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
by AdExchanger // Friday, March 15th, 2019 – 12:06 am “Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media. Today’s column is written by Preethy Vaidyanathan, chief product officer at Tapad, a part of Experian For years marketers asked, “How do we get more data?” Now that they’ve mastered data mining, marketers want to know what’s next. The time has come for organizations to make their abundance of digital data actionable, increase ROI and reach consumers with consistent, personalized experiences across all touchpoints. A seamless consumer experience can only be achieved by consolidating digital data. Organizations, however, are finding that consolidating data silos is more time-consuming and complicated than initially expected. The challenges One of the most pervasive obstacles companies face in when consolidating data is adopting inefficient and costly tactics that quickly become outdated. For instance, over the last couple of years, many companies turned to enterprise data warehouses to consolidate data silos, but some were too expensive or poorly suited for raw, unstructured and semi-structured data. This led companies to adopt data management initiatives, which bogged down many enterprises. Perception among senior level executives is another challenge. Many still question the need for digital transformation – achieving greater efficiencies through updating business and organizational processes with new technologies. Gartner found that more than half (54%) of senior executives say their digital business objective is transformational, while 46% say their objective is optimization. Digital transformation and data consolidation require time and effort. So, many large organizations work to overcome data silos as part of a multiyear digital transformation versus an immediate action item, delaying the benefits the company sees from taking on this project. All of these challenges make delaying progress in data consolidation easy, but companies should remember the impetus for doing so: creating a seamless customer experience that, in turn, drives business results. Brands with higher quality customer experience grow revenue faster than direct competitors with lower quality customer experience. The approach Many brands go into the digital transformation process assuming they have massive amounts of customer data, and that much of it is valuable or will be in the future. They might spend months aggregating that data in data stores or data lakes – at great expense. The trouble is that their data was scattered across multiple databases, which means it’s highly fragmented. As a result of this fragmentation, marketers can’t activate their data in ways that enhance the customer experience. To do so, companies must ensure their digital data is highly flexible so it can provide a holistic view of the consumer journey across every digital, in-store, in-venue and offline channel. I’d recommend that organizations taking on data centralization initiatives prioritize use cases that offer the company the greatest benefit. This is where organizations should establish a “crawl, walk, run” approach to data centralization to ensure key executives buy into the process. Starting with a subset of use cases, such as customer retention or upsell, or with a campaign, which is an even smaller starting point, allows executives to see the benefits of data consolidation projects relatively quickly. Once they validate these initial benefits, they can expand the range of use cases or campaigns, as well as the marketing ROI for their business. While data centralization is a long-term project that may take several years to complete, it doesn’t mean a business can’t get started now and see measurable results quickly. Break down data consolidation into stages so the organization can experience wins along the way. At the end of the day, data consolidation will help organizations deliver more effective marketing campaigns that drive business growth. Contact us today
Tapad’s technology enhances Bidtellect clients frequency capping and audience extension capabilities cross device. NEW YORK, Feb. 28, 2019 /PRNewswire/ — Tapad, part of Experian, is a global marketing technology company and leader in digital identity resolution solutions, today announced a new partnership with Bidtellect (now Simpli.fi), a leading native Demand-Side Platform (DSP). Bidtellect’s paid content distribution platform will leverage The Tapad Graph™ as its first cross-device partner. The integration will offer Bidtellect’s clients in the U.S. and Canada cross-device frequency capping and enhanced audience extension capabilities. The combination of Tapad’s leading cross-device technology, with Bidtellect’s unparalleled scale and optimization capabilities, will allow content marketers within brands and agencies to develop even more strategic, effective content marketing campaigns. The Tapad Graph™ will allow content marketers to gain greater reach and create more relevant, unified messaging with targeted delivery, when used in conjunction with Bidtellect’s technology. Marketers can expect to benefit from amplified reach, and enhanced, privacy-safe engagement with desired audiences as a result of this partnership. “Partnering with Tapad, the leaders in cross-device data, provides Bidtellect with a complete solution that leverages both probabilistic and deterministic mapping strategies,” said Mike Conway, Chief Technology Officer at Bidtellect. “The Tapad relationship expands our audience size by providing the opportunity to reach the same user across multiple devices and, when used in conjunction with our frequency capping functionality, ensures increased reach, reduced ad saturation, and elimination of wasted ad spend.” As the partnership progresses, Tapad will also work with Bidtellect to provide advanced attribution for conversions and engagement metrics including connectivity and amplification. These advanced insights will help brands and agencies develop a more holistic approach to content marketing, so they can build audiences and influence bidding algorithms that directly impact their business. Contact us today
The Tapad Graph Now Offered in Adobe Audience Manager, part of Adobe Analytics Cloud New York, NY — August 7, 2018 — Tapad, now part of Experian, is advancing personalization for the modern marketer, announced today that its proprietary Tapad Graph is now integrated with Adobe Audience Manager, part of Adobe Analytics Cloud, helping marketers expand their view of consumers and boost results through Tapad’s probabilistic solution. Tapad has been working closely with the Adobe Audience Manager team on this integration. With the Tapad Graph integration, customers based in the U.S. and Canada can use the Tapad Device Graph to expand the reach of audiences defined and activated in Adobe Audience Manager to extend first- and third-party data and deliver personalization across paid, earned and owned channels, publisher sites, programmatic, and more. Tapad worked closely with Adobe to develop the integration, allowing marketers to enable first-party data that has been previously tied to cookies and mobile. This offering has been beta-tested by leading organizations across retail, financial services, telecom providers, and more. Tapad has repeatedly proven its ability to provide marketers with a unified view of the customer across channels and screens. With the Tapad Graph, a global identity graph that currently supports more than 100 enterprise customers and 200 integration partners, marketers can extend their reach and customize messages based on user and household-level data. Contact us today