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.
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In our Ask the Expert Series, we interview leaders from our partner organizations who are helping lead their brands to new heights in adtech. Today’s interview is with Georgia Campbell, Head of Strategic Partnerships at Kontext. What types of audiences does Kontext provide, and what are some top use cases for these insights in marketing strategies? Kontext leverages its 1st-party, deterministic shopping data to generate real-time online audiences. What sets Kontext apart is our ability to see the entire consumer journey, from shopping interest to intent and purchases, at a SKU-level. This comprehensive visibility allows us to create purchase-based audiences across various consumer verticals, such as frequent online shoppers, consumers shopping for beauty, segments using Mastercard, or Black Friday enthusiasts. Our data engine, built on a foundation of approximately 100 million consumer profiles and over 10 billion full-funnel, real-time shopping events, enables the creation of precise audience segments. This real-time 1st-party shopper data is invaluable for partners aiming to understand and engage with consumers more effectively. Whether a brand wants to activate past shoppers in a specific category or reach new audiences with a propensity to buy, Kontext provides the insights needed to make informed decisions. Some examples of audience types include these (and hundreds more): In-Market Shoppers: Consumers showing high intent to purchase specific categories, like skincare or electronics, based on recent online behavior. Past Purchasers: Shoppers who have made verified purchases within specific time frames, such as beauty products in the last 18 months. Frequent Shoppers: High-frequency buyers identified through repeated purchasing behaviors. Seasonal Shoppers: Consumers active during key shopping seasons, like Black Friday, Mother’s Day, Valentine’s Day, etc Premium Buyers: Shoppers who used a premium CC (eg. Amex) and a higher AOV (average order value) Beauty Buyers: an audience that has indicated intent to purchase beauty products (deterministic past purchasers also avail) By using Kontext data, brands can identify the right audiences across multiple verticals, such as retail, CPG, health & wellness, auto, business, energy & utility, financial, and travel. Additionally, our collaboration with Experian allows further refinement of these audiences through layered data from specialty categories like demographics, lifestyle & interests, mobile location, and TV viewing habits. How is Kontext’s data sourced, and what differentiates it from other data providers? Kontext’s data is unique because it is deterministic, 1st-party, and collected as transactions occur. We capture the entire path-to-purchase, down to the SKU-level product detail, across 100 million consumer profiles and more than 10 billion real-time shopping events. Our proprietary technology, embedded in widgets across our 5 million premium online destinations, tracks the full consumer journey—from reading an article of interest to clicking on our dynamic commerce modules, adding items to cart, and completing purchases. This real-time data collection ensures there is no lag between digital events and their connection to consumer profiles. Unlike other providers, we do not aggregate data from multiple platforms; instead, we focus on building our models and insights based on authentic online consumer behavior. Our data stands out due to its: Deterministic Nature: We capture 1st-party data as transactions occur (all in real time) Full-Funnel Coverage: We capture consumer journeys from awareness to purchase, providing a complete view of consumer behavior. Real-Time Insights: Our data engine processes events in real-time, enabling timely and relevant marketing actions. How does Kontext ensure the accuracy and reliability of its audience data? Kontext ensures accuracy and reliability through our unique technology and direct data sourcing. By not aggregating data from other platforms, we maintain control over the quality and integrity of our insights. Our continuous investment in refining our models around online consumer behavior further enhances the precision of our audience data. What types of brands or verticals might resonate the most with Kontext audiences for activation? Any brand looking to understand and activate online shopping behavior – informed by 1st-party transaction data – will resonate with Kontext audiences. Essentially, any vertical that benefits from understanding real-time shopping behaviors, such as retail, health & wellness, auto, and financial services, will find our data invaluable. We have particularly strong insights in beauty, hair care, health & wellness, and values-based online shopping habits, as well as the food & beverage space. Retail & Consumer Goods: Leveraging shopping behavior data for targeted campaigns. Health & Wellness: Identifying consumers with specific health and wellness interests. Automotive: Targeting potential buyers of electric vehicles or eco-friendly products. Financial Services: Engaging high-value shoppers with premium credit card usage. And many more How does Kontext’s data help advertisers navigate the challenges posed by the deprecation of third-party cookies? As third-party cookies become less reliable, Kontext’s 1st-party data becomes invaluable. Our deterministic data engine, which does not rely on cookies, offers: Direct Consumer Insights: Accurate and consented data directly from consumer interactions. Privacy Compliance: Our data collection methods are fully compliant with privacy regulations, ensuring secure usage. Cross-Device Coverage: We use verified digital identifiers, allowing seamless unification and targeting across multiple devices. What measures does Kontext take to maintain data privacy and compliance, and how does this benefit advertisers? Data privacy and compliance are fundamental to Kontext. We meet or exceed all privacy compliance and security standards, ensuring that our data sourcing and usage are transparent and comply with regulations (CCPA, CPRA, VCDPA, etc). Kontext prioritizes data privacy and compliance through: Consented Data Collection: All data is collected with explicit consumer consent. Robust Security Protocols: Data is encrypted and secured with industry-leading practices. Compliance with Regulations: We adhere to global privacy laws, including GDPR and CCPA. User Control: Consumers have the ability to opt-out and manage their data preferences. Can you share success stories / use-cases where advertisers significantly improved their campaigns using Kontext’s data? To give you a sense of how Kontext data can be applied, here are two use-cases: Beauty Brand Campaign: An agency hoping to activate an audience of beauty purchasers for a Major Beauty Brand could utilize Kontext’s custom audience of high-value beauty product purchasers. By targeting those consumers who had bought similar products in the last 12 months and had an average cart size of over $50, the campaign would significantly increase performance and ROAS. Electric Vehicle Launch: For a major auto manufacturer’s EV launch, Kontext could be used to identify eco-friendly consumers who had not yet purchased an EV but had shown interest in sustainable products. This precise targeting could lead to higher engagement and conversion rates for the campaign. Thanks for the interview. Any recommendations for our readers if they want to learn more? For those interested in learning more about Kontext, reach out for a personalized consultation. Contact us About our expert Georgia Campbell, Head of Strategic Partnerships, Kontext In her current role as Head of Strategic Partnerships at Kontext, Georgia plays a pivotal role in shaping the company’s strategic direction within the data space. With a deep-seated expertise in leveraging data to drive impact for companies, Georgia has been forging key partnerships that enhance the effectiveness and reach of Kontext’s offerings. Georgia comes from a background in emerging technology, where she has been focused on cultivating partnerships and employing data-driven approaches to spearhead market expansion efforts. She started her career in finance, managing investments across equity, debt, and alternative assets at Brown Advisory. In this Q&A, Georgia shares her insights on Kontext’s Onboarding partnership with Experian, offering perspective on how Kontext’s unique insights can unlock new opportunities for advertisers and brands alike. Latest posts
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