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.
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Study reveals that brands with more mature identity programs were significantly more likely to be successful in achieving their key objectives Tapad, a part of Experian, a global leader in cross-device digital identity resolution and a part of Experian, has commissioned Forrester Consulting, part of a leading research and advisory firm, to conduct a new study that evaluates the current state of customer data-driven marketing and explores how marketers can use identity solutions to deliver privacy safe and engaging experiences, in an evolving data landscape. The study highlights the changing ground rules for digital marketing and the threat that poses to marketers’ ability to deliver against long standing KPIs and campaign goals. Nearly two-thirds (62%) of respondents said that the forces of data deprecation will have a significant (40%) or critical (21%) impact on their marketing strategies over the next two years. Among those surveyed, identity resolution strategies have surfaced as an opportunity to create more powerful customer experiences, with 66% aiming to have it help improve customer trust and implement more ethical data collection and use practices, while nearly 60% believe it will point the way to more effective personalization and data management practices. Although organizations are eager to implement identity resolution strategies, a complex web of solutions and partners makes execution a challenge. For example, respondents report using at least eight identity solutions on average, across nearly six vendor partners, and they expect that fragmentation to persist in the ‘cookieless’ future. Additionally, brands’ identity resolution technologies typically represent a patchwork of homegrown and commercial solutions. Eighty-one percent of respondents use both in-house and commercial identity resolution tools today, and 47% use a near-equal blend of the two. Despite the challenges, many brands have the foundation for a strong identity resolution strategy in place, and they are thriving as a result. Specifically, more mature brands were 79% more successful at improving privacy safeguards to reduce regulatory and compliance risk, 247% more successful at improving marketing ROI, and over four times more effective at improving customer trust compared to their low-maturity peers. Additional insights include: Marketers Are Increasingly Playing a Key Strategic Role Within the Organization, But There is a Mandate to Demonstrate Value. Nearly three-quarters of respondents in our study agree the marketing function is more strategically important to their organization than it used to be, while almost two-thirds agree there’s more pressure than ever to prove the ROI or business performance of their activities. 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Marketers are always challenged to expand sales beyond “business as usual,” while being good stewards of company resources spent on marketing. Every additional dollar spent on marketing is expected to yield incremental earnings—or else that dollar is better spent elsewhere. You must be able to determine return on advertising spend (ROAS) for any campaign or platform you add to your marketing mix. A key driver of positive ROAS is incremental customer actions produced by ad exposure. Confident, accurate measurement of incremental actions is the goal of an effective testing program. Why do we test campaign performance? Because demonstrating incremental actions from a campaign is a victory. You can keep winning by doing more of the same. Not finding sufficient incremental actions is an opportunity to reallocate resources and consider new tactics. Uncertainty whether the campaign produced incremental actions is frustrating. Ending a profitable marketing program because incremental actions were not effectively measured is tragic. Test for success When you apply rigorous methods to test the performance of campaigns, you can learn to make incremental improvements in campaign performance. The design of a marketing test requires the following: Customer Action to be measured during the test. This action indicates a recognizable step on the path to purchase: awareness, evaluation, inquiry, comparison of offers or products, or a purchase. Treatment, i.e., exposure to a brand’s ad during a campaign. Prediction regarding the relationship between action and treatment (e.g., Ad exposure produces an increase in purchase likelihood). Experimental design is the structure you will create within your marketing campaign to carry out the test. Review of results and insights. Selecting a customer action to measure Make sure that the customer action you measure in your test is: Meaningful to the campaign’s goal. What is the primary goal of the campaign? Is it brand awareness? Web site visits? Inquiries? Completed sales? An engagement by the customer. Your measurement should capture meaningful, deliberate interaction of consumers with the brand. Attributable to advertising. There should be a reasonable expectation that ad exposure should increase, or perhaps influence the nature of customer actions. Abundant in the data. Customer action should be a) plentiful and b) have a high probability of being recorded during the ad campaign (in other words, a high match rate between actions and the audience members). Selecting campaign treatments It is best for treatments to be as specific as possible. Ad exposures should be comparable with respect to: brand and offer, messaging, call to action, and format. Making a prediction This is the “hypothesis.” Generally, you assume that exposure to advertising will influence customer actions. To do this, you need to reject the conclusion that exposure does NOT affect actions (the “null hypothesis”). Elements of an experimental design An attribution method that links each audience to their purchase action during the test. This consists of a unique identifier of the prospect which can be recognized both in records of the audience and records of the measured action during the measurement period. A target audience that receives ad exposure. A control audience that does not receive ad exposure. It provides a crucial baseline measurement of action against which the target audience is compared. Time boundaries for measurement, related to the treatment: Pre-campaign Campaign Post-campaign Randomly selected audiences (recommended) Some audience platforms, such as direct mail and addressable television operators, feature the ability to select distinct audience members in advance. Randomly selected audiences can generally be assumed to be similar in all respects except ad exposure. The lift of the action rate is simple to calculate: Campaign Lift = (Action Rate (target) / Action Rate (control)) -1 Non-randomly selected audiences are more difficult, but still possible, to measure effectively. There may be inherent biases between them that may or may not be obvious. To measure campaign performance, we must first account for any pre-existing differences in customer actions, and then adjust for these when measuring the effect of ad exposure. Typically, the pre-campaign period (and possibly the post-campaign period as well) are used to obtain a baseline comparison of actions between the two audiences. This is a “difference of differences” measurement: Baseline lift = (Action Rate (target) / Action Rate (control)) -1 Campaign Lift = (Action Rate (target) / Action Rate (control)) -1 Net campaign lift (advertising effect) = Campaign Lift – Baseline Lift Analyzing results and insights How large is the lift? This is generally expressed as a percentage increase in action rate for the target audience vs. the control audience. Are we confident that the lift is real, and not just random noise in the data? This question is answered with the “confidence level.”. 95% confidence means the probability of a “true positive” result is 95%; and the probability of a “false positive” due to random error is 5%. What was the campaign cost per incremental action? If you also know the expected revenue from each incremental action, you can project out incremental revenue, from which you can calculate return on ad spend. Other insights: Do the results make directional sense (we would hope that ad exposure will cause an increase in customer actions, not a decrease)? Does action rate generally increase with the number of ad exposures? Summary Well-designed testing and measurement practices allow you to learn from individual advertising campaigns to improve decision-making. 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It’s almost that time of the year again, the time to put away fourth of July merchandise and replace it with this year's favorite superhero backpacks. It’s almost back-to-school season, and parents and kids from kindergarten to college are preparing for school's "new normal." To navigate the challenge of 2021, Experian’s Marketing Analytics team is sharing Back-to-School shopping season insights with you. Download the eBook to learn more. Our outlook about this year's Back-to-School shopping season can help you better plan and improve your marketing effectiveness. The report covers who's actively shopping for school supplies, whether they're shopping in-person or online, and what they're buying this year. Here's a summary of what you'll learn in the report: Who (specifically) is shopping for back-to-school supplies this year? More than half of online searches related to Back-to-School were made by a small set of consumer segments. We’ve identified 4 Mosaic® groups as being in-market for back-to-school merchandise. To find these types of consumers, we used online behavioral data and filtered for households with school-age children between 5 and 15 years old. Each group, such as Flourishing Families, share similar shopping behaviors and needs. While each group of consumers has a need for Back-to-School merchandise, they have different circumstances that require more personalized marketing. Let's break down each Mosaic® group to better understand their size and key features so that you can build more personalized messaging. Contact us for segments and insights specific to your brand. Power Elite As you can see in our Mosaic® product brochure, Power Elite is categorized as Group A. This is the largest group analyzed in the report, accounting for 4.5 million U.S. households. Here are the Power Elite consumer types actively shopping for back-to-school merchandise this year: A01: American Royalty A03: Kids and Cabernet A04: Picture Perfect Families Key Features: Wealthy Highly Educated Politically conservative Purchase housewares and electronics in store Vacation and fitness retail influencers Luxury lease cars Flourishing Families Also called Group B in this report, Flourishing Families is comprised of 3.7 million U.S. households. Active consumer types: B07: Across the Ages B08: Babies and Bliss B09: Family Fun-tastic Key Features: Affluent Charitable contributors Athletic activities High-priced children’s clothing Home products & furnishings Sporting good Suburban Style Suburban Style, also Group D, is made up of 2.9 million U.S. households. Active consumer types: D15: Sport Utility Families D16: Settled in Suburbia Key Features: Comfortable lifestyle Ethnically diverse Politically diverse Instagrammers Children’s games Wholesale members Family Union The Family Union group, Group I, is the smallest of those analyzed in this report, but still a respectable size: 1.2 million U.S. households. Active consumer types: I31: Hard Working Values Key Features: Bilingual Married with kids Large households Hunting clothing Automotive tools Will they shop online or in stores? Prepare for a return to in-store shopping as the US moves post-pandemic. These consumers have shopped in-store for Back-to-School and have trended toward in-store shopping as the vaccine was distributed. Mobile location data shows these consumers actively shopped in-person during the 2019 Back-to-School season, and are shopping in-person again post-pandemic. Experian analyzed consumer mobile location data for big box retailers, department stores, malls and apparel-accessory stores since June 2019. The aggregated number of visits was indexed each month against 12-month average of that respective year. An index higher than 100 indicates shopping behavior that month was higher than the average of that year. An index less than 100 indicates shopping behavior that month was less than the average of that year. Planning store layouts and inventory will be more important this year for marketers as consumers return to the stores for Back-to-School shopping needs. What will they buy? Plan for Back-to-School product composition to be like pre-pandemic while you plan your inventory. Keep an eye on local outbreak risk which dictates whether school districts will pivot to remote learning. Product composition during the 2020 Back-to-School season was skewed away from apparel and towards virtual learning materials, such as home office supplies and technology, but should revert to pre-pandemic behaviors. Using ConsumerViewTM Transactional data, we compared consumer product composition during the 2019 and 2020 back-to-school shopping seasons. Children’s Apparel and Accessories: share was smaller in 2020, and was a more dramatic impact for Groups A, B, and D. Books: Groups B and D saw an increased share in 2020, but Groups A and I saw little change. Home Office: share was greater in 2020 for all groups, particularly Group A. Computers: share was greater in 2020 for all segments, particularly Group I Want to learn more? Improve your marketing ROI and grow your business during back-to-school season using Experian’s new Discovery Platform. No sign-up required: watch the demo to learn how retailers like you can use The Discovery Platform™ to track online versus in-store shopping and safely navigate evolving back-to-school consumer behaviors.