Measurement boundaries to include offline behavior for mobile campaigns
By BJ Hatcher
Over the last several years, we have seen mobile marketing emerge and rise in complexity and offerings to advertisers. The mobile space is dedicated to delivering optimal consumer experiences that keep a consumer engaged with brands while they are on the go.
Proving the efficacy of programs based on both devices – lack of cookies – and consumers who jump across platforms to complete transactions has always been challenging. Only in the last year have some serious nerds stepped into the game and started to provide great resources for marketers to effectively measure mobile campaigns.
These new advancements are increasingly difficult to sum up into a bullet point or two.
To fully understand how they work, you will have to do lots of homework to understand technical capabilities, take a critical eye to the consumer journey, and complete a course in statistical regression theories.
That is why we need nerds.
Find proven correlations between on- and offline channels
In my team’s experience, mobile campaigns consistently drive up to five times more new customer acquisitions in our clients’ retail channels compared to online storefronts.
Regardless of the nuances of a particular client, our results show the importance of continually pushing the measurement boundaries to include offline behavior for mobile campaigns.
Here is how to do it.
Approach #1: Leverage location-based ad networks
Location-based ad networks can help track consumers through the entire ad-call process.
Companies such as PlaceIQ and Placed offer an excellent starting point. These vehicles provide offline measurement insights about traffic to stores. (Any data about actual sales attribution comes from other forms of measurement or assumption sets.)
PlaceIQ monitors and pinpoints the location of an ad-call. The reports include a “Place Visit Rate” that highlights the movements in location between ad-calls – where someone’s device was before and after they were served one of your ads.
• The upside: Treated groups are compared to non-treated holdout groups to provide a high level of confidence in the findings.
• The downside: The follow-up reports are very thorough and often take longer to deliver than general campaign reporting cycles.
Placed is another tool that many ad networks and targeting teams use to report in-store traffic metrics.
Placed manages a user base of opted-in consumers who allow the app to GPS-locate them.
The primary benefit to marketers is that ad-calls are not required to measure the migration or effectiveness of an ad. Plus, you are able to use the Placed platform to determine which other locations your consumers are most likely to visit for future campaigns.
• The upside: Placed works with most ad networks making it easier to maintain your best-performing partners for an additional cost.
• The downside: Most of the data provided is an extrapolation based off of a sizeable, yet small, user base of consumers.
Approach #2: Leverage attribution partners
These companies include the likes of VisualIQ, Convertro, Adometry and a few others. They have only nuanced differences between them, so I would recommend meeting with them individually to compare and contrast their methodology and pricing.
Warning: Make sure to have your best team of nerds with you to ensure that the hard questions are asked.
• The upside: Attribution partners can take your list of activations and match them to their lists of known digital identifiers and provide various forms of fractional allocation based on all touch types used.
• The downside: Measurement is limited to audience matches within the digital identifiers, around 15–40 percent; results are then extrapolated back up to make 100 percent.
Approach #3: Go list-driven, baby
This is my favorite of the advancements in the mobile space.
As a direct marketer, I love a list because it is a set of known, targeted and considered individuals. This information also allows you to create a continual optimization loop to enhance audience targeting and find new buyers based on models built using real data and real results.
It is ideal to facilitate data matches from targeted leads to digital identifiers while maintaining protective silos to protect PII (personally identifiable information) from intermingling.
• The upside: Once activations come through, you can match new acquisitions to the people on the original targeted list to measure campaign performance across all sales channels – not just online or through in-store assumptions.
• The downside: PII regulations prevent making the complete connection to your original source files – no matter how much you want to. Rules are rules.
ANY ONE OF these partnerships can begin telling the broader, more complex, more intricate attribution story for your campaigns.
I have no doubt that approaches #4–20 are currently being developed in a huge office complex or in someone’s parents’ garage. I cannot wait to see what the next generation of nerds rise to create.