knock on effect being that all banks will offer a robust set of APIs for Consumers and then Services they trust to access their banking information.

First these APIs will mean a whole host of consumer apps that let consumers slice and dice their spend data and then once aggregated and the privacy policies are correctly established this data will begin to surface in the marketing world and hence we will see the exact offline spend data that mobile ads generate.

It may take another 5 years for this Location story to fully unfold but Im sure that one day there truly will be a "Google adwords platform for the real world"... but by then it might just be called "Google Real World" or "Facebook Real World" if Publishers can't find a way to maintain their market share of Ad Spend... but that's a topic for another Post!

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To see a demo report of Christmas 2016 Shopping Visits or January 2017 Gym Uplift get in touch with us - contact@rippll.com - 0207 749 4181