reach & frequency


The ARF Knowledge Center provides secondary research services for ARF and ARF-MSI members on a broad range of topics. Analysis of ARF member questions posed to the Knowledge Center in 2023 shows distinct trends and interests posed by different constituent members. As always, most questions tend to be specific to the unique business needs of each member, but there were some overarching trends, which highlight the evolving landscape of the marketing and advertising industry. This FAQ discusses these trends alongside sample member questions and Knowledge Center research reports.

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Linear vs. Streaming: Current State of Creative and Media

Nicole Lawless DesJardins, Ph.D.Sr. Director Data Science,

Leslie Wood, Ph.D.CRO,

Leslie Wood and Nicole Lawless DesJardins, both of, provided an overview of advertising trends in linear and streaming over the last two years in terms of ad creative, campaign and measurement. They defined linear as any content purchased on a national schedule and noted that variations exist across industries. Among their findings:
  • Streaming creatives outnumbered linear 2:1 during 2022 and 2023, and the share of streaming creatives increased in 2023.
  • In terms of creative rotation, linear creatives were on air five times longer during 2022 and 2023 than streaming creative. On linear, a smaller set of creatives will rotate on and off for over a year while on streaming, there are many creatives and each creative runs for two months.
  • The majority of impressions are on linear, but there has been a 24% year-over-year increase in streaming impressions. There are also variations depending on seasons and industries.
  • During 2022-2023, the vast majority of the campaigns in both years were linear only or linear first; however, streaming only, streaming first and mixed campaigns saw significant growth year-over-year.
Key takeaways:
  • Ad creative: Creatives ran for a shorter period of time on streaming compared to linear platforms during 2022-2023.
  • Campaign: Across industries, brands were increasingly leveraging streaming on top of traditional linear TV buys. Linear-only campaigns have declined and there have been increases in mixed and streaming-first campaigns, along with the emergence of streaming-only campaigns.
  • As much as 57% of a campaign’s audience is exposed to the campaign on both streaming and linear TV.
  • Measurement: Linear generated higher reach and frequency per campaign compared to streaming.
  • In 2023, linear’s reach surpassed streaming’s reach across industries, and average frequency is nearly 2.5x higher on linear.
  • The sweet spot of frequency on streaming is approximately four and is less variable than linear due to better targeting and frequency control within the streaming environment.

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OOH Measurement’s Game Has Changed

Christina RadiganSVP, Research & Insights, Outfront

Christina Radigan of Outfront explored the advantages of out-of-home advertising (OOH) and discussed advancements in its measurement techniques. Christina noted that with the loss of cookies and third-party data, contextual ad placement will see a renewed sense of importance, and in OOH, location is a proxy for context, driving content. She further indicated the benefits of OOH citing a recent study by Omnicom, using marketing mix modeling (MMM), which found that increased OOH spend drives revenue return on ad spend (RROAS). This research also highlighted that OOH is underfunded, representing only 4% to 5% of the total media marketplace. Following up on this, Christina pointed to attribution metrics, measuring the impact of OOH ad exposure on brand metrics and consumer behaviors, to demonstrate OOH's effectiveness at the campaign level. Expanding on their work in attribution, she noted changes stemming from the pandemic: Format proliferation and greater digitization, privacy-compliant mobile measurement ramping up (opt-in survey panel and SDK) and performance marketing and measurement becoming table stakes for budget allocations. New measurement opportunities from OOH intercepts included brand lift studies, footfall, website visitation, app download and app activity and tune in. Finally, she examined brand studies conducted for Nissan and Professional Bull Riders (PBR), showcasing the effectiveness of OOH advertising in driving recall, ticket sales and revenue. Key takeaways:
  • MMMs return to the forefront, as models become more campaign sensitive and are privacy compliant (powered by ML and AI).
  • A study from Omnicom, using MMM, found that optimizing OOH spend in automotive increased brand consideration (11%) and brand awareness (19%). In CPG food, optimizing OOH spend increased purchase intent (24%) and optimizing OOH spend in retail grocery increased awareness (9%).
  • OOH now represents a plethora of formats (e.g., roadside ads, rail and bus ads, digital and print) and has the ability to surround the consumer across their journey, providing the ability to measure up and down the funnel, in addition to fueling behavioral research.
  • Key factors for successful measurement in OOH: feasibility (e.g., scale and scope of the campaign, reach and frequency), the right KPIs (e.g., campaign goal) and creative best practices (Is the creative made for OOH?).
  • OOH advertising is yielding tangible outcomes by boosting consumer attention (+49%). Additionally, there has been a notable surge in advertiser engagement (+200%).
  • Ad recall rates in OOH continue to increase (e.g., 30% in 2020 vs. 44% in 2023).

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Tune-In to Discover What is Making Audiences Tune-Out

Travis FloodExecutive Director of Insights, Comcast Advertising

Duane Varan, Ph.D.CEO, MediaScience

Travis Flood (Comcast Advertising) and Duane Varan (MediaScience) presented research, which explored improving ad pod architecture, aimed at better engaging audiences by understanding what makes them tune-out. To provide framework to their research process, Travis indicated they started with a literature review, to understand the existing viewer experience. Focus was placed on the quantity, quality and relevance of the ads, in addition to media effectiveness studies (e.g., pod architecture, ad creative, getting the right viewers, etc.). Duane indicated that the literature review unveiled gaps, particularly in the examination of the content within the middle section of an ad pod. Based on this, the goal of the subsequent research was to understand the optimal duration of ad pods to optimize both the viewer experience and brand impact, difference in impact (e.g., more ads vs. fewer ads in the same break duration) and the impact of frequency on viewers and brands. The research included 840 participants who watched a 30-minute program with structured ad breaks. Feedback was measured using a post-exposure survey, neurometrics and facial coding. Results revealed that shorter pod length, grouping consistency in ad length and capping frequency at two to three ads per program as most effective. Key takeaways:
  • Optimal pod length: Two minutes or less leads to better results. After viewing 2 minutes of ads, recall begins to decrease. Recall is 2x higher at 2 minutes vs. 3 minutes, and after 3 minutes, recall is at its lowest point.
  • Viewers are more engaged as ads begin. Using facial coding data showed that for a heavy clutter cell, there was marginally less joy in the first 5 seconds of the ad, indicating that ad load impacts how viewers experience ads.
  • Facial coding data revealed that ad clutter can diminish how funny scenes are for viewers.
  • Consistency is key in ad lengths within a pod. Viewer testing showed that when ads had different lengths in a pod, it made the ad break feel longer compared to pods with ads of the same length.
  • Ad frequency was optimized at two per program. There was significant boost in ad recognition and purchase intent going from 1 to 2 exposures in a program. Capping frequency at 2-3 per program can positively impact recognition and purchase intent.

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AI Driven Video Formats Drive Results for Brands

Tori KangYouTube Specialist, Google

Danielle PerrellaHead of Measurement, Google

Tori Kang and Danielle Perrella from Google talked about AI from a media and video perspective with a summary of the overall landscape and an examination of how AI delivers on its promise in the ways it is working within Google’s YouTube. Tracing video’s effectiveness through the consumer funnel, Tori noted how the accelerating consumer complexity in viewing habits requires marketers to be more agile in navigating audience fragmentation, and AI’s capabilities are able to do the heavy lifting by saving time, optimizing efficiency and improving performance. Danielle illustrated how Google’s AI mechanism, Video Reach Campaigns, measured up against manually optimized campaigns and traditional YouTube formats in comparing ROAS and incremental sales. Key takeaways:
  • In improving reach and efficiency compared to manually optimized YouTube video campaigns, a Bubly case study showed using AI delivered 33% more reach at a 64% lower CPM.
  • Implementing AI earned an average ROAS 3.7x (+271%) higher than, and drove more than double the incremental sales (+111%) of, manually optimized YouTube video campaigns.
  • Identifying areas for optimization by finding inefficiencies, defining how AI can answer critical business questions and evaluating how AI can improve key metrics are the key questions for considering how AI should be integrated into a media plan.

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Forecasting & Optimizing Reach in a PII Compliant Measurement Ecosystem

Spencer LambertVP, Product & Partnership Success, datafuelX

Matthew WeinmanSr. Director, Advanced Advertising Product Management, TelevisaUnivision

Reach and frequency planning requires access to unique viewership data, which has become increasingly restricted due to identity restrictions. However, challenges exist with panel-only measurement, including the undercounting of Hispanic and Spanish language coverage, stated Matthew Weinman (TelevisaUnivision). Panel data undercounts Hispanics audiences by upwards of 20%, even for broad demographics. The benefits of big data exist across audience planning, viewership measurement and outcomes. Excessive frequency can be limited while maintaining or expanding reach, as well as improving ROAS. However, there are barriers to working with big data, including PII compliance. Additionally, the size and scale of big data leads to lengthy ID forecast times and computing costs. Spencer Lambert (datafuelX) presented details of their approach to ID-level forecasting which included their reach and frequency clustering methodology. Key takeaways:
  • Advantages of clustering methodology over identity methodology for reach and frequency:
  • Efficiency and accuracy: Delivers comparable accuracy metrics
  • Lower error rates: Seven percent for cluster reach forecasts vs. 20% error rate on identity-scaled reach forecasts
  • Cross-platform reach and frequency: By scaling cluster assignments to digital IDs, this methodology can empower cross-platform management and optimization
  • Lower compute time and costs
  • PII compliant: Preserves the use of identity-level planning

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The Power of AI for Effective Advertising in an ID-free World

Rachel GantzManaging Director, Proximic by Comscore

Amidst heightened regulations in the advertising ecosystem, Rachel Gantz of Proximic by Comscore delved into a discussion of diverse AI applications and implementation tactics, in an increasingly ID-free environment, to effectively reach audiences. Rachel highlighted signal loss as a "massive industry challenge," to provide a framework for the research she examined. She remarked that the digital advertising environment was built on ID-based audience targeting, but with the loss of this data and the increase in privacy regulations, advertisers have placed their focus on first-party and contextual targeting (which includes predictive modeling). In her discussion, she focused on the many impacts predictive AI is having on contextual targeting, in a world increasingly void of third-party data, providing results from a supporting experiment. The research aimed to understand how the performance of AI-powered ID-free audience targeting tactics compared to their ID-based counterparts. The experiment considered audience reach, cost efficiency (eCPM), in-target accuracy and inventory placement quality. Key takeaways:
  • Fifty to sixty percent of programmatic inventory has no IDs associated with it and that includes alternative IDs.
  • Specific to mobile advertising, many advertisers saw 80% of their IOS scale disappear overnight.
  • In an experiment, two groups were exposed to two simultaneous campaigns, focused on holiday shoppers. The first group (campaign A) was an ID-based audience, while the second group was an ID-free predictive audience.
    • Analyzing reach: ID-free targeting nearly doubled the advertisers’ reach, vs. the same audience, with ID-based tactics.
    • Results from cost efficiency (eCPM): ID-free AI-powered contextual audiences saw 32% lower eCPMs than ID-based counterparts.
    • In-target rate results: Significant accuracy was confirmed (84%) when validating if users reached with the ID-free audience matched the targeting criteria.
    • Inventory placement quality: ID-free audience ads appeared on higher quality inventory, compared to the same ID-based audience (ID-free 27% vs. ID-based 21%).

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How Attention Measurement Optimizes Marketing Campaigns for Success

Neala BrownSVP of Strategy and Insights, Teads

Laura ManningSVP of Measurement, Cint

This presentation focused on the intersection of attention and brand lift. The partnership between Teads and Cint relates to the challenge of scalability, access to data and insights, and collaboration and innovation. They used normative data sets in order to examine the performance. Beyond viewability, in partnership with Adelaide who uses AU—an omnichannel metric that predicts the probability of placement to capture attention and drive subsequent impact— they conducted 17 studies in 2023. There were variance in results, in statistical significance, outcomes, etc. Results: #1 case study—media that scored highly attentive showed higher product familiarity and favorability; #2 case study—for a flat and neutral campaign, higher attention drove higher brand lift across every brand funnel metric. In terms of applicability, from a media planning perspective, this learning can be leveraged toward outcomes. When aggregating across all 17 case studies, frequency matters—lower frequencies require more AU to move metrics. People who are already familiar react to lower AU media. For favorability—more “energy” or AU is needed to move people here, it’s easier to move people with high level of familiarity. For ad recall—even at higher exposure levels, the ad needs to be high quality and needs more attention. Notably, these case studies can be replicated. Key takeaways:
  • Frequency matters: lower frequencies require more AU to move metrics.
  • Familiarity reacts to lower AU media.
  • Favorability: it’s easier to move people with high level of familiarity.
  • For ad recall, the ad needs to be high quality.

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The Age of Acquisition Effect Has a Small Impact on Brand Recognition


Things learned early in life are recognized faster and more accurately, according to the age-of-acquisition effect, a phenomenon studied widely in psychology research. A new study recently published in the Journal of Advertising Research explores how this effect works in the context of brand names. It finds that brand exposure frequency and usage recency have a much greater effect on recognition than the age of acquisition itself.

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Insights on Advertising

The recent ARF OTT 2023 conference covered, among many other things, advertising opportunities in the new media environment. Here are two examples: first a summary of the variety of ad formats available now. Second, insights on the role of ad/product placements.

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