Footfall Attribution methods - by Targetoo


Footfall Attribution measurement tools are hot, but relying on installed SDK’s means that measuring Footfall - in many countries/geo’s - is not possible. Or is it?

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In this blogpost we describe how it’s possible to measure footfall in countries where little to none installed SDK’s (of third party data providers) are present.

Let’s take a step back. Footfall Attribution relates to measuring the physical effect of (any) campaign - in the form of actual/physical store visits. As you would figure, Footfall Attribution campaigns are mostly (and only) relevant for companies with physical stores/shops.

Normally, to prove Footfall Attribution, a third party data provider is scaled up. A third party data provider has their - or their associates’/partners’ - Software Development Kit (SDK) installed in many applications (apps). This allows these third party data providers to track the location of a device. The problem is that in many countries little to none popular apps have an SDK installed in them. Making - proving - Footfall Attribution not possible. Not to even mention the GDPR deployed/activated in Europe (some third party data providers no longer offer Footfall Attribution in the EU because of this). We have found a way - however - to prove footfall attribution in countries where little to none installed SDK’s are apparent, relying and using the sheer volume we are able scale up - based on the many adexchange integrations we have. Not to mention the fact that this solution is not in conflict with the GDPR - which is active in Europe.

Footfall Attribution by Targetoo
We have tested this technique/solution in several countries as to date. For starters we launch a ‘normal’ campaign. Either targeted nationwide (whatever country it may be) or deploying significant GEO-Fences in the area’s around the shops of the advertisers/client in question. We make sure that these main GEO-fences do not cover the actual location of the physical shops - applying a ‘safe’ margin of 250 meters around every location/shop. We then launch small GEO-fences on the exact location of the physical stores/shops of the advertiser in question. At that point the fun starts: we export the device id’s which have been served a banner within the normal line. After a few days, we then export the device id’s which have been served a banner, within the small GEO-fences (located on/above the physical shops). At that point we simply analyze if there has been served a banner within the small GEO-Fences, which prior has been served a banner within the main/normal campaign. And with that; proving Footfall. And for all the doubters/non-believers out there; this technique actually works!

Disclaimer; we were skeptical - when testing this technique - to say the least. As any expert can tell you; an (in-app) impression has to be served in order for us to register the position/location of a device. This means that the user/consumer needs to open an app while being in the store. This is very different from an installed SDK sending the location of a device. In most cases, the user/consumer doesn’t even need to open the app. The SDK forwards the location solely based on the fact that the app is present/installed on the device in question. But again; in many countries there are not sufficient SDK’s installed to make a proper Footfall analysis. Not to mention the privacy issues this method brings to the table. All in all, a proper GEO-Fencing tool and old-school analyzing - can be the deciding factors for you to determine Footfall for your brand or client.

Reach out if you want to learn more about this technique and/or want to test it for your brand/client.


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