By Ron Menich, VP, Advanced Analytics & Data Science, Catalina
MAIDs are mobile ad IDs used for media delivery that come in two flavors: IDFAs (Identifiers for Advertisers, for iPhones) and AAIDs (for Androids).
Let’s take a look at the role of MAIDs in ad personalization today: They connect a customer to an ID so that a retailer can intelligently use their purchase history to construct relevant product recommendations. The retailer can recommend to a known shopper --- one with a frequent shopper card --- that they buy more of their favorite products or try new ones. The retailer can serve up these recommendations in ads, possibly supported by incentives such as coupons, through a variety of media channels.
But how do you reach potential shoppers who aren’t loyal to your store or who have no frequent shopper cards? Though seemingly vexing, this problem is not insurmountable. Indeed, Catalina continues to use MAIDs collected from consenting participants and available from numerous data providers to tell us about the habits of potential shoppers, as described below:
Mobile location data can be used to target shoppers who have been in the vicinity of one or more of your stores regularly in the last six months. You can also pinpoint if they’ve shopped at a nearby competitor.
Crosswalk the MAIDs
It’s also possible to connect MAID data to frequent shopper cards with something the industry calls an Identity Graph, a collection of relationship linkages sourced in a privacy respectful manner from external providers. We can then crosswalk the Identity Graph from MAID to household ID and then, in turn, to frequent shopper card identities associated with that household. Those MAIDs that cannot be crosswalked to frequent shopper cards are called unknown shoppers and are a target for frequent shopper card acquisition.
Determine Relevancy Scores
There is typically a large pool --- often hundreds --- of potential ads that can be delivered to each unknown shopper. Catalina identifies five to 20 ads that are most relevant to those shoppers: we use machine learning models to create a relevancy score for the potential delivery of the ads in this pool to a targeted digital ID. For unknown shoppers, we first learn how historical mobile location behaviors relate to historical purchase behaviors for known shoppers. For example, we can look at the purchase behavior of known shoppers whose mobile behavior shows they regularly visit health clubs and apply it to prospective shoppers for healthy foods.
Armed with these relevancy scores, retailers can then drive personalized media delivery in a DSP. Some unknown shoppers intrigued by these ads will start visiting your stores regularly and acquire a frequent shopper card.
Adapt to MAID Fade
In the near term, the digital media industry is concerned that the iOS 14.5.1 update will decrease the number of IDFAs to whom retailers can deliver media. Google will follow with an associated impact on AAID availability later this year.
If the number of available IDFAs drops, then retailers can target alternative unknown shopper identities. For example, combine data about household zip codes near one of your stores with non-IDFA digital IDs like cookies, IP addresses and connected TV addresses associated with those households.
Longer term, as both MAIDs and cookies continue to decline in number, retailers will need to increasingly look to contextual targeting. This strategy delivers ads on particular apps and websites on particular days of week - or times of day – that with the highest propensity for potential shoppers to click or otherwise engage with the media. Additionally, retailers can use digital incentives to encourage shoppers to continue to provide their identities so that they can continue to receive relevant incentives.
Customer acquisition will continue to be a challenge for grocery, drug, and convenience store retailers, but with mobile location and alternative data sources you will be able to target loyal and prospective shoppers with relevant, personalized content.
To learn more, explore Catalina’s Digital Circular Personalizer, which delivers relevant ads to both known- and unknown shoppers.