At A Glance
Consumer behavior shifts faster than traditional model inputs can track it, which leaves marketers spending against audiences that are less likely to respond. Experian's AI enhanced modeling applies AI to find and validate stronger predictive signals across our existing data and audience products, producing a 10% average model lift across hundreds of models. For marketers, that means better targeting and less wasted spend, without a new workflow.Consumer behavior keeps shifting across channels, and the signals that point to your most relevant audience aren’t always obvious. Experian’s AI enhanced modeling applies AI to find and validate stronger predictive signals across our data and modeling technology, so marketers can reach higher-potential consumers with less wasted spend.
A consumer may not fit an expected profile exactly, but still show behaviors that suggest strong relevance for a campaign, product, or offer. When audience strategies rely too heavily on static or less predictive signals, marketers can miss valuable opportunities, spend against less relevant consumers, and increase the risk of audience fatigue.
Stronger audience strategies start with stronger model inputs. This article looks at why signal quality matters, what better model inputs can mean for audience performance, and how AI can help identify more useful predictive patterns.
Why do traditional attributes alone leave opportunity on the table?
Traditional attributes, like demographic and static data, remain valuable. They help marketers understand who consumers are, how they live, and what they may care about.
Audience relevance is typically influenced by a combination of attributes rather than any single factor. A consumer may not match an obvious buyer profile yet still show signals that suggest they are likely to act. When audience strategies rely too heavily on familiar or less predictive signals, marketers may overlook high-potential consumers and continue spending against audiences that are less likely to respond. That can limit reach, reduce efficiency, and increase audience fatigue.
Advances in AI and modeling are helping marketers move beyond obvious or static signals toward more current, actionable audience intelligence, improving model performance, and strengthening the predictive power that supports better marketer outcomes. By making it easier to test more combinations and compare which inputs are more likely to predict a desired outcome, AI can help turn hidden patterns into useful signals for audience planning and activation.
How does AI find more useful predictive patterns?
AI helps modeling teams identify meaningful combinations and transformations of data signals that can be difficult to find through manual data science work alone. It surfaces correlations, suggests signal combinations, and accelerates the review of possible model inputs. That gives modeling teams a faster way to design, test, and compare more potential predictors for a defined objective.
AI also gives modeling teams another way to look at the data. Every data scientist brings valuable experience, context, and judgment to model development, but all people also bring their own perspectives and blind spots. AI can supplement that expertise by suggesting feature combinations or data patterns the team may not have known to explore.
In that way, AI acts less like a replacement for human expertise and more like an added team member. It introduces new possibilities, expands the range of inputs under consideration, and helps modeling teams see relationships that may not be obvious at the start.
Those suggestions still require human review, testing, and validation, but they can help data scientists learn from the data in new ways and produce stronger outcomes for marketers. For marketers, better inputs mean audiences that reflect current behaviors, interests, purchase patterns, and intent more accurately, which supports stronger campaign performance and better outcomes.
More data only matters when it leads to better signals. AI helps prioritize validated signals that improve predictive outcomes, rather than expanding inputs without a clear purpose. AI models can help identify nonlinear interactions and subtle patterns that may otherwise go unnoticed. With the right data foundation and validation process, those patterns can become stronger model features that support more relevant audiences.
Why does data quality determine AI model performance?
AI is only as useful as the data behind it. Predictive modeling depends on accurate, compliant, expansive, and relevant data. Strong signals need to be tested against known seed or deterministic data, which gives models a reliable benchmark for comparison. That validation is what separates useful predictive signals from interesting patterns.
By applying these AI-driven advancements to our modeling approach, Experian has seen a 10% average model lift across hundreds of models, validated against known seed or deterministic data.
For marketers, these improvements can translate into more efficient and effective outcomes across the use cases they rely on. With more predictive models powering audience inputs and decisions across planning, enrichment, modeling, onboarding, and activation, marketers can focus efforts on higher-potential consumers and move more quickly toward their goals.
What do stronger signals mean for marketers?
More useful predictive signals help marketers focus effort and spend on consumers who are more likely to match a desired outcome, such as purchase, response, retention, or upsell.
This is where Experian’s AI enhanced modeling comes in. AI enhanced modeling is built into our data foundation and modeling technology that applies AI to identify and test more predictive, current, and actionable signals across our data and audience products.
As part of our data foundation, AI enhanced modeling:
For marketers, this means better model outcomes across the use cases and applications you already rely on, including onboarding, modeling, Enrichment, Marketing Attributes, and audience activation, without adopting a new workflow.
How does AI improve model development speed and performance?
Consumer behavior will keep shifting, so audience strategies need model inputs that capture those changes and show marketers which consumers are more likely to act.
AI uncovers signal combinations that traditional exploration may miss, analyzes large and diverse data sets, and compares potential predictors faster. When paired with strong data, modeling expertise, and validation, these inputs support better model performance and more relevant audience decisions.
Our AI enhanced modeling brings together AI, data, identity, modeling expertise, and validation to identify more predictive, current, and actionable signals across the products and use cases you already rely on. This gives you better audience intelligence, more relevant activation, and stronger marketing performance, with responsible data use built into the process.
To see how AI enhanced modeling can strengthen your predictive audiences, talk to our team today.
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
Experian AI enhanced modeling is our approach to applying AI within our existing data foundation and modeling technology. It identifies and tests more predictive, current, and actionable signals across our data and audience products, without requiring a new workflow.
Experian AI enhanced modeling has produced a 10% average model lift across hundreds of models, validated against known seed and deterministic data.
Experian AI enhanced modeling doesn’t replace human data scientists. AI surfaces signal combinations and patterns a team might not think to test. Every suggestion still goes through human review, testing, and validation before it becomes part of a model.
Experian AI enhanced modeling strengthens existing use cases across onboarding, modeling, Enrichment, Marketing Attributes, and audience activation, using the workflows marketers already rely on.
Latest posts
Experian Marketing Services and Data Quality President Genevieve Juillard recently sat down with Zach Rodgers, host of the AdExchanger Talks podcast to discuss the future of identity, the importance of data transparency and privacy, and our recent acquisition of Tapad. Genevieve focused on the opportunity for our industry to reimagine an advertising ecosystem that is resilient and adaptable; one that takes advantage of emerging data and prioritizes data transparency and consumer privacy. She also discussed the importance of advertising strategies that put consumers at the heart of every decision and give them more control over their data. Genevieve shared with AdExchanger that Experian’s acquisition of Tapad, a global leader in digital identity resolution, was a natural fit for our company. Tapad’s approach and role in the ecosystem is very much aligned with Experian’s, which is to develop solutions that are resilient to industry and consumer changes. The combination of our capabilities supports interoperability across all types of identifiers, both online and offline, and will position us to help our clients navigate the post-third-party cookie world. To learn more about Experian’s plans to support an effective advertising ecosystem that will evolve with our dynamic industry, listen to the full podcast Embracing ‘Healthy Fragmentation’ In Ad Tech, With Genevieve Juillard. Get in touch
It’s been over a year since Google announced they’d be deprecating the third-party cookie and in that time there’s been a major focus on two types of cookieless identity solutions. Identity vendors and marketers are strategizing which of these two future solutions best fits their needs so they can achieve privacy-safe scale once third-party cookies are no longer available for use on Chrome. Let’s break down these solutions and the considerations marketers need to take into account when deciding what partners to move forward with in the future of identity resolution. Authenticated Traffic Solutions Authenticated traffic solutions (ATS) are a type of digital identification that asks the end-user to identify themselves via personal information, most commonly email address. Often, you’ll see self-authentication at the point of entry to a website that asks you to create an account or login immediately to access the content you are seeking. E-commerce sites use authentication to keep track of consumer purchases and inform advertising decisions for that customer; and publishers use it to tailor featured content, or, more importantly for this discussion, leverage it within the ad ecosystem for targeting. While authentication can provide very valuable user data for audience segmenting and targeting, it can be limited in scale for a single publisher to leverage and monetize on their own. That’s why some identity vendors have worked to integrate themselves within as many publisher authentication modules as possible, so that they can create an aggregate of scale for the ad ecosystem to tap into. But, even this isn’t going to deliver the reach marketers truly thirst for. Alternatively, Facebook has the scale for authenticated traffic, but they keep their data inside a walled garden, so the utility of those authenticated users is only valuable within the Facebook ecosystem. 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