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?


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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