Key takeaways
- First-party data records what happened inside one customer relationship. It can’t show the life stage, household makeup or financial pressure behind a purchase.
- Context earns its place in a customer record when it’s accurate, governed, and tied to a decision like segmentation, modeling or personalization.
- Governed context improves segmentation, prospecting, retention outreach and personalization by adding life stage, financial pressure and household signals.
- Experian maintains more than 5,000 consumer attributes, averaging roughly 250 per person, built with privacy and governance controls and grounded in durable identity.
A brand can have five years of purchase history, a loyalty program with millions of records, and a fully built-out customer data platform, and still not know why a customer walked away last quarter. First-party data has become one of the clearest ways brands can differentiate, which is exactly why it’s worth asking what more of it can and can’t do.
First-party data describes a relationship, not the whole person
A customer’s purchase history, site visits, app activity, and loyalty behavior are accurate records of what happened inside that one relationship. They don’t show who that person is beyond the transaction, the life stage, household makeup, or financial pressure that actually shapes a decision. Every brand runs into this limit eventually, no matter how much first-party data it has collected.
More sources isn’t the same question as better sources
The common response is to add more data sources. Third-party data, contextual signals, geographic data, each layered on to fill in what first-party data can’t see alone. Combining sources like this can expand what a brand can see.
It doesn’t automatically mean every layer earns its place in the customer record. Volume and quality are different questions, and only one of them is guaranteed by adding another source. A pile of attributes from unverified providers can sit alongside a brand’s best first-party and third-party data without ever being governed or relevant enough to act on with confidence.
Relevant, governed context is the differentiator, not another data source
Adding a new data type isn’t the same as adding context that’s actually relevant to the decision in front of you. A longer list of attributes doesn’t make a customer record more useful on its own, whatever source it came from. Context earns its place in that record when it’s accurate, tied to a real decision, like segmentation, modeling, or personalization, and usable within the governance standards a brand already holds itself to.
That’s a higher bar than volume. It’s also the one that actually changes outcomes. Predictive models and AI-driven decisioning only raise that bar. The more decisions get automated, the more they depend on inputs that are relevant, not just abundant.
What governed context actually changes
When a customer record includes real-world context, demographic, lifestyle, behavioral, and household signals that are built and governed responsibly, the decisions drawn from it change in specific ways.
- Segmentation reflects who people actually are, not just how they’ve transacted so far.
- Prospecting can find people who resemble a brand’s best customers in ways that matter, not just in surface-level behavior.
- Retention improves when outreach reflects a fuller picture of the person, not just their last transaction.
- Planning and personalization can account for the realities, like life stage or financial pressure, that shape whether a message lands.
That context is only as useful as the identity foundation underneath it. Context grounded in durable identity moves with a brand across the platforms, partners, and activation paths it already uses, rather than sitting locked inside one system.
At Experian, we maintain more than 5,000 consumer attributes, averaging roughly 250 per person, built and maintained with privacy and governance controls in mind. That depth creates the most value when it extends a brand’s own first-party foundation, grounded in identity and usable across the activation paths a brand already relies on, not locked into a single platform or vendor.
Context, not volume, is the differentiator
Owning first-party data was the starting point. It was never the finish line, and adding more sources on top of it isn’t the finish line either unless each one clears the same bar. The brands that get the most value out of their customer data in the next few years will know exactly what their data still can’t tell them, and will go find that missing context in a way they can trust and govern.
That’s the work I’d focus on, making the first-party data a brand already has tell a fuller, more accurate story about the people behind it, rather than acquiring more data for its own sake. If you want to talk through what that could look like for your first-party data, connect with our team.
About the author
Kevin Dunn
Chief Revenue Officer, Experian
Kevin Dunn joins Experian Marketing Services with more than 20 years of leadership experience across marketing and advertising technology, most recently serving as Senior Vice President of Brands and Agencies at LiveRamp. In that role, he led growth across retail, CPG, travel, hospitality, financial services, and healthcare, overseeing new business, account expansion, and channel partnerships.
Kevin is known for building cohesive, accountable teams and leading with optimism, clarity, and a strong sense of shared purpose. His leadership philosophy centers on empowering people, driving positive outcomes for clients and fostering a culture where teams can grow, take smart risks, and succeed together.
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In our Ask the Expert Series, we interview leaders from our partner organizations who are helping to lead their brands to new heights in ad tech. Today’s interview is with Jordan Feivelson, VP, Digital Audiences at Webbula. Jordan is a 22-year advertising industry veteran who has worked for media properties such as WebMD and Disney. Over the past ten years, he has transitioned to the data and programmatic space, including growing the data business for Kantar Shopcom and Adstra. What types of advertisers might benefit from utilizing Webbula audiences across various verticals? Can you provide examples of how different industries successfully leverage your data to achieve specific campaign goals? Most advertisers can leverage Webbula’s award-winning attributes for their activation initiatives. Webbula offers approximately 3,000 syndicated segments covering categories such as Demographics, Automotive, Political, Mortgage, B2B, Hobby/Interest/Lifestyle, and Interests & Brand Preferences (brand name targeting). Audience insights and marketing strategies What specific types of audience segments does Webbula provide? How can advertisers leverage these segments to craft more effective, personalized marketing strategies? Webbula has incredible depth and breadth within its verticals, giving marketers the tools to deliver targeted messaging effectively. Our Demographic, B2B, Mortgage, Automotive, and Interest and Brand Preferences segments each contain 500-1,000 segments, all built on deterministic, self-reported, and individually linked data. We ensure the best accuracy with multiple deterministic data points tied to the real world (ex., first name, last name, postal address, and email address). Some examples of our unique syndicated audience types: B2B: A view of the latest industry trends with detailed cuts of the professional world, such as companies with and not within the Fortune 500 companies and job positions that are directors and below. This also includes custom capabilities, including ABM (list of target companies in an activation campaign or by industry code (ex. NAICS, SIC). Interest and Brand Preferences: Consumers who have shown interest and affinity to hundreds of brands (ex., Nike), genres (ex., comedy, hip hop), sports teams, and more. Mortgage: A detailed view of homebuyers’ purchase range, loan type (ex. jumbo loan, standard loan), mortgage amount, interest rate, and more. With Webbula’s audience data, brands can create a comprehensive picture of their audiences down to the individual level and reach them accurately. Data quality, sourcing, and differentiation How is consumer data sourced and curated at Webbula? Are there data quality standards that Webbula establishes for consumer data, and how do you ensure your sources and methods meet these standards consistently? Webbula’s data is aggregated from over 110 trusted and authenticated sources, including publishers, data partners, social media, and more. The data collected comes directly from consumers who self-report information through surveys and other methods. We apply our hygiene filters to mitigate fraud and accurately score the data. Data Collection: The data collected comes directly from consumers who self-report information through surveys, questionnaires, transactions, and sign-ups. This ensures that brands display ads to audiences based on self-identified, cross-channel behaviors, not modeled assumptions. Hygiene Solutions: Webbula applies multi-method hygiene solutions to mitigate fraud and accurately score the data before onboarding, ensuring that all data meets the highest quality standards. Examples of Data Sources: Questionnaires: Self-reported data through surveys, offer submissions, and telemarketing. Transactions: Deterministic data from aftermarket parts, online purchases or services, and more. Sign-ups: Individually linked data from information entered through sweepstakes, infomercials, newsletters, and forms. What differentiates Webbula’s data from other data providers in the market? Can you explain the unique value proposition that Webbula offers in terms of data depth and breadth? Due to our extensive experience in data cleansing, we provide the most accurate data within the programmatic ecosystem. TruthSet, the leading programmatic accuracy measurement company, has ranked Webbula as having the highest number of top attributes compared to other data providers with 150M+ HEMs. Additionally, Publicis Groupe and Neutronian further validate Webbula’s data quality, underscoring its position as a leader in the industry. Webbula’s data stands out in the market due to its unmatched accuracy and quality, achieved through years of expertise in data cleansing. Unlike other providers, Webbula’s foundation lies in its robust email hygiene process, ensuring that all data entering the programmatic ecosystem is thoroughly cleansed. Privacy, compliance, and future-proofing What measures does Webbula take to maintain data privacy and compliance? How do these efforts benefit advertisers in an evolving regulatory landscape and ensure ethical standards? Webbula was created over a decade ago with a future-proof, privacy-compliant foundation. We understand the industry’s rapid changes, including government and state legislation and cookie depreciation. Our goal has always been to build long-term partnerships and ensure we are prepared for industry changes. We rely on validated offline data sources, making us resilient to external influences. Success stories Can you share success stories where advertisers saw significant campaign improvements using Webbula’s data? What were the key factors that contributed to these successes? Our success is measured by client feedback and increased client spend. Webbula has helped several key advertisers achieve six-figure monthly thresholds by providing the most accurate data to meet campaign KPIs. Clients consistently return to use our data, validating our belief that “the proof is in the pudding.” Thanks for the interview. Any recommendations for our readers if they want to learn more? For those interested in learning more about Webbula, reach out for a personalized consultation. Contact us Latest posts
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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