By: Lara Koenig, global product lead, MiQ
A blog series explaining some of the concepts, processes and technologies we need to do our jobs – in plain English.
(NB. There’s a glossary of terms at the bottom of this post for explanations of some of the technical terms marked with *).
We’re currently in the midst of the biggest shift in the way digital advertising works since its inception. And that’s all because of what’s changing in the world of user identity.
Driven by consumer privacy concerns and accelerated by technology changes, identifiers that used to be widely adopted like third-party cookies and mobile device IDs are going away.
This big question is: what will this new world of anonymity look like for advertisers and brands?
In the first blog of this mini-series, we looked at authenticated data and how advertisers can use it in programmatic campaigns. This time, we’re looking at the other side of the new data landscape: anonymous data.
What is anonymous data?
As the name would suggest, anonymous data is the most privacy-conscious form of data, in that an individual person’s identity or identifiers are never exposed or shared in any form. It’s already very attractive for brands who have a low-risk threshold when it comes to potentially misusing data.
Instead, contextual data points are collected about the context in which a consumer is browsing. This can be either online data (the kind of websites they visit) or geo-contextual data based on things like zip code or postal codes and census data, rather than the identity or behavior of a specific individual or device.
These data points can be gathered together (or ‘aggregated’) into cohorts, groups of users all sharing the same behavioral characteristics. By doing so, advertisers can target people within certain cohorts, but never get any access to anything that will identify an individual user.
How does anonymous data work in programmatic?
Contextual data has been around for ages. Things like census data pre-date digital by a long way. But innovations around how we can use contextual and cohort-based data in digital advertising are gathering momentum, presenting strong new opportunities to reach valuable audiences at scale.
Let’s take a look at the various anonymous solutions out there:
Anonymous planning signals: This approach uses data that has been collected at a geo level. The identification of the geographic location of a user is captured via a variety of data collection methods. The data is typically aggregated to a postal code, or census tract that allows for granular segmentation, but not exposing an individual’s data.
Contextual targeting: This approach is based on web page-level contextual signals. By using contextual intelligence, advertisers and publishers can determine the kind of websites similar users visit and build relationships between them. So, obviously, if you’re selling cars, you want to advertise on websites about cars. But, using contextual data, you might also be able to identify the other types of websites car-website visitors regularly go to such as car insurance or personal finance sites, niche car blogs and forums, or even subsections of larger websites like ebay.
Cohort targeting: When it comes to marketing, the term ‘cohort’ refers to specific experiences, behaviors, or other factors shared by a group of consumers. These cohorts are used to identify and target segments of the market that, although they may fit into other models, are more effectively grouped and treated as one.
For example, men between the ages of 55 and 70 are a demographic grouping. But men between the ages of 55 and 70 who served in the Navy on board aircraft carriers are a cohort. Cohorts are far more specific than standard demographic groupings and as such are a valuable tool for precisely targeted marketing campaigns and niche businesses.
By using cohorts, advertisers can separate specific groups of people even within their demographic groupings, preserving the anonymity of the user whilst still allowing audience targeting, site retargeting, and attribution.
Anonymous measurement: There are also new approaches for cohort-based measurement, provided by the browser. This solution – proposed by the Google Chrome team as part of their ‘Privacy Sandbox’ solutions and still in early testing – in theory allows ad optimization and campaign attribution to happen while avoiding any user-level tracking.
Using anonymous data in real-world campaigns
Here’s an example of how we’ve tried using anonymous data in one of our campaigns for the quick-service restaurant, Subway.
In the campaign, Subway wanted people to know that they can order their meals through all the major food delivery apps.
Usually, the food delivery apps work within a two-mile radius of an outlet, so a standard user-level targeting approach might be to run digital ads that target people within that area. But that would be really inefficient – it’s highly unlikely that your audience is going to be evenly distributed across the two miles.
Using anonymous mobile data (provided by one of our partners) we could be more precise. By finding out the locations of active delivery app users we built clusters of audiences all in the same area that were much more useful. So, we could target, for instance, areas where there are lots of students, but avoid areas where ordering would be less common, like residential care homes or office blocks at the weekends.
We can then go further. Using behavioral trends from the anonymous mobile data, we built a richer picture of the kinds of sites the target audience was browsing, as well as the times and days they were most likely to be using delivery apps.
By using anonymized mobile data rather than cookies, the campaign drove over 112,000 clicks to Subway’s website, with a total of 497,000 unique target customers made aware of Subway’s availability on food delivery apps.
This translated into a 7.5% uplift in brand awareness of Subway’s availability on food delivery apps, accurately reaching half a million customers with specific messaging.
Start testing now
Unlike some of the other solutions that are rising up to replace cookies, anonymous data has actually been around for a while. Though the ways we use it and the technologies involved are becoming increasingly sophisticated, there’s no reason to ‘wait and see’ – you can start testing the power of anonymous data in your campaigns right now.
And one thing’s for sure – the marketers who get in early and work out the best way to use it to achieve their precise goals will be the ones best placed for when cookies are finally phased out.
Glossary of terms
Cookies – Little bits of data stored in your browser that allow consumers to be tracked around the web – to remember where they’ve been and what they’ve looked at. Soon to be a thing of the past.
Identity – The overarching term for the technologies and techniques advertisers use to differentiate between people on the internet so they can understand and target them more effectively.
Authenticated data – Data that’s obtained by someone logging in to a site or platform, usually based on long-lasting identifiers like email addresses and phone numbers.
Contextual Data – Contextual data is information that provides perspective into an event, person or thing. It provides a broader understanding by showing how disparate pieces of data relate to each other, placing them into a larger picture.
Anonymity – The measure of how possible it is to identify an individual consumer based on the data collected from them.
Programmatic advertising- Programmatic advertising is the automated buying and selling of online advertising. Targeting tactics are used to segment audiences using data so that advertisers only pay for ads delivered to the right people at the right time, and depend less on the “spray and pray” method of digital advertising.