Some context around the future of contextual marketing

Google recently announced that third-party cookies won’t crumble on Chrome until 2024.

Before we let all of the crumbs blow away, all of us know that contextual advertising is more than waiting in the wings. Thankfully, there are several use cases, success stories and wins to show how and where the power of context matters.

Verve Group has been thinking about a world without identifiers for quite some time. Moments.AI™ , our contextual advertising platform has uncovered new ways for brands to find ways to meaningfully connect with the audiences that matter most to them.  In fact, we are working with Getty Images to harness the relationship between quality imagery and meaningful content.

While our focus here is on contextual marketing/targeting in its present form, we also need to think about its future form as well. In order for us to keep walking, we need to nurture and evolve ‘in-the moment’ contexts into meaningful cohorts based on collective moments of interest. Context, in other words, is a piece of the pie.

At Verve Group, we have also been thinking about it through the lens of other solutions that deliver similar value. Those platforms offer us all ways for contextual targeting to improve. And because we know that measurement is at the heart of what we do, we need to also think long and hard about how to best measure performance without the use of identifiers.

Let’s start with the “other platforms” in discussion. In 2021, Verve Group launched ATOM, or Anonymized Targeting on Mobile. In response to Apple’s new privacy strategy on iOS, ATOM was designed to provide a mobile-first, machine-modelled cohort targeting solution, that’s built on anonymous data assets like context. Google’s Topics operates similarly on interest-based models. Topics is a follow up to Google's learning and feedback from the earlier FLoC trials.

The same approach to iteration and education needs to take place on the measurement front. How can ad performance be measured without identifiers? 
 

Media Mix Modelling 

Media mix modelling is a statistical method based on an extensive set of historical data. This method is great for forecasting, but has its weakness when it comes to measuring the impact of advertising for a product launch when no historical data has been recorded. To inform media mix modelling, campaigns are often turned off entirely to arrive at a baseline for measurement. However, this practice of turning off any marketing activity entirely is often not feasible for advertisers.
 

Incrementality 

Incrementality studies can range from “simple” A/B testing to testing by geography (e.g. promoting a product launch in one region while letting it run by itself in another). These options, however, become complex when many different messages are being tested in parallel. Marketing impact can also be measured without identifiers in consumer surveys or panels. This method can grant insights into consumer demographics, share of voice, purchase intent, and more. However, results rely on active survey or panel participation. An automated algorithmic based approach to incrementality is casual inference. By adding all possible factors to the equation, these models can granularly analyse which marketing activity leads to an increase in conversion results, and which marketing practice might lead to cannibalisation. 
 

Cohort-based measurement 

Cohort-based measurement finds its application with solutions such as data clean rooms or contextual advertising. While data clean rooms bundle different advertisers’ and publishers’ user data within a black box, any participant can easily send queries to retrieve cohort-based insights. At Verve Group, we create lookalike cohorts for traditional ad campaigns, based on the context into which an ad is embedded. By taking into consideration the environment in which high propensity audiences are navigating, we can add the brand safety factor back into the picture. 

Running a campaign for trailblazers for a major international tech company, we found a 300% uplift in CTR and a decrease of 96% in brand safety risk when targeting against contextual audiences, compared to traditional campaigns. An assortment of different levers, from brand safety risk to recency score help us optimise our campaigns, we can tailor any campaign towards the right target audiences.  And we are not stopping here.

All of these approaches protect user identity but still deliver valuable insights to the advertiser, and some of them are already available solutions.

We know there is a broad horizon to the future of contextual marketing.  But we need to remember that there are more than one ways to do things, and that requires each of us to work together and evolve different perspectives, or should I say, context. Don’t think of the next two years as a reprieve from a cookieless reality. Rather, think of them as much-needed time to reimagine campaign performance across channels.