Month: April 2022

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Introducing Moderation Dashboard: a streamlined interface for content moderation

Hive
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Over the past few years, Hive’s cloud-based APIs for moderating image, video, text, and audio content have been adopted by hundreds of content platforms, from small communities to the world’s largest and most well-known platforms like Reddit.  

However, not every platform has the resources or interest in building their own software on top of Hive’s APIs to manage their internal moderation workflows.  And since the need for software like this is shared by many platforms, it made sense to build a robust, accessible solution to fill the gap.

Today, we’re announcing the Moderation Dashboard, a no-code interface for your Trust & Safety team to design and execute custom-built moderation workflows on top of Hive’s best-in-class AI models.  For the first time, platforms can access a full-stack, turnkey content moderation solution that’s deployable in hours and accessible via an all-in-one flexible seat-based subscription model.

We’ve spent the last month beta testing the Moderation Dashboard and have received overwhelmingly positive feedback.  Here are a few highlights:

  • “Super simple integration”: customizable actions define how the Moderation Dashboard communicates with your platform
  • “Effortless enforcement”: automating moderation rules in the Moderation Dashboard UI requires zero internal development effort
  • “Streamlined human reviews”: granular policy enforcement settings for borderline content significantly reduced need for human intervention
  • “Flexible” and “Scalable”: easy to add seat licenses as your content or team needs grow, with a stable monthly fee you can plan for

We’re excited by the Moderation Dashboard’s potential to bring industry-leading moderation to more platforms that need it, and look forward to continuing to improve it with updates and new features based on your feedback.

If you want to learn more, the post below highlights how our favorite features work.  You can also read additional technical documentation here.

Easily Connect Moderation Dashboard to Your Application

Moderation Dashboard connects seamlessly to your application’s APIs, allowing you to create custom enforcement actions that can be triggered on posts or users – either manually by a moderator or automatically if content matches your defined rules.

You can create actions within the Moderation Dashboard interface specifying callback URLs that tell the Dashboard API how to communicate with your platform.  When an action triggers, the Moderation Dashboard will ping your callback server with the required metadata so that you can successfully execute the action on the correct user or post within your platform.

Implement Custom Content Moderation Rules

At Hive, we understand that platforms have different content policies and community guidelines. Moderation Dashboard enables you to set up custom rules according to your particular content policies in order to automatically take action on problematic content using Hive model results. 

Moderation Dashboard currently supports access to both our visual moderation model and our text moderation model – you can configure which of over 50 model classes to use for moderation and at what level directly through the dashboard interface. You can easily define sets of classification conditions and specify which of your actions – such as removing a post or banning a user – to take in response, all from within the Moderation Dashboard UI. 

Once configured, Moderation Dashboard can communicate directly with your platform to implement the moderation policy laid out in your rule set. The Dashboard API will automatically trigger the enforcement actions you’ve specified on any submitted content that violates these rules.

Another feature unique to Moderation Dashboard: we keep track of (anonymized) user identifiers to give you insight into high-risk users. You can design rules that account for a user’s post history to take automatic action on problematic users. For example, platforms can identify and ban users with a certain number of flagged posts in a set time period, or with a certain proportion of flagged posts relative to clean content – all according to rules you set in the interface.

Intuitive Adjustment of Model Classification Thresholds

Moderation Dashboard allows you to configure model classification thresholds directly within the interface. You can easily set confidence score cutoffs (for visual) and severity score cutoffs (for text) that tells Hive how to classify content according to your sensitivity around precision and recall.

Streamline Human Review

Hive’s API solutions were generally designed with an eye towards automated content moderation. Historically, this has required our customers to expend some internal development effort to build tools that also allow for human review. Moderation Dashboard closes this loop by allowing custom rules that route certain content to a Review Feed accessible by your human moderation team.

One workflow we expect to see frequently: automating moderation of content that our models classify as clearly harmful, while sending posts with less confident model results to human review. By limiting human review to borderline content and edge cases, platforms can significantly reduce the burden on moderators while also protecting them from viewing the worst content.

Setting Human Review Thresholds

To do this, Moderation Dashboard administrators can set custom score ranges that trigger human review for both visual and text moderation. Content scoring in these ranges will be automatically diverted to the Review Feed for human confirmation. This way, you can focus review from your moderation team on trickier cases, while leaving content that is clearly allowable and clearly harmful to your automated rules. Here’s an example rule that sends text content scored as “controversial” (severity scores of 1 or 2) to the review feed but auto-moderates the most severe cases.

Review Feed Interface for Human Moderators

When your human review rules trigger, Moderation Dashboard will route the post to the Review Feed of one of your moderators, where they can quickly visualize the post and see Hive model predictions to inform a final decision.

For each post, your moderators can select from the moderation actions you’ve set up to implement your content policy. Moderation Dashboard will then ping your callback server with the required information to execute that action, enabling your moderators to take quick action directly within the interface.

Additionally, Moderation Dashboard makes it simple for your Trust & Safety team administrators to onboard and grant review access to additional moderators. Platforms can easily scale their content moderation capabilities to keep up with growth.

Access Clear Intel on Your Content and Users

Beyond individual posts, Moderation Dashboard includes a User Feed that allows your moderators to see detailed post histories of each user that has submitted unsafe content. 

Here, your moderators can access an overview of each user including their total number of posts and the proportion of those posts that triggered your moderation rules. The User Feed also shows each of that user’s posts along with corresponding moderation categories and any corresponding action taken. 

Similarly, Moderation Dashboard makes quality control easy with a Content Feed that displays all posts moderated automatically or through human review. The Content Feed allows you to see your moderation rules in action, including detailed metrics on how Hive models classified each post. From here, administrators supervise human moderation teams for simple QA or further refine thresholds for automated moderation rules.

Effortless Moderation of Spam and Promotions

In addition to model classifications, Moderation Dashboard will also filter incoming text for spam entities – including URLs and personal information such as emails and phone numbers. The Spam Manager interface will aggregate all posts containing the same spam text into a single action item that can be allowed or denied with one click.

With Spam Manager, administrators can also define custom whitelists and blacklists for specific domains and URLs and then set up rules to automatically moderate spam entities in these lists. Finally, Spam Manager provides detailed histories of users that post spam entities for quick identification of bots and promotional accounts, making it easy to keep your platform free of junk content. 

Final Thoughts: The Future of Content Moderation

We’re optimistic that Moderation Dashboard can help platforms of all sizes meet their obligations to keep online environments safe and inclusive. With Moderation Dashboard as a supplement to (or replacement for) internal moderation infrastructure, it’s never been easier for our customers to leverage our top-performing AI models to automate their content policies and increase efficiency of human review. 

Moderation Dashboard is an exciting shift in how we deliver our AI solutions, and this is just the beginning. We’ll be quickly adding additional features and functionality based on customer feedback, so please stay tuned for future announcements.

If you’d like to learn more about Moderation Dashboard or schedule a personal demo, please feel free to contact sales@thehive.ai. 

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How AI Unlocks Better Sponsorship Measurement

Hive
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Dan Calpin, President of Hive, spoke at the 2022 MIT Sloan Sports Analytics conference. Watch Dan’s presentation for insights on how Hive’s AI powers more scalable and comprehensive sponsorship measurement and branded content intelligence, enabling brands to more fully capture the value of their investments and rights holders to better price their assets.

Presentation: How AI-Powered Measurement Can Increase the Value of Your Sponsorships by 30% or More

For more sponsorship measurement insights, check out our Super Bowl LVI brand exposure insights and 2022 March Madness sponsorship analysis.

This analysis leverages Mensio, Hive’s media solution. Mensio uses AI to power faster and more granular sponsorship measurement and branded content intelligence across platforms.

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OCR Moderation with Hive: New Approaches to Online Content Moderation

Hive
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Recently, image-based content featuring embedded text – such as memes, captioned images and GIFs, and screenshots of text – have exploded in popularity across many social platforms. These types of content can present unique challenges for automated moderation tools. Not only does embedded text need to be detected and ordered accurately, it also must be analyzed with contextual awareness and attention to semantic nuance. 

Emojis have historically been another obstacle for automated moderation. Thanks to native support across many devices and platforms, these characters have evolved into a new online lexicon for accentuating or replacing text. Many emojis have also developed connotations that are well-understood by humans but not directly related to the image itself, which can make it difficult for automated solutions to identify harmful or inappropriate text content.

To help platforms tackle these challenges, Hive offers optical character recognition (OCR)-based moderation as part of our content moderation suite. Our OCR models are optimized for the types of digitally-generated content that commonly appears on social platforms, enabling robust AI moderation on content forms that are widespread yet overlooked by other solutions. Our OCR moderation API combines competitive text detection and transcription capabilities with our best-in-class text moderation model (including emoji support) into a single response, making it easy for platforms to take real-time enforcement actions across these popular content formats. 

OCR Model for Text Recognition

Effective OCR moderation starts with training for accurate text detection and extraction. Hive’s OCR model is trained on a large, proprietary set of examples that optimizes for how text commonly appears within user-generated digital content. Hive has the largest distributed workforce for data labeling in the world, and we leaned on this capability to provide tens of millions of human annotations on these examples to build our model’s understanding. 

We recently conducted a head-to-head comparison of our OCR model against top public cloud solutions using a custom evaluation dataset sourced from social platforms. We were particularly interested in test examples that featured digitally-generated text – such as memes and captioned images – to capture how content commonly appears on social platforms and selected evaluation data accordingly. 

In this evaluation, we looked at end-to-end text recognition, which includes both text detection and text transcription. Here, Hive’s OCR model outperformed or was competitive with other models on both exact transcription and transcription allowing character-level errors. At 90% recall, Hive’s OCR model achieved a precision of 98%, while public cloud models ranged from ~88% to 97%, implying a similar or lower end-to-end error rate.

OCR Moderation: Language Support

We recognize that many platforms’ moderation needs extend beyond English-speaking users. Hive’s OCR model supports text recognition and transcription for many widely spoken languages with comparable performance, many of which are also supported by our text moderation solutions. Here’s an overview of our current language support:

LanguageOCR Support?Text Moderation Support?
EnglishYesYes (Model)
SpanishYesYes (Model)
FrenchYesYes (Model)
GermanYesYes (Model)
MandarinYesYes (Pattern Match)
RussianYesYes (Pattern Match)
PortugueseYesYes (Model)
ArabicYesYes (Model)
KoreanYesYes (Pattern Match)
JapaneseYesYes (Pattern Match)
HindiYesYes (Model)
ItalianYesYes (Pattern Match)

Moderation of Detected Text

Hive’s OCR moderation solution goes beyond producing a transcript – we then apply our best-in-class text moderation model to understand the meaning of that speech in context (including any detected emojis). Our backend will automatically feed text detected in an image as an input to our text moderation model, making our model classifications on image-based text accessible with a single API call. Our text model is generally robust to misspellings and character substitutions, enabling high classification accuracies on text extracted via OCR even if errors occur in transcription. 

Hive’s text moderation model can classify extracted text across several sensitive or inappropriate categories, including sexuality, threats or descriptions of violence, bullying, and racism. 

Another critical use-case is moderating spam and doxxing: OCR moderation will quickly and accurately flag images containing emails, phone numbers, addresses and other personal identifiable information.  Finally, our text moderation model can also identify promotions such as soliciting services, asking for shares and follows, soliciting donations, or links to external sites. This gives platforms new tools to curate user experience and remove junk content. 

We understand that verbal communication is rarely black and white – context and linguistic nuance can have profound effects on how meaning and intent of words are perceived. To help navigate these gray areas, our text model responses supplement classifications with a score from benign (score = 0) to severe (score = 3), which can be used to adapt any necessary moderation actions to platforms’ individual needs and sensitivities. You can read more about our text models in previous blog posts or in our documentation.

Our currently supported moderation classes in each language are as follows:

LanguageClasses
EnglishSexual, Hate, Violence, Bullying
SpanishSexual, Hate
PortugueseSexual, Hate
FrenchSexual
GermanSexual
HindiSexual
ArabicSexual

Emoji Classification for Text Moderation

Emoji recognition is a unique feature of Hive’s OCR moderation model that opens up new possibilities for identifying harmful or harassing text-based content. Emojis can be particularly useful in moderation contexts because they can subtly (or not-so-subtly) alter how accompanying text is interpreted by the reader. Text that is otherwise innocuous can easily become inappropriate when accompanied by a particular emoji and vice-versa.

Hive OCR is able to detect and classify any emojis supported by Apple, Samsung, or Google devices. Our OCR model currently achieves a weighted accuracy of over 97% when classifying emojis. This enables our text moderation model to account for contextual meaning and connotations of emojis used in input text. 

To get a sense of our model’s understanding, let’s take a look at some examples of how use of emojis (or inclusion of text around emojis) changes our model predictions to align with human understanding. Each of these examples is from a real classification task submitted to our latest model release.

Here’s a basic example of how adding an emoji changes our model response from classifying as clean to classifying as sensitive.  Our models understand not only the verbal concept represented by the emoji, but what the emoji means semantically based on where it is located in the text. In this case, the bullying connotation of the “garbage” or “trash” emoji would be completely missed by an analysis of the text alone. 

Our model is similarly sensitive to changes in semantic meaning caused by substitutions of emojis for text.

In this case, our model catches the sexual connotation added by the eggplant emoji in place of the word “eggplant.” Again, the text alone without an emoji – “lemme see that !” – is completely clean.

In addition to understanding how emojis can alter the meaning of text, our model is also sensitive to how text can change implications of emojis themselves.

Here, adding the phrase “hey hotty” transforms an emoji usually used innocuously into a message with suggestive intent, and our model prediction changes accordingly.  

Finally, Hive’s OCR and text moderation models are trained to differentiate between each skin tone option for emojis in the “People” category and understand their implications in the context of accompanying text. We are currently exploring how the ability to differentiate between light and darker skin tones can enable new tools to identify hateful, racist, or exclusionary text content.

OCR Moderation: Final Thoughts

User preferences for online communication are constantly evolving in both medium and content, which can make it challenging for platforms to keep up with abusive users. Hive prides itself on identifying blindspots in existing moderation tools and developing robust AI solutions using high-quality training data tailored to these use-cases. We hope that this post has showcased what’s possible with our OCR moderation capabilities and given some insight into our future directions. 

Feel free to contact sales@thehive.ai if you are interested in adding OCR capabilities to your moderation suite, and please stay tuned as we announce new features and updates!


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New and Improved AI Models for Audio Moderation

Hive
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Live streaming, online voice chat, and teleconferencing have all exploded in popularity in recent years. A wider variety of appealing content, shifting user preferences, and unique pressures of the coronavirus pandemic have all been major drivers of this growth. Daily consumption of video and audio content has steadily increased year-over-year, with a recent survey indicating that a whopping 90% of young people watch video content daily across a variety of platforms. 

As the popularity of user-generated audio and video increases, so too does the difficulty of moderating this content efficiently and effectively. While images and text can usually be analyzed and acted on quickly by human moderators, audio/video content – whether live or pre-recorded – is lengthy and linear, requiring significantly more review time for human moderation teams. 

Platforms owe it to their users to provide a safe and inclusive online environment. Unfortunately, the difficulties of moderating audio and video – in addition to the sheer volume of content – have led to passive moderation approaches that rely on after-the-fact user reporting. 

At Hive, we offer access to robust AI audio moderation models to help platforms meet these challenges at scale. With Hive APIs, platforms can access nuanced model classifications of their audio content in near-real time, allowing them to automate enforcement actions or quickly pass flagged content to human moderators for review. By automating audio moderation, platforms can cast a wider net when analyzing their content and take action more quickly to protect their users. 

How Hive Can Help: Speech Moderation

We built our audio solutions to identify harmful or inappropriate speech with attention to context and linguistic subtleties. By natively combining real-time speech-to-text transcription with our best-in-class text moderation model, Hive’s audio moderation API makes our model classifications and a full transcript of any detected speech available with a single API call.  Our API can also analyze audio clips sampled from live content and produce results in 10 seconds or less, providing real-time content intelligence that lets platforms act quickly.

Speech Transcription

Effective speech moderation needs to start with effective speech transcription, and we’ve been working hard to improve our transcription performance. Our transcription model is trained on moderation-relevant domains such as video game streams, game lobbies, and argumentative conversations.

In a recent head-to-head comparison, Hive’s transcription model outperformed or was competitive with top public cloud providers on several publicly available datasets (the evaluation data for each set was withheld from training). 

Each evaluation dataset consisted of about 10 hours of recorded English speech with varying accents and audio quality. As shown, Hive’s transcription model achieved lower word error rates than top public cloud models. This measures the ratio of incorrect words, missed words, and inserted words to the total number of words in the reference, implying Hive’s accuracy was 10-20% higher than competing solutions. 

Audio Moderation

Hive’s audio moderation tools go beyond producing a transcript – we then apply our best-in-class text moderation model to understand the meaning of that speech in context. Here, Hive’s advantage starts with our data. We operate the largest distributed data-labeling workforce in the world, with over five million Hive annotators providing accurate and consensus-driven training labels on diverse example text sourced from relevant domains. For our text models, we leaned on this capability to produce a vast, proprietary training set with millions of examples annotated with human classifications. 

Our models classify speech across five main moderation categories: sexual content, bullying, hate speech, violence, and spam. With ample training data at our disposal, our models achieve high accuracy in identifying these types of sensitive speech, especially at the most severe level. Our hate speech model, for example, achieved a balanced accuracy of ~95% in identifying the most severe cases with a 3% false positive rate (based on a recent evaluation using our validation data). 

Thoughtfully-chosen and accurately labeled training data is only part of our solution here. We also designed our verbal models to provide multi-leveled classifications in each moderation category. Specifically, our model will return a severity score ranging from 0 to 3 (most severe) in each major moderation class based on its understanding of full sentences and phrases in context. This gives our customers more granular intelligence on their audio content and the ability to tailor moderation actions to community guidelines and user expectations. Alternatively,  borderline/controversial cases can be quickly routed to human moderators for review.  

In addition to model classifications, our model response object includes a punctuated transcript with confidence scores for each word to allow more insight into your content and enable quicker review by human moderators if desired. 

Language Support

We recognize that many platforms’ moderation needs extend beyond English-speaking users. At the time of writing, we support audio moderation for English, Spanish, Portuguese, French, German, Hindi, and Arabic. We train each model separately with an eye towards capturing subtleties that vary across cultures and regions. Our currently supported moderation classes in each language are as follows: 

We frequently update our models to add support for our moderation classes in each language, and are currently working to add more support for these and other widely spoken languages. 

Beyond Words: Sound Classification

Hive’s audio moderation model also offers the unique ability to detect and classify undesirable sounds. This opens up new insights into audio content that may not be captured by speech transcription alone. For example, our audio model can detect explicit or inappropriate noises, shouting, and repetitive or abrasive noises to enable new modalities for audio filtering and moderation. We hope that these sound classifications can help platforms identify toxic behaviors beyond bad speech and take action to improve user experience. 

Final Thoughts: Audio Moderation

Hive audio moderation makes it simple to access accurate, real-time intelligence on audio and video content and take informed moderation actions to enforce community guidelines. Our solution is nimble and scalable, helping platforms of all sizes grow with peace of mind. We believe our tools can have a significant impact in curbing toxic or abusive behavior online and lead to better experiences for users.

At Hive, we pride ourselves on continuous improvement. We are frequently optimizing and adding features to our models to increase their understanding and cover more use cases based on client input. We’d love to hear any feedback or suggestions you may have, and please stay tuned for updates!

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2022 March Madness: Sponsors Generate $410M+ in Media Value

5 brands - AT&T;, Capital One, Coca-Cola (including Powerade), Nike, and Spalding - collectively earned more than half of the total media value generated during the 2022 Men’s and Women’s tournaments

Hive
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At a glance:
  • 2022 March Madness generated more than $410M in equivalent media value for brands exposed within telecasts of the Men’s and Women’s tournaments (excluding commercials)
  • Exposure was concentrated within official NCAA sponsors, equipment providers, and the apparel brands outfitting participating teams, which collectively earned 93% of equivalent media value
  • Across the Men’s and Women’s tournaments, Capital One earned the most brand exposure among NCAA Corporate Champions, as did Buick among NCAA Corporate Partners, Nike among uniform sponsors, Spalding among equipment providers, and State Farm among all other brands
  • Among uniform sponsors, Nike outfitted more than half of all teams across the Men’s and Women’s tournament (69 of 136 participating teams) and earned the most cumulative exposure among uniform sponsors (61% in the Men’s tournament and 48% in the Women’s)
  • Official NCAA corporate partners – especially Coca-Cola – benefited from a surge in search engagement during tournament games, according to data from EDO which connects televised brand exposure from sponsorships and advertisements to online search activity

The 2022 NCAA Division I Men’s and Women’s Basketball Tournaments are now in the books after 134 games, millions of busted brackets, and two new national champions. This year, Hive and Elevate Sports Ventures teamed up to determine the final scores for the brands who invested alongside March Madness.

The following insights were generated using Hive’s AI-powered media intelligence platform, Mensio, which provides always-on measurement of in-content brand exposure for more than 7,000 brands across 24/7 programming from national TV channels and regional sports networks. Mensio is trusted by a diverse set of leading brands, rights holders, and agencies to measure the value of and share of voice from sponsorship activations, product placement, and other in-content exposures.

Official NCAA partners lead in-game brand exposure

Through 134 total tournament games – 67 from each of the Men’s and Women’s tournaments – brands earned more than 187 hours of cumulative brand exposure and more than $410 million in equivalent media value from their in-content exposure (excluding traditional commercials).

“Given the tradition and extraordinary momentum behind March Madness, and the mounting attention by brands on collegiate athletes and athletics, the university sports ecosystem is primed for mature brand exposure analysis of this nature,” said Kyle Folts, Vice President, Elevate Sports Ventures, Insights. “At Elevate Sports Ventures, we believe measuring exposure at scale empowers sponsors to efficiently and effectively make data-driven decisions that optimize their partnerships.”

With significant NCAA branding and deliberate assets for sponsorship placements, in-game brand exposure was concentrated within two tiers of official NCAA sponsors, equipment providers, and uniform sponsors (see Figure 1).

Official NCAA sponsors, equipment providers, and uniform sponsors collectively earned 93% of total time on screen and 94% of total equivalent media value in the Men’s tournament, and 78% of total time on screen and 66% of total equivalent media value in the Women’s tournament. The difference in mix between tournaments was driven by additional sponsorship assets available in the First and Second Rounds of the Women’s tournament; during those games, State Farm’s logo was placed on the stanchion arm along with a collection of other brands which were visible on the pole pads at the base of the stanchion and varied by arena.

Figure 1.
Figure 1.

AT&T, Capital One, Coca-Cola, Nike, and Spalding among March Madness exposure winners

The three NCAA Corporate Champion brands – AT&T, Capital One, and Coca-Cola (including Powerade) – along with top earners Nike and Spalding collectively earned 50% of the total televised screen time and 56% of the total equivalent media value earned by brands across the 2022 Men’s and Women’s tournaments.

Across the 2022 Men’s and Women’s tournaments, Capital One earned the most cumulative brand exposure among NCAA Corporate Champions. Buick was the most exposed brand among NCAA Corporate Partners, while Nike led the uniform sponsors, Spalding the equipment providers, and State Farm led all other brands (see Figure 2).

While in-stadium exposure is carefully scripted based on a brand’s contractual terms, the natural variability of gameplay and broadcast coverage can result in brands with similar assets earning different values from their placements.

“Most digital signage in arenas is allocated to brands for a fixed duration. However, brands get the most value from the subset of that exposure which is visible to the larger audience watching the event at home, which can often vary across brands based on gameplay,” said Dan Calpin, President of Hive – Enterprise AI. “The ability to measure this exposure in near real-time, especially during a season or multi-week event like March Madness, creates an opportunity to better align exposure with where brands get value.”

Figure 2.
Figure 2.

Nike leads exposure among apparel brands; Air Jordan outperforms in both tournaments

While most NCAA sponsors enjoy exclusivity among their competitive set during the tournament, apparel brands are unique in that uniform sponsorships are contracted with teams – resulting in a competition for exposure among Nike, Under Armour, Adidas, and Nike’s Air Jordan brand.

More than half of the teams in the tournament (69 of 136) wore Nike uniforms; however, the exposure earned by apparel brands are a combination of both how many of their teams make the tournament, and how many games those teams ultimately play.

In the Men’s tournament, Under Armour outfitted the second-most teams (14; behind Nike’s 35) but saw only one of those teams advance to the Sweet Sixteen and none beyond that. Meanwhile, Air Jordan only outfitted six Men’s teams but strong performance resulted in the brand representing 25% of the field in the Sweet Sixteen, Elite Eight, and Final Four, as well as one half of the National Championship game (see Figure 3).

Figure 3.
Figure 3.

In the Women’s tournament, the distribution of games was identical to the distribution of teams. Nike outfitted 50% of the teams, and those teams played in 50% of all games (see Figure 4).

image

Official NCAA Sponsors earned higher search engagement during telecasts

NCAA sponsors engaged viewers beyond traditional ads and in-game signage. According to data from data, measurement, and analytics software company EDO, top sponsors experienced a higher than average Search Engagement Rate (SER) in the minutes adjacent to their on-screen exposure. SER is EDO’s proprietary metric based on the increase in online search activity for a brand or product in the minutes immediately following a televised exposure (controlling for impressions, duration, and other factors).

SER for the three NCAA Corporate Champions – AT&T, Capital One, and Coca-Cola – was 1.69x that of the Primetime Broadcast and Cable average, meaning the trio generated 69% more search engagement than average (see Figure 5). Across the board, March Madness advertisers experienced 26% more search than the average Primetime Broadcast and Cable program. Of particular note is Coca-Cola, generating 126% more online search than average, likely due to their “Coca-Cola® with Coffee” launch campaign.

“NCAA programming typically performs exceptionally well in EDO data,” said Laura Grover, Head of Client Solutions at EDO. “In 2021, for example, NCAA March Madness programming comprised four of the top ten most engaging sports programs across all of TV. Further, the Men’s Championship Game was the second strongest sports environment for driving ad engagement in 2021, trailing only Super Bowl LV.” Grover continued, “This year’s games provided a similarly engaging environment, and Coca-Cola experienced considerable success pairing the NCAA environment with the launch of Coca-Cola with Coffee. That campaign has proven to be Coca-Cola’s strongest of the past year.”

Figure 4.
Figure 4.

About Hive

Hive is the leading provider of cloud-based AI solutions for content understanding, which are trusted by hundreds of the world’s largest and most innovative organizations. The company empowers developers with a portfolio of best-in-class, pre-trained AI models, serving billions of customer API requests every month. Hive also offers turnkey software powered by proprietary AI models and datasets, enabling industry-leading applications for critical business needs. Collectively, Hive’s solutions are transforming legacy approaches to content moderation, brand protection, sponsorship measurement, context-based ad targeting, and more. For more information, visit thehive.ai or follow on LinkedIn.

About Elevate Sports Ventures

Elevate Sports Ventures is a best-in-class sports and entertainment consulting firm, providing proven, innovative solutions to organizations across the global sports and entertainment landscape. Elevate taps into the extensive resources, relationships, and expertise of its partners to innovate and execute comprehensive strategies and solutions in Venue Renovations, Sales and Marketing, Stadium Licenses, Premium Ticketing, Corporate Hospitality, Customer Research, Strategy and Analytics, Sales Training, and more. Formed in partnership between the San Francisco 49ers and Harris Blitzer Sports & Entertainment (HBSE) in 2018, Elevate welcomed Oak View Group (OVG), Ticketmaster and Live Nation as partners in June, 2018. For more information, visit: www.ElevateSportsVentures.com or follow @ElevateSV on Twitter or LinkedIn.