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
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 Samantha Zhang, Senior Data Scientist, and Jim Meyer, General Manager of the DASH TV Universe Study at the Advertising Research Foundation (ARF). DASH is an annual tracking study conducted by the ARF to define and better understand TV audience behavior and household dynamics. What does DASH measure, and how does it help the industry understand TV consumption today? By capturing hundreds of individual- and household-level data points from each respondent in a rigorous and nationally projectable sample, DASH creates a comprehensive picture of U.S. consumer TV “infrastructure” – how America watches. Core elements in DASHElements that create context in DASHTV setsLocation | brand | smartness | service modes | sources DemographicsConnected devices Game consoles |video players | streaming devicesYesterday viewing Daypart | TV/device genre | Out-of-home viewingMobile devicesOwners | sharing usersShoppingOnline and in-store | Exposure to major RMNsInternet serviceModes | ISPs | connectivity by device Streaming audio Streaming TVSVOD/AVOD tiers and sharing | FAST Email accounts and apps Live TV Modes of access | including casting from devices Social media For example, DASH gathers: Data on every TV set, including brand, room location, age, “smartness,” and connection devices and modes Household connectivity and video service data, even in homes with no TV set Internet Service Providers (ISP) and TV service usage, including Multichannel Video Programming Distributors (MVPDs), virtual vMVPDs, streamers (ad-supported and premium), and Free Ad-Supported Television (FAST) channels Person-level ownership and usage of video-capable mobile devices, including smartphones, tablets, and laptops Measures of viewing and co-viewing across dayparts, devices, and services Additional modules covering shopping and retail media networks, streaming audio, social media, email, and apps Broad coverage and granularity make DASH a uniquely robust source of truth for practitioners across the industry, including measurement experts and ad programming strategists. DASH also reports regularly (and publicly) on key industry dynamics. DASH identified a growing segment of device-only viewers – now nearly 9 million households that watch TV, but do not own a TV set – and highlighted the implications of that trend for traditional ratings systems based only on households with TV sets. Households (HHs – million)2025 HHs (M) U.S. penetrationChange vs. 2024 (M)Total US134.8100%+2.7Connected TV (CTV)114.685%+2.1TV (Set)124.292.2%+1.1Device-only8.86.6%+1.6TV-Accessible133.198.7%+2.7 DASH called out the rise in app-based pay TV and proposed a new connection framework that better represents the modern TV world, in which linear and streaming overlap. DASH also defines the universes of households reachable with advertising. This graphic, for example, shows how all ad-supported linear and streaming properties in aggregate define the true scale of TV advertising. While 35 million households (and growing) are reachable only with streaming ads and 13 million (and falling) only with linear ads, most households are reachable with both, underscoring the importance of understanding the “overlap.” Who uses DASH data, and what decisions does it help inform? There are three primary users of DASH, each with its own use cases: Measurement providers, including Nielsen, use DASH to calibrate viewership data, turn household data into persons data (and vice versa) and estimate potential reached audiences–what the providers call media-related universe estimate (MRUEs)–for the calculation of ratings. Not surprisingly, measurement companies were the first to see the value that an independent TV universe study could provide. Media companies, including major broadcasters and streamers, use DASH to add context and color to their ad sales presentations – and to track the measurement providers, whose ratings play a major role in valuing ad inventory. AdTech companies, including Experian, use DASH to create high-value audience segments for activation. The recent accreditation of DASH by the Media Rating Council (MRC) and adoption by Nielsen as an input to its TV ratings have generated interest from a broad range of companies. We are actively pursuing new licensees and partners to make DASH more useful within, and even outside, the TV ecosystem. What does MRC accreditation signify, and why is it meaningful for DASH? MRC accreditation means DASH passed a rigorous audit conducted by Ernst & Young over many months, which validated our methodology, controls, and data quality. MRC accreditation establishes that DASH is an industry-standard dataset. While the service provider normally announces its own accreditation, the MRC took the unusual step of issuing its own release on DASH, announcing the accreditation of DASH for TV universe estimation and endorsing the study for broader, cross-media use. How does Experian use DASH data to build audiences? The segments combine specific TV usage habits and behaviors from DASH with Experian data on demographics, spending, and other contextual inputs to create a fuller view of consumer viewing behavior. They are designed to be valuable to advertisers in many categories and planning contexts – and to be customizable to fit advertisers’ media targets. The segments can be used to: Apply or suppress audiences to improve target coverage across a campaign Better align media and creative Reach elusive but high-value viewers, such as Ad Avoiders Drive valuable consumer behavior Achieve specific advertising objectives What are some practical use cases for DASH-based audiences? Here are some practical use cases for four different kinds of DASH segments in five different advertiser categories. Travel Co-WatchersA couples-only resort uses TV Co-Watching Households without Children to strengthen target reach and ad memory recallA big theme park destination uses TV Co-Watching Households with Children to reach families in moments of togetherness Home Entertainment TV Owners and Brand LoyalistsA premium TV manufacturer uses the overlap of Multi Brand TV Owners and Single Brand TV Loyalist Households to market its newest TV model to its most loyal consumers. Fast Food Screen Size ViewersA fast food chain with a high-impact new brand campaign uses Large Screen TV Viewers to better align the media and creativeThat same fast food chain uses Small-Screen TV Viewers to drive store traffic by increasing exposure of its retail campaign among on-the-go viewers Financial Services Cord Cutters A personal cost management app and a cash-back credit card target Streaming-First Cord Cutter Households to reach young, tech-savvy, cost-conscious consumers Thanks for the interview. Where can readers learn more about DASH? We started work on DASH seven years ago, and it’s been fun to watch it “grow up.” Our partnership with Experian is a big step toward putting DASH to work for advertisers and agencies. To learn more, visit our site at https://theARF.org/DASH or contact us at DASH@theARF.org. Contact us About our experts Samantha Zhang, Senior Data Scientist at ARF Samantha Zhang is a Senior Data Scientist at the Advertising Research Foundation working on the DASH TV Universe Study, with additional research spanning areas including attention measurement, digital privacy, and artificial intelligence. Jim Meyer, General Manager, DASH, at ARF Jim Meyer is general manager and co-founder of the ARF DASH TV Universe Study and managing partner of Golden Square, LLC, which advises media and research technology companies on growth strategy and development. Latest posts
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