Why Smarter Data Can Drive Ad Performance on a Smaller Budget

Budgets continue to shrink amid rising marketer pressures to deliver ROI, navigate fragmented media, and meet evolving consumer expectations.

This belt-tightening isn’t a passing trend; marketers are winning budget battles much less frequently. One area in which marketers can better utilize their budget is in data, by reimagining how they source, activate, and optimize it across various platforms.

To meet demands, brands and agencies must rethink their data strategy from the ground up. A revised data strategy can make all the difference, helping marketers to improve targeting, gain faster insights, and collaborate effectively with the right partner.

First-Party Data: A Foundation that Might Be Faulty

Amid evolving privacy regulations and browser changes, marketers have rightfully pivoted to first-party data strategies for identifying and engaging their customers. First-party data has many great qualities, such as being consented to, accurate, and often easy to collect via brands’ websites and other owned channels.

However, first-party data has its limitations. It’s often spotty, stale, and siloed across teams and platforms. Even experts at the major analyst relations firms agree. “First-party data will never tell us how customers interact with competitors, what other types of products they like or buy, or what they do when they aren’t engaged in one of your experiences,” said Forrester Analyst Zeid Khater.

What brands need now is a way to leverage all of the first-party data they have and augment it to derive more value, said Nate Carter, Vice President of Global Sales, Agency and Identity, at Dun & Bradstreet, a leading global provider of business decisioning data and analytics. “This allows them to be more accurate in terms of campaign performance and understanding who their audience is.”

To fill in these gaps, marketers and their agency partners are turning to third-party data sources to help enrich their existing data.

Third-Party Data: Creating a More Comprehensive, Actionable View

The third-party cookie debacle led many marketers to deprioritize third-party data. Not all third-party data is indeed created equally. But — and Khater agrees here, too — third-party data (along with cookies) isn’t going anywhere. In fact, it remains key to enhancing audience targeting and media efficiency.

The best strategy for budget-strapped marketers is to ensure they source more of the correct third-party data, rather than simply gathering more data in general.

“When you’re bringing in third-party data, you need to be pickier in your evaluation than you are with first-party data,” Carter said. “And when you know that you’re bringing in something of quality, it’s then figuring out how to join those two data sets.”

High-quality third-party data should be:

• Accurate, meaning correct in the context of the particular attribute marketers seek
• Complete, with data sets capturing all data points available for a given instance
• Standardized in a format necessary for inputting information and identifying duplicate data points
• Reliably sourced from a trusted industry authority
• Privacy-compliant with the applicable regulations for data collection, processing, and usage

But data alone isn’t enough. To truly unlock its value, brands and their agencies need the right tools.

AI as a Force Multiplier

Data stands to become a competitive advantage not only for brands themselves but also for their agency counterparts.

Brands are no longer solely relying on agencies for tasks such as creative development or media buying. To differentiate themselves and complement the brands they work with, agencies now require a technology and data skillset that’s unique to them. This approach also helps budget-constrained brands spend smarter. Instead of fighting for a budget to buy data, they can tap into their agency partners.

One way agencies can help marketing clients optimize their data usage is through artificial intelligence (AI) and machine learning tools. AI stands to transform creative development, media planning, and audience segmentation. For example, the large language models (LLMs) underpinning generative AI enable rapid development of multiple creatives, improving speed-to-market.

“I think there are two core functions of AI that we see as a data provider,” Carter said. “The first core function is making the data joins and processing our data in an environment faster, and allowing for much larger workloads. The second is the ability to both derive insights and activate insights. Similarly, you’re looking at being able to do things in a way that’s much more exciting and, I think, it allows for a lot more creativity.”

However, AI requires relevant information to base its assumptions and behaviors on. Its models must ingest data that meets the above five pillars to turbocharge marketer productivity and performance. As the old saying goes, when it comes to AI, it’s garbage in, garbage out. Good data is more likely to lead to good results, while poor data can cause hallucinations, biases, and other errors. Weak data management is a key factor in why approximately one-third of AI programs fail, according to Deloitte.

As other expertise shifts in-house at the brand or becomes commoditized among competitors, agencies that can tap into comprehensive data and smarter, AI-driven tools can redefine their value proposition. This brings us to a critical decision: choosing the right data partner.

How to Select the Right Data Partner

There are really three main criteria agencies must prioritize when considering a data partner to help enhance marketer outcomes. This isn’t a multiple-choice exercise: the right partner must tick all of these boxes to be worthy of evaluation. In each area, here are some starter questions to consider asking a prospective data partner:

1. Transparency and ethics

Is the data ethically sourced and compliant with privacy regulations? What’s the methodology? Are they transparent? Can they explain how they’re aggregating their data?

For example, the Dun & Bradstreet D-U-N-S® Number — a unique, nine-digit identifier for businesses — ties data to a company, providing a precise and uniform way to manage business attributes and information.

“We can go back and match D-U-N-S Numbers to customer acquisition campaigns that the agency’s running,” Carter said. “So, you targeted this business, they purchased it, and now we can also do lifetime value modeling. And that’s allowed us to look at ‘Is the audience effective?’ but also certain channels where we can bring that data in.”

2. Integration capability

Can they effectively integrate third-party data with first-party data to enrich your understanding of existing audiences? Does the data plug into your media stack and analytics tools? Can you integrate with customer relationship management (CRM) systems to enhance existing data with the latest insights? Does data sit in a one-stop shop as an organic component of marketing campaigns, instead of having to import it?

3. Strategic, future-proofed alignment

“Is the way that a data provider gathers data sustainable for the next 50 years?” Carter asked. “Are they operating in a way that is dependent entirely on current technologies, dependent entirely on whatever’s popular now, and they’re going to figure it out as they move along? Or do they have a long-term vision and plan to be in this space? And is that vision really at their core?”

With the right partner, data is more than an asset; it becomes a competitive advantage. And while budget constraints are real, so is the opportunity to innovate with data and AI for both brands and their agency partners. Reevaluate your data strategy, embrace AI, and choose a data partner who aligns with your goals. Improving ROI and performance isn’t about spending more. It’s about spending and strategizing wisely.