At A Glance
Demographics alone can miss people who are likely to respond, so looking at how attributes work together can help you build more relevant audiences. See how Experian’s AI-enhanced modeling surfaces stronger combinations of predictive signals and validates them against known data to improve targeting within your existing workflow.Key takeaways:
- Combine attributes to uncover likely responders that individual demographic or behavioral signals may miss.
- Use AI to explore predictive patterns and interactions that are difficult to find through manual analysis alone.
- Validate promising signals against known seed or deterministic data, supported by quality inputs and human review.
- At Experian, our AI-enhanced modeling has produced a 10% average model lift across hundreds of models, validated against known seed or deterministic data.
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.
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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.
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