What Happens to Drive-to-Store Campaigns After Apple’s IDFA Update?

Apple’s forthcoming IDFA changes for iOS 14 will cause a number of shifts within the mobile marketing landscape. But one considerable challenge for marketers could be adjusting to a new feature known as “approximate location.” Instead of providing a precise location, iOS 14 users can enable “approximate location” for their apps, which limits their actual positioning data to within 10 square miles. For marketers, this is obviously a wildly imprecise measurement, especially for those looking to measure the effectiveness of their location-based campaigns in a direct-response fashion.

On the surface, it would appear that Apple is dealing a crushing blow to the measurement of location-based marketing efforts. However, there’s no reason for marketers to abandon their drive-to-store campaigns. There are alternative measurement solutions that will prove more important to mobile marketing campaigns going forward. This includes tried-and-true tactics that have long been leveraged more heavily in other areas of the advertising ecosystem.

The True Effect of Apple’s Update

First off, let’s acknowledge that attribution in drive-to-store campaigns has always been a challenge. The IDFA was in no way a panacea in this regard, even on Apple devices. Conversions in drive-to-store campaigns, which typically refer to the purchasing of a product at a physical location, are not tracked by the IDFA—at least not directly. 

What the IDFA is useful in tracking is clicks and web visits on Apple devices. Conversions, particularly those that occur at brick-and-mortar locations, are quite a bit more complicated. Typically for a drive-to-store campaign, conversions are tied to credit card purchases, which are tied to email addresses, which are then tied to device IDs. It’s a long path to attribution, and a lot of information gets lost along the way. 

Furthermore, what is often thought to be one-to-one attribution these days rarely is. In fact, most of the attribution providers that measure store footfall actually leverage panels and statistically relevant samples to derive their attribution and measurement metrics. That’s because even under current IDFA policies and industry developments, there are limitations in regard to what can be effectively tracked and attributed. So, the best place to look for results remains the cashier and the increase in sales over time. 

Given that Apple’s Limit Ad Tracking feature already renders roughly one-third of iOS users totally anonymous, drive-to-store conversion measurement has been limited at the device-level for some time. The iOS 14 update from Apple simply adds another challenge on top of what was already a difficult endeavor. For marketers who haven’t done so yet, they should take this opportunity to pivot to measurement strategies that are less reliant on the ever-shifting policies of tech giants like Apple.

Doubling Down on Media Mix Modeling

In light of Apple’s iOS 14 update, we’re going to see the mobile ad industry, including location-based marketers, increasingly turn their attention to a more comprehensive “media mix modeling” approach to measure campaign ROI. Given the complexity of tracking in-store conversions from digital advertising, this is a movement that has already been gaining momentum in the mobile advertising industry. Apple’s latest shift only adds fuel to the fire. 

While digital media—and mobile in particular—have long been focused on one-to-one measurements (due to the personal nature of devices), privacy concerns and ever-shifting user tracking policies among tech companies have contributed to a rapid rise in the imprecision of many common digital measurement methodologies. Likewise, many click- and view-focused attribution strategies fail to account for natural sales behaviors (i.e., ones not fueled by exposure to advertising) and the impact of offline marketing channels. That’s where media mix modeling shines.  

Media mix modeling, which has been employed in traditional marketing campaigns for decades, is a top-down measurement methodology that looks to quantify the incremental impact of spend in a given advertising channel. It’s a broad-based concept that evaluates channel-specific media spend against a conversion metric like sales. It’s quite distinct from common bottoms-up approaches in digital media that focus on clicks and views. 

A big part of media mix modeling is the measurement of incrementality, or the incremental lift, driven by exposure to a given campaign. This typically involves lift studies and tests that evaluate the actions of groups of users who were known to be exposed to a campaign via a certain channel versus a group that was not exposed. The findings from these groups can then be extrapolated based on the sample size. These statistical models avoid the privacy concerns of many of today’s one-to-one attribution approaches and, done right, are still a highly reliable way of understanding incrementality. 

Ultimately, Apple’s IDFA changes will affect how marketers measure their campaign impact, but it’s not the wholesale disruption some are purporting. It’s simply the next step in the shift toward a more comprehensive media mix modeling approach that delivers reliable ROI understanding while respecting user privacy. 

Ionut Ciobotaru is Chief Product Officer at Verve Group.

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