Originally appeared in MarketingProfs
We all understand the importance of data quality. Metrics like third-party validations, match rates, and accuracy scores help us assess data quality on its own terms. Yet too often, organizations struggle to connect high-quality data with real-world business outcomes. How does data accuracy directly impact the ability to reach target audiences and campaign performance?
Scale and cost: The tradeoffs of accuracy
Marketers are frequently incentivized to prioritize broad reach, even at the expense of precision. This often leads to decisions driven by short-term gains—reaching more people at a lower cost. The temptation is deceptively straightforward, but deep down we know overly simplistic approaches are likely to fall short.
Cheaper data solutions, even if they seem to provide greater reach, mask a deeper issue. The data may not be accurate. In fact, the initial savings from cheaper data typically result in higher long-term costs due to inefficiencies and waste that are hard to track. Unless you’re carefully evaluating your campaign results, it can be difficult to see where the inefficiencies are creeping in.
The hidden cost of inaccurate data
Programmatic platforms make it easy for mistargeted impressions to slip through unnoticed. Common issues include:
Stale data, where consumer behaviors and locations have changed but the data hasn’t.
Inactive signals, where you think a digital identifier like a device ID is addressable, but the device hasn’t been used in months.
Disparate or duplicative data, where you think you’re reaching three people but in fact it’s just one person who you’re frequency bombing. Nobody likes getting the same ad over and over again. It’s like if your co-worker messaged you “hey” five times in a row.
Direct mail waste is tangible: towering stacks of returned mail serve as undeniable reminders of inefficiency, not to mention the financial costs of wasted postage. Digital campaigns, by contrast, often obscure their inefficiencies within complex programmatic platforms or impression reports. It’s like watching a gust of wind scatter piles of paper into the ether—it’s hard to track and quantify.
As a result of these data inaccuracies, brands mistakenly assume they’re optimizing their budgets when, in fact, they’re hemorrhaging money and reaching the wrong people with a message they don’t care about. It’s a marketer’s nightmare scenario.
The perceived savings from cheaper, less accurate data turn out to be an illusion.
The compounding effect of inaccurate data
Consider a situation where an inaccurate insight or signal prompts a brand to adjust its targeting toward an underperforming segment. Each new campaign uses this flawed data to guide its optimizations, amplifying the waste. What starts as a minor inefficiency quickly becomes a significant budget drain, funneling resources into segments that aren’t delivering.
If you bake a cake but use salt instead of sugar–each new ingredient only makes the final product more unpalatable. With ad targeting, the feedback loop created by optimization tools exacerbates this issue. Decisions are made based on misleading metrics, perpetuating flawed strategies and causing brands to over-invest in underperforming tactics. Without scrutiny, brands risk building entire strategies on fundamentally flawed insights.
The value of investing in the highest quality data
With accurate data, brands can zero in on the right audience. This is particularly critical in lookalike modeling. By enriching customer files, brands can understand the nuances of who their best customers are—and how to find more of them. Tailored messaging, based on a consumer’s actual behaviors and interests, deepens engagement. Conversion rates rise as campaigns meet customer needs.
Accurate data also provides insights that aren’t immediately obvious. Sometimes, seemingly minor behaviors or unexpected demographic segments can emerge as key drivers of conversions. It’s like finding the one avocado at the grocery store that’s perfectly ripe…you’re well on your way to delicious guacamole.
To truly grasp the impact of data accuracy, traditional validation metrics such as third-party assessments (e.g. Truthset) should be paired with other performance indicators that show you how well data reflects actual consumer behavior. With this complete picture in view, the choice is obvious: quality data is worth the premium.
Acting on what the data tells you
Collecting accurate data is just the first step—the real challenge is having the ability to act on what it reveals. Many brands enter campaigns with preconceived notions about their target audience, only to find the data tells a different story. Ignoring these insights stifles growth. The value of data-driven marketing lies in trusting the insights and adapting strategies accordingly.
How to test if your current approach is working
We understand that changing data providers can feel daunting, but there are low-lift ways to explore whether your current approach is truly delivering. Test the waters by selecting an Experian Audience on a major platform or building a custom audience to see how your campaigns perform. Alternatively, collaborate with Experian’s insights team to gain a deeper understanding of your audience and determine if it aligns with your current strategy. It’s a small step that could lead to a big impact.
Get in touch with our team today
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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