Marketers should turn to AI to drive more value from their data assets

By Amol Waishampayan, CTO, fullthrottle.ai

Is first-party data letting down brands and advertisers?

A recent discussion with industry leaders from Snowflake, Salesforce and LiveRamp highlighted the many ongoing challenges companies face as they shift their attention from third-party cookies to first-party data. Martin Kihn, a senior vice president at Salesforce, went as far as saying that companies are experiencing a “trough of disillusionment” when it comes to first-party data.

That frustration, combined with Google’s recent change in stance on the deprecation of third-party cookies, may lead some companies to take their foot off the gas when it comes to investing in first-party data. But falling back into easy, comfortable routines now would be a mistake with long-term implications: third-party cookies will eventually go away one way or the other (either by force, regulation or opt out), and marketers must have a strategy to leverage first-party data as that happens. Plus, first party data can be a treasure trove of untapped value.

Adopting a first-party data strategy is undoubtedly harder than marketers expected, but the rewards are clear: first-party data has proven to be better than third-party cookies at identifying target customers and providing useful insights. First-party assets also allow brands to more effectively personalize their marketing messages; according to research from Segment, “78% of companies consider first-party data to be the most valuable source of data for personalization.”

The current challenges in adopting first-party data are a speed bump, not a barrier. To get over the hump and start moving forward, marketers need to take advantage of tools and strategies that help simplify the process and align with their business goals.

Recognizing and overcoming first-party data challenges

For companies used to the ease of established third-party data tools, it’s understandable why first-party data can feel overwhelming. But as brands and marketers discover new use cases and applications of first-party data, this approach will continue to disrupt the marketplace independent of what happens with third-party tools. Companies can use their owned data assets to create increasingly precise and detailed profiles of their customers; drive predictive analytics and modeling; and enhance targeting for marketing campaigns. Most importantly, brands can achieve these goals while remaining fully compliant with relevant privacy and security regulations.

But some challenges still remain:

• Scale: This has always been the draw of third-party data — a seemingly limitless marketplace of data points to inform marketing campaigns. A company’s first-party data assets will be limited by what they’re able to collect, organize and deploy. For companies with an immature data practice, this can feel like an insurmountable hurdle.
• Interoperability: An organization may be sitting on a mountain of data, but is it able to match that data and use it across different tools and platforms? Data hygiene and interoperability are key to overcoming the scale challenges mentioned above. Companies must store a robust mix of data together and use a proprietary or third-party system to match multiple data points to the same user. Nailing down interoperability is also essential to manage frequency and avoid the consequences of duplication.
• Accuracy: We’ve all heard the phrase “garbage in, garbage out” as companies get their first-party data resources in order. But the point stands — inaccurate or incomplete data could have advertisers aiming and firing in the wrong directions.

In order to succeed over the long term, companies must be able to generate more first-party data, organize it more effectively to ensure interoperability and accuracy, and activate it for practical use cases.

The roadmap to long-term data success

Marketers must take key steps to veer away from the trough of disillusionment and begin building a future-proof first-party data strategy. AI-fueled solutions are entering the picture at just the right time to make it easier and faster for marketers to achieve these goals.

Most importantly, companies must invest in finding and mining more sources of first-party data — leaning into the current period of disruption and taking advantage of AI to scale their resources. From email sign-ups to transaction data to loyalty programs to social media activity, customers provide companies with an ever-growing amount of data. The key is to identify, capture and organize that data at every available opportunity.

Over the long term, marketers can lay the groundwork for growth by simplifying deployment of first-party data. This will require marketers to invest in key AI-driven technologies and partnerships — connecting to the supply side, integrating bidders, and identifying the vendors that will help them drive more value from their data assets. Focusing now on a logical, interoperable infrastructure will make it easier for companies to design and assess campaigns that deliver on their sales and marketing goals.

Rather than getting bogged down in frustration, marketers should embrace this opportunity for creativity — identifying new use cases that leverage AI that can be tailored to their business objectives. This isn’t a moment for one-size-fits-all approaches; the more a company is able to lean into its unique assets, competitive advantages, and business strategies, and envision what is possible with AI, the more successful they will be in leveraging first-party data.

Where marketers go from here

With so much uncertainty and change in the marketplace, what should marketers do now to set themselves up for the future?

One thing is for sure: ongoing signal loss is unavoidable. Ignoring the inevitable — or getting discouraged by current challenges with first-party data — will only put companies further behind as third party data resources diminish.

For companies to overcome the so-called “trough of disillusionment,” they need to identify the solutions and strategies that deliver clarity. They need to embrace AI. Not just as a term but as a use case. Experiment often, adjust quickly, and commit to the approaches that deliver proven value. Above all, don’t stop moving forward. The rest of the marketplace won’t wait for you.