Use AI to weather the upcoming data deprecation storm

Last summer’s announcement that Google’s third-party cookie deprecation has been pushed out to 2023 drew cheers, yawns, and everything in between. But the question remains: what will advertisers and marketers do with the extra time? Will they squander it and resist evolution until the bitter end? Or will they use it to make real changes in pursuit of better business outcomes and improved customer experiences?

There’s a scene in “My So-Called Life,” the 90s TV drama that followed a group of angsty teens as they navigated the pitfalls of a suburban high school. The protagonist, Angela Chase, is huddled in the girls’ bathroom, frantically studying for a test, and demoralizing herself for not doing it sooner. Suddenly, another student bursts through the door and triumphantly shouts, “The copy machine ate our geometry test!” Given the time period and its lack of digitization, this meant that the crowd of girls huddled in the bathroom had a one-day reprieve and all cheered triumphantly, grateful for the extra study time.

We’ve now arrived at our “the copy machine ate our geometry test” moment. This tipping point has been a long time coming, whether or not Google pulled the plug on cookies in Chrome. Without data deprecation, consumers continue to opt-out of third-party cookie tracking and data storing amid ongoing privacy concerns, as they have done for the past several years. Various sets of government regulations around the globe are in response to these concerns. The browser and operating system restrictions, as well as the tightening of data portability rules within walled gardens, have signaled major disruptions to the digital marketing environment for several years. This is why closing the door on legacy data practices is a golden opportunity to deliver customers better experiences, a goal that continues to elude many of us in the existing landscape.

Overall, consumers aren’t happy with the State of the Union between themselves and the digital marketing industry. We struggle with frequency, relevance, context, and personalization in our engagement with them. We engage too often with irrelevant content at the wrong times and deliver either not enough or too much personalization to consumers. Not only that, but we’re getting it wrong, and we have been for quite some time. And the number of consumers who’ve installed ad blocks since 2019 proves it: Statista reported in March that there are more than 763.5 million ad block users worldwide. We’re driving consumers away from wanting to even have conversations with us.

But it’s not that they don’t want to hear from us, it’s the way we’re speaking to them that isn’t working. When there is a true value exchange, consumers welcome engagement.  According to Merkle’s “Consumer Experience Sentient Report” released in April which reviewed how consumers feel about brands using their data in digital marketing and advertising, about half of adult US internet users said that when brands use their data in advertising, it helps them discover (50%) and find (49%) products and services that interest them — but 44% said this often feels invasive.

Undoubtedly, there will be brands and other organizations in the digital ecosystem that will resist change and hold onto legacy practices until the bitter end of data deprecation. But If you want to be in the better outcomes and experiences camp, even if you haven’t started your transformation, now is the time, and here’s how to get started.

Commit to better privacy and transparency on all your channels

This may seem obvious, but many organizations do not lead with consumer trust, privacy, and transparency when it comes to digital marketing and advertising. And although it’s in the fine print, respecting consumer privacy always makes for a better outcome. Always be transparent about how you use customer data. Be responsive when the consumer asks you for information, and deliver clear and understandable explanations of the types of data you collect and how you use it.

Audit your data strategy and MarTech stack

Many current MarTech stacks look like a jumble of point solutions to deploy segment-based, fixed marketing campaigns, and as such are leveraging multiple kinds of data. Spend the time to audit exposure – you might have external partners in your media ecosystem and not even know what types of data segments are being utilized. What do your data usage, audience targeting, and search capabilities look like? What steps can you take in the data deprecation future to deploy new types of targeting, modeling, and beyond that aren’t reliant on third-party data and technology? Are there new partnerships you can create to access the data you need to round out your strategy? Do some of your technologies need to be replaced?

Various cookie alternatives are emerging to fill the data gap looming for many organizations. But these solutions will not be a like-for-like replacement. As alternatives remain largely unproven, it becomes even more essential to identify where your business needs to strengthen or replace existing capabilities. For example, in 2020 Forrester Research defined a new type of data, zero party data, as “data that a customer intentionally and proactively shares with a brand, which can include preference center data, purchase intentions, personal context and how the individual wants the brand to recognize her.” It is essentially a subset of first-party data that gives its users broader contextual signals about a consumer with their consent. But this approach assumes that consumers will want to participate and on the scale that advertisers need to make the investment worth the reach. Also positioned as a viable alternative when facing data deprecation are data cohorts where organizations share first-party data to create types of interest-based segments in privacy compliant ways.  Google’s FloC is an example. The efficacy of this approach is yet to be determined.

Invest in AI and centralize all your data

Today’s consumer moves through the web at an accelerated pace, constantly changing their context. If we as marketers don’t move with them, we miss the opportunity to connect. Fixed campaigns and rules-based customer journeys aren’t about what the customer needs or wants at the moment, they are about what products and services we want to talk to them about. The only way to keep up with both the consumer and the industry as it evolves is to implement solutions that rely on AI and machine learning.

Artificial intelligence-backed marketing and advertising solutions can ingest your customers’ signals as they move through the web and interact with your brand to deliver highly relevant, perfectly timed customer interactions. Most leverage either predictive or adaptive modeling capabilities to predict consumer behavior, even in the face of a data deprecation storm. The most accurate tools leverage both. Predictive modeling and analytics are not new to marketing and advertising technologies. Predictive analytics ingests data from historical customer interactions to try and predict customer behavior, and most of the time is created by data scientists. Conversely, adaptive modeling starts with no data inputs and learns in real time from data gathered during a customer interaction. Models auto-create and optimize on the fly. The key to hyper-personalized, perfectly timed customer engagements is the capability to ingest immediate inputs and deliver an interaction at that moment.

These capabilities greatly reduce, and in some cases eliminate, the need for external data sources. Especially because AI empowers businesses to aggregate and centralize data from all over the organization for use in marketing initiatives. We keep hearing that there are brands across various industries that don’t have access to enough first-party data to survive in a post-cookie digital landscape, but many organizations are sitting on a trove of it. However, because it may be sitting outside of marketing in areas of the business such as finance, customer service, operations, etc., and because they don’t have solutions that can stitch it together, it goes wasted.

Making these changes may seem daunting because they are. Major strategic and organizational shifts are a heavy lift, but the delay has given us the gift of time to make them. And because we aren’t time-crunched, we can do it right and get away from the cobbled-together stacks of the past. We are in the middle of a digital revolution, and we are running out of time to make the big changes that brands need to make to weather the upcoming storm data deprecation will create. And isn’t it always better to fix the roof when it’s dry outside?