At A Glance
With Experian’s Digital Graph, a leading DSP resolved 84% of IDs and increased match rates across digital channels such as CTV and display. The result: stronger attribution, clearer ROI proof, and renewed client confidence.Demand-side platforms (DSPs) are more than just technology providers, they’re strategic partners, helping marketers answer the key question: “How should I spend my media budget?”
A leading DSP struggled to attribute consumer actions across digital channels such as connected TV (CTV) and display. Without connecting impressions to conversions, they risked losing client trust and ROI proof. With Experian’s Digital Graph, they resolved 84% of IDs and increased match rates, strengthening attribution and client confidence.
The challenge
A leading DSP had trouble showing which ads drove results across CTV, display, and digital. Without linking ad views to conversions, they couldn’t prove ROI.
The missing piece was attribution. They needed to show which channels drove conversions, but without strong identity resolution, it was hard to connect CTV ads to website activity.
What is Experian’s Digital Graph?
Built from trillions of real-time data points and updated weekly, Experian’s Digital Graph connects billions of identifiers across devices and households, such as cookies, mobile ad IDs (MAIDs), CTV IDs, IP addresses, universal IDs, and more. It gives DSPs a reliable foundation by linking these identifiers back to households and individuals, improving DSPs’ ability to offer attribution by better connecting impressions to conversions.
What makes the Digital Graph unique is its scale and freshness. It ingests trillions of signals in real time and delivers updates weekly. That consistency matters: it gives DSPs confidence that they’re working with the most accurate view of digital identity.
AI and machine learning (ML) are core to how we maintain that level of accuracy. Our models use sophisticated clustering algorithms to analyze device connections at both household and individual levels. By evaluating data points such as timestamps, IP addresses, user agents, cookie IDs, and device identifiers, these algorithms create precise device groupings that enhance targeting and measurement accuracy. The models are continuously refined, ensuring our clients can better understand consumer behaviors within households and activate more effective, personalized marketing.
Think of it like connecting puzzle pieces scattered across devices and channels. On their own, each piece doesn’t say much. Together, they reveal the full picture of who saw an ad, engaged, and converted, and which ads performed best.
The solution
By syncing its cookies with the Digital Graph, the DSP gained access to related identifiers, including:
- MAIDs
- CTV IDs
- IP addresses
- Experian cookies
This expanded identity universe gave the DSP a unified view of individuals and households, making it possible to connect impressions to conversions across devices and channels. With each weekly refresh, attribution models stayed accurate and up to date, turning fragmented signals into proof of performance.
Results
Within weeks, the DSP saw measurable improvements:
- 84% of IDs synced
- 9% increase in match rates
With a stronger foundation of digital identifiers, the DSP matched more MAIDs, CTV IDs, and IP addresses to conversions. This allowed them to show clients exactly which ads and channels drove ROI, transforming impression reports into actionable proof of performance and strengthening client trust.
Why attribution matters now
Attribution has never been more critical. With signals fading and marketing budgets under pressure, DSPs need reliable data to prove performance.
Experian’s Digital Graph takes a multi-ID, always-on approach, refreshed weekly with trillions of signals. This delivers consistency and accuracy that single-point, stale-ID solutions can’t match.
For this DSP, that meant transforming attribution from guesswork into clear proof, strengthening client trust, and proving ROI across channels.
Connect with us today to see how our Digital Graph can help you maximize advertiser trust and ROI.
Ready to strengthen your approach to attribution?
FAQs
Experian’s Digital Graph is a privacy-conscious identity resolution solution built from trillions of real-time data points, refreshed weekly, that links identifiers like cookies, MAIDs, CTV IDs, Unified I.D. 2.0 (UID2), ID5 IDs and IP addresses to households and individuals.
Experian’s Digital Graph improves attribution by connecting impressions to conversions across devices and channels, giving DSPs a clearer view of which ads and channels drove results.
While many platforms rely on single, static IDs, Experian’s Digital Graph uses a multi-ID, always-on approach with weekly refreshed, ensuring accuracy even as signals shift.
When you use Experian’s Digital Graph, you can expect higher match rates, more synced IDs, clearer attribution models, and stronger proof of ROI for your clients. Because Experian’s Digital Graph serves as the backbone of the industry, it also helps DSPs maximize the scale and reach they can deliver to advertisers.
Yes. Experian’s Digital Graph is designed with privacy in mind, ensuring compliance while still delivering accurate attribution insights.
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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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