At A Glance
Artificial intelligence can improve marketing performance when the data behind it is complete, accurate, and governed. This article explains three data investments that will improve AI’s value in marketing and shows how an AI-ready data foundation can help teams plan, activate, and measure.Advertising has entered a practical AI era, not the sci-fi version. Teams are already using models to predict outcomes, automate decisions, and tailor creative at scale. In plenty of cases, the technology does exactly what it should.
When results plateau, it’s rarely because AI “doesn’t work.” More often, it’s because the inputs aren’t built for what AI is trying to do. AI doesn’t invent customer understanding, but amplifies the foundation you feed it, good or bad. The upside is encouraging: when brands treat data readiness as a growth move (not an IT chore), AI becomes faster to activate, easier to trust, and simpler to prove.
Here are three data gaps that quietly cap AI’s value in marketing, and the investments that open up more room for measurable impact.
1. Why first-party data only gives you part of the picture
First-party data gives marketers a valuable view of customer activity, but it rarely tells the full customer story on its own. AI performs better when models can see both direct brand interactions and broader context, such as life stage, media behavior, and shifting intent.
First-party data captures how customers interact with your brand across email, websites, apps, transactions, and loyalty programs. First-party data is excellent at showing what someone did with you, but it’s less reliable at revealing who they are outside of those interactions. This narrower view can leave AI models optimizing around incomplete customer understanding.
A grocery chain may know a household buys cereal every week. But that doesn’t automatically tell you whether they’re a new parent, recently moved, in-market for a vehicle, or more likely to respond to a premium offer than a discount.
There’s another limitation to first-party data: AI tends to optimize around the patterns it can already see. When models rely primarily on first-party data, they often become highly effective at finding more customers who look like existing ones. While that can improve efficiency, it can also cause AI to overlook emerging audiences, changing consumer behaviors, or new sources of demand that aren’t yet reflected in the brand’s customer base.
AI thrives when context is available. Without it, models still produce outputs, but they’re forced to guess more often than anyone wants to admit.
Invest in the data foundation: Enrich for decisions, not vanity
To make first-party data more useful for AI in marketing, marketers should:
Brands can close these gaps by enriching first-part data with high-quality demographic, behavioral audience insights that support planning, activation, and measurement. Experian helps marketers build a more complete customer view by connecting first-party data with trusted marketing data and audience insight. When first-party data becomes more complete and consistent, AI stops optimizing around partial truths and starts driving decisions you can stand behind.
2. Why stale identity makes AI scale the wrong patterns quickly
AI can move fast, which makes identity accuracy and data freshness central to performance. If customer records are duplicated, outdated, or mislinked, AI can learn from the wrong signals and repeat those errors across campaigns.
A common example is simpler than people expect: a profile tied to the wrong household, or a record carrying outdated attributes. Even with sophisticated bidding, frequency, and creative selection, you’re still serving the wrong message to the wrong person, just more efficiently. This is where many “AI didn’t work for us” stories start. The more optimistic framing is that this is also where many turnaround stories start.
Invest in the data foundation: Make “one person” actually mean one connected view
Identity resolution gives AI a more stable base for targeting, personalization, and measurement. Marketers can improve that base by reducing fragmentation before data reaches the model. To make identity more reliable for AI in marketing, marketers should:
Accurate identity resolution is central to effective AI in marketing. When customer records are unified and current, AI models can recognize people and households more accurately, which improves targeting, personalization, and measurement. Fragmented or outdated identities can create errors, from duplicate messages to irrelevant offers.
Experian helps marketers organize, connect, and expand their data into a connected foundation, so teams can act on insights across systems, channels, and tools.
When identity and freshness hold up, AI has stable ground to stand on, and gains show up faster and more reliably.
3. How data governance makes AI scalable
Clear data governance helps AI programs scale. When teams know how data can be accessed, shared, and used, they can scale AI with greater confidence and fewer operational risks. As AI moves deeper into marketing workflows, data becomes more valuable, not only for targeting but for model training, personalization, and measurement.
That value cuts both ways. Sloppy access controls, unclear permissions, and vendor sprawl can erode customer trust and dilute business advantage. And when teams don’t feel confident in governance, they hesitate to scale. AI becomes a pilot project instead of a business capability.
The good news is that brands don’t have to choose between using data and protecting it. The winners operationalize both.
Invest in the data foundation: Treat governance as performance infrastructure
Data governance isn’t separate from AI performance and is the layer that helps leaders scale what works. To make data governance practical for AI in marketing, marketers should:
When teams understand how data can be accessed, shared, and used, they can move faster. Experian supports responsible data practices by building protection standards into activation workflows. That includes defining access by role, guardrails for partner data use, and compliance with privacy regulations. With these controls in place, brands can scale AI programs with greater trust.
The payoff: AI that’s faster, more relevant, and easier to prove
AI can raise the ceiling for advertising, but it can’t compensate for incomplete customer views, mislinked identities, or weak controls. It will accelerate what’s already happening, good or bad. The most successful brands will be the ones that make three investments: completeness, accuracy, and governance, so AI produces outcomes that can scale.
When the data foundation is ready, AI stops being a shiny layer on top of marketing and becomes a practical advantage inside it. Contact us to talk about building an AI-ready data foundation that supports stronger planning, activation, and measurement.
About the author
Jeremy Meade
VP, Data Operations & Governance, Experian
Jeremy Meade is VP, Data Operations & Governance, at Experian Marketing Services. With over 15 years of experience in marketing data, Jeremy has consistently led data product, engineering, and analytics functions. He has also played a pivotal role in spearheading the implementation of policies and procedures to ensure compliance with state privacy regulations at two industry-leading companies.
FAQs
AI-ready marketing data is complete, consistent, current, and governed enough for models to use with confidence. It connects customer activity, identity, and outcome signals so AI can support better planning, activation, and measurement decisions.
First-party data shows how customers interact with a brand, but it may not show broader context such as life stage, household changes, media behavior or shifting intent. AI needs that added context to reduce guesswork and help marketers make more relevant decisions.
Identity resolution affects AI performance by helping models recognize people and households more accurately across systems and channels. When identities are fragmented or outdated, AI can repeat the wrong patterns through duplicate messages, irrelevant offers or misread campaign signals.
Experian helps marketers build an AI-ready data foundation by connecting first-party data with trusted marketing data, audience insight, and identity resolution. Experian’s identity foundation helps teams organize, connect and expand customer data so AI can support stronger planning, activation and measurement.
Data governance matters for AI marketing programs since teams need clear rules for access, sharing and use before they can scale with confidence. Strong governance helps brands protect customer trust, reduce data sprawl, and turn AI from a pilot into a business capability.
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Marketing data and identityStudy reveals that brands with more mature identity programs were significantly more likely to be successful in achieving their key objectives Tapad, a part of Experian, a global leader in cross-device digital identity resolution and a part of Experian, has commissioned Forrester Consulting, part of a leading research and advisory firm, to conduct a new study that evaluates the current state of customer data-driven marketing and explores how marketers can use identity solutions to deliver privacy safe and engaging experiences, in an evolving data landscape. The study highlights the changing ground rules for digital marketing and the threat that poses to marketers’ ability to deliver against long standing KPIs and campaign goals. Nearly two-thirds (62%) of respondents said that the forces of data deprecation will have a significant (40%) or critical (21%) impact on their marketing strategies over the next two years. Among those surveyed, identity resolution strategies have surfaced as an opportunity to create more powerful customer experiences, with 66% aiming to have it help improve customer trust and implement more ethical data collection and use practices, while nearly 60% believe it will point the way to more effective personalization and data management practices. Although organizations are eager to implement identity resolution strategies, a complex web of solutions and partners makes execution a challenge. For example, respondents report using at least eight identity solutions on average, across nearly six vendor partners, and they expect that fragmentation to persist in the ‘cookieless’ future. Additionally, brands’ identity resolution technologies typically represent a patchwork of homegrown and commercial solutions. Eighty-one percent of respondents use both in-house and commercial identity resolution tools today, and 47% use a near-equal blend of the two. Despite the challenges, many brands have the foundation for a strong identity resolution strategy in place, and they are thriving as a result. Specifically, more mature brands were 79% more successful at improving privacy safeguards to reduce regulatory and compliance risk, 247% more successful at improving marketing ROI, and over four times more effective at improving customer trust compared to their low-maturity peers. Additional insights include: Marketers Are Increasingly Playing a Key Strategic Role Within the Organization, But There is a Mandate to Demonstrate Value. Nearly three-quarters of respondents in our study agree the marketing function is more strategically important to their organization than it used to be, while almost two-thirds agree there’s more pressure than ever to prove the ROI or business performance of their activities. Consumers Expect Brands to Deliver Engaging Experiences Across Highly Fragmented Journeys: Tapad, a part of Experian found that 72% of respondents agree that customers demand more relevant, personalized experiences at the time and place of their choosing. At the same time, 67% of respondents recognize that customer purchase journeys take place over more touchpoints and channels than ever, and 59% of respondents agree that those journeys are less predictable and linear than they once were. Marketing Runs on Data, But the Rules Governing Customer Data Usage are Ever-Evolving: According to the study, 70% of decision-makers agree that consumer data is the lifeblood of their marketing strategies – fueling the personalized, omnichannel experiences customers demand. At the same time, 69% of respondents recognize that customers are increasingly aware of how their data is being used. At least two-thirds agree that data deprecation, including tighter restrictions on data use (66%), as well as operating system and browser changes impacting third-party cookies (68%) means that legacy marketing strategies are unlikely to remain viable in the long-term.“ Our latest survey findings give us a better understanding of how our customers and other companies around the world are trying to master the relationship between people, their data and their devices,” said Mark Connon, General Manager at Tapad, a part of Experian. “This research shows why it’s fundamental for the industry to continuously work to develop solutions that are agnostic. Tapad, a part of Experian has worked tirelessly to deliver on this with our Tapad Graph, and by introducing solutions like Switchboard to help the evolving ecosystem and in turn helping customers reap the benefits of better identity in both short and long-term.” The study is founded on an online survey of over 300 decision-makers at global brands and agencies, which was fielded from March to April, 2021. Data deprecation and identity are fast-developing, moving targets, so this study delivers targeted insights and recommendations for how to prepare for coming shifts in customer data strategies – whether they manifest tomorrow or a year from now. Get in touch
With the recent announcement of delaying third-party cookie deprecation to 2024, the industry has more time to rethink how to identify and communicate with consumers.