As all marketers know, a customer’s path to purchase can be a long and complicated road, spanning multiple touch points across devices and mediums. For brands with a brick-and-mortar footprint, measuring the impact of online ads on offline sales volume is a critical component to properly valuing the success of your digital marketing program.
There are a number of tools available to digital marketers that can help provide visibility into this offline spillover. Below, we will walk through some of the more popular tools on the market, as well as some strategies for working towards a data-driven measure of the incremental lift that digital ads afford brick-and-mortar sales.
Google Store Visits Data
Store Visits is the easier to implement, albeit the more rudimentary, method for tracking offline conversion information that follows from customers’ digital ad interactions.
The basic gist of Store Visits is that Google can match users’ search activity to their mobile device through their Google login; from there, GPS data can report when that user visited the advertiser’s brick-and-mortar location. This information is then extrapolated to estimate the total store visits from users who previously interacted with a digital ad.
One of the major draws of this data source is that it does not require any data to be provided by the advertiser beyond verification of store address information within AdWords.
Even if you are willing to take Google’s geofencing (defining store locations by mapping GPS coordinate boundaries) assumptions and extrapolation algorithm as gospel, Store Visits data leaves you only with the number of users who interacted with an ad online and then entered a physical store location. It does not tell advertisers exactly how many of those visitors end up making purchases in stores.
If you, as a business, track conversion rates and average tickets in the store, you can back your way into an estimate of offline orders and revenue that came from users interacting with your brand through a digital channel. This relies on a number of assumptions around in-store customer behavior and disregards the potentially significant differences between customer segments in estimating revenue driven by the reported store visits.
The lack of concrete offline purchase information is the most significant shortcoming of the Store Visits offering but it’s still a good start to getting an idea of in-store spillover from online ads.
Google In-Store Transactions Beta
Fortunately, Google has a second source of in-store measurement that does not rely on conversion assumptions: In-store Transactions. This program works by linking an advertiser’s AdWords account to a third-party data provider which processes a list of store transactions through its cookie pool to match back online ad interactions to offline conversions.
This allows the advertiser to see concrete transactions in physical stores by users who first interacted with an advertisement online. Because In-store Transactions data is anonymized and matched to advertisers’ point-of-sale records on a one-to-one basis, it allows you to see definitive and quantifiable offline conversions driven by digital ads.
It is important to keep in mind, as laid out in a recent SEL post by my colleague Andy Taylor, the entire In-Store Transactions system is contingent upon a user appearing in the partnering data provider’s cookie pool, and match rates vary widely from client to client. Since match rates will always be less than 100%, Google can provide extrapolated estimates for what the full value of in-store transactions is for your account.
Though this may be a tempting offer, given that it will result in higher offline spillover in your reports, it requires that you make assumptions about customers’ shopping behavior. If you are looking for concrete figures, the raw reported numbers will give you a defendable minimum for your digital program’s influence on in-store purchases.
Offline Tracking for Display Advertising
From a display perspective, offline visibility from these Google betas is limited to the GDN. Fortunately, a number of display providers, including Placed, PlaceIQ, and Foursquare, offer some form of in-store visitation measurement as a differentiator of their services.
Most of these capabilities are centered on mobile device data – GPS information, IP information, & location information from mobile ad calls—techniques that allow the platform to infer the physical location of users. Each platform is different, though, so it is important for an advertiser to do their due diligence before deciding to run with a given provider.
For advertisers of brands that are heavily skewed towards offline conversions, finding those display vendors with offline visibility is incredibly important, and should be a serious consideration when determining the optimal partner set for their display efforts.
Cross-Channel Measurement Solutions
Platform-specific measurement tools are definitely important for understanding the offline impact of your digital program, but an ideal solution would be able to holistically collect and report this data across channels. Data providers such as LiveRamp and Datalogix offer products that track offline conversions across nearly all digital channels and deduplicate orders for multi-touch customer journeys.
Within the context of the Google beta, the data providers Google partners with sift through offline transaction files and flags any transactions that match to a user with Google ad touches. By partnering with data partners directly, advertisers can widen this net to include touches from Bing and Yahoo, as well as most display partners and comparison shopping engines, providing a much more robust view of your digital program’s offline impact.
A major benefit of running on the In-Store Transactions beta is that Google currently absorbs the costs of the third party provider. Working directly with one of the data providers necessitates that you pick up this cost internally.
Though it can be tough to swallow the idea of paying for a service that you can receive (in a limited fashion) for free, the expanded scope of tracking capabilities, and the resulting lift in traceable offline spillover, can prove well worth the investment for retailers that skew heavily towards offline purchases, particularly if a significant portion of your digital dollars go to channels outside of AdWords.
Another creative way to directly associate offline purchases with online ad interactions is through e-receipt tracking. Retailers that follow up on in-store purchases with an email containing customers’ receipt and purchase information can tag those emails with a tracking pixel that fires when the message is opened.
At that point, if the user has one of your cookies on his/her browser, you can match back that cookie ID’s ad interactions with the specific details of his/her offline purchase. Assuming that you are placing cookies on anyone who touches an ad, this can be an effective method for capturing offline spillover.
This method will have match rates of less than 100%, similar to Google In-Store transactions, given it is dependent upon customers’ e-receipt open rate.
Store Locator and Location Extension Interactions
Unfortunately, without any tracking of offline spillover, there is no intelligent way to measure what percentage of people who use a store locator or location extension actually end up going into one of your physical locations. The best you can do is make a reasonable estimate, which is far from the data-driven precision we strive for in our marketing efforts.
In both cases, users are looking to find information on how to get to one of your brick-and-mortar locations; the only difference is whether they do so on your site or within an ad unit directly on the SERP. As such, it is reasonable to assign them the same value for your program.
Users engaging in these types of interactions can often overlap with those tracked through other methods. Thus, advertisers using multiple methods to tie offline conversions back to digital ads should take steps to avoid double-counting and overstating the offline value.
Methods of Measuring Digital Incrementality
While these methods can all help to give advertisers an idea of the number of in-store purchases that have ties to online interactions, they do not represent the number of offline interactions that were driven by those interactions. In other words, these methods don’t account for the number of offline transactions that would have taken place without those online interactions.
One method to get at this measure is to run market-level hold-out testing, alternating on/off periods for your digital advertising, then retroactively measuring the change in store visitation and sales to determine the incremental lift.
Alternatively, you could test two markets that are deemed similar against one another simultaneously, with one running digital ads while the other suspends them, flipping after a set period. This removes the seasonality variable from the test, but opens the door to any number of variations that come with treating two different markets as interchangeable.
However, hold-out testing can be difficult to sell internally for some organizations, as the thought of ads going dark for extended periods of time can be hard to swallow. Results might also end up statistically inconclusive, landing you back at square one.
Whatever your strategy for assessing incrementality, it’s important for advertisers to understand that just because a purchase has ties to an online click does not mean that the click was what drove the individual to a store. This is particularly true of brand ads, and should impact how you adjust your online investment as a result of offline purchase tracking.
Institutional Support for Offline Spillover Data
As digital marketers, comprehensive, accurate measurement of the impact our online efforts have on brick-and-mortar sales is the ultimate prize. For the broader organization, however, shifts in the attribution of offline sales can be met with resistance from internal stakeholders.
Some parties in the organization may be hesitant to accept that a portion of revenue that used to be credited to them is now being credited elsewhere. Institutional support is essential for your offline spillover attribution to be used in meaningful decision-making.
Being able to clearly explain your modeling assumptions, and the underlying data supporting them, can help bring internal stakeholders on board in support of your enterprise attribution model. Unfortunately, no tool available can provide the level of exhaustive data collection necessary to perfectly fuel omni-channel attribution.
Nonetheless, marketing programs the world over seek to get closer to this answer, and there are ways to gain visibility into the incrementality of digital advertising’s spillover into brick-and-mortar stores that can help guide the way.
Taking the First Step
Given the many considerations that go into it, attempting to measure and report on offline spillover from your digital program can seem like an overwhelming task. Indeed, to fully explore the interaction across channels requires significant effort and support.
That shouldn’t scare you off, though, from taking the first step. Store Visits data is available for all advertisers to set up in AdWords, providing a low-risk starting point to dive into the numbers and see what you find.
The results of these initial analyses can be used to garner institutional support for further exploration and serve as evidence to justify investment in fleshing out the impact of digital advertising on brick-and-mortar retail sales within your organization.