Beyond Black and White: The True Color of Brand Safety

Beyond Black and White: The True Color of Brand Safety

  23:02    Silver Push

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Over the past few years, a lot of brand safety issues have surfaced that have led marketers to review their brand safety measures. The current coronavirus crisis has intensified the brand safety woes of marketers, as most of the brands don't want ad adjacency to the content dealing with morbidity and mortality. 

Common brand safety methods used by marketers include blacklisting and whitelisting. Blacklisting involves avoiding placement of ads against content containing one or more blocked keywords. In case of video content, a blocked keyword is searched in topic, title, description and metadata.

Keyword-based blacklisting method is in reality not that effective as it seems to be. It is marred by under- and over-blocking of content. Research shows that because of the use of keyword blacklists, more than half of the safe stories published on the major news platforms are being incorrectly tagged as brand unsafe.

Keyword-based blacklisting method can lead to blocking of completely innocuous content. This is because it fails to comprehend the nuances in context, i.e. it is unable to understand the true context in which a keyword is used. For example, if "alcohol" is the blocked keyword, then the blacklisting method will not only tag a video featuring drunk and driving as unsafe, but will also tag a video featuring a recipe in which alcohol has been used as one of the ingredients, as unsafe.

Another problem with blacklisting is that universal blacklists cannot be created. They have to be regularly updated and modified according to the brands' requirements, current happenings and events, latest news, countries, languages and culture. There is also a requirement to tweak blacklists regularly on the basis of current safe content consumption patterns of consumers, so that increased reach for the advertising campaigns can be achieved. Overall, keyword-based blacklisting method is quite cumbersome to implement as it needs a lot of fine-tuning. With this method, content under- and over-blocking is a common problem, and this hinders marketers in getting optimal results from their advertising campaigns.

A whitelist enlists content that has been labeled as safe for ads to be placed against it. A whitelist provides a safe and trusted environment to brands to advertise within. Curating a whitelist for advertising on a video platform, for example for YouTube advertising, involves tagging unsafe content at the keyword, topic, video and channel levels. Video-level tagging helps brands to filter out unsafe videos from an otherwise safe channel; brands do not have to blacklist the entire channel just because of one or few unsafe videos.

Again, like keyword blacklists, whitelists need to be regularly updated, otherwise the campaigns will not witness an increase in reach, and brands will miss newer safe and engaging content for their ads; ads will keep displaying against the same video content enlisted in the static whitelist. 

Creation of whitelists is not an easy process; it requires a lot of curation by marketers, and is time-consuming and expensive. As the whitelisting method limits the number of videos against which ads can be placed, marketers are unable to take the full advantage of the true potential of huge video hosting platforms like YouTube. The campaign's reach gets reduced and the right audience does not get fully targeted.

The above-mentioned brand safety methods provide only suboptimal brand safety and have significant limitations. A highly effective way of ensuring brand suitability and safety is provided by contextual brand safety method that makes use of AI and computer vision. AI-powered brand safety platforms that deploy computer vision technology, provide high degree of context relevance unmatched by keyword-based methods.

Computer vision can accurately detect contexts in videos such as faces, objects, logos, on-screen text, emotions, scenes and activities. Thus, it can effectively detect unsafe or harmful contexts in videos without the risk of under- and over-blocking of content.

Amid the coronavirus pandemic, computer vision-powered brand safety platforms enable brands to selectively block ads against mortality-related coronavirus content, while allowing ad placement against positive coronavirus content. Thus, brands can safely capitalize on the news content; this is not possible with keyword-blacklists that fail to understand the true context in which the keyword "coronavirus" is being used.

By using AI-based contextual brand safety method, marketers can not only effectively block ad placement against recognized unsafe categories, but can also custom define unsuitable contexts that are unique to a brand. This helps them provide a fully suitable environment to brands for advertising.         

Computer vision enables marketers to go beyond blacklists and whitelists in order to achieve brand safety in its true color.    

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Using Computer Vision for Effective Visual Content Strategy

Using Computer Vision for Effective Visual Content Strategy

  02:46    Silver Push

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Visual formats such as images and videos are embraced by people over just a plain piece of text. Images and video enable brands to bring life to their messages, making consumers better understand their products and services. For brands, an effective and strong visual content strategy drives engagement and sales.

Research shows that brands are using visual formats much more on their own platforms and their social media pages for conveying messages to consumers, but less frequently in display ads.

But what is causing marketers to give less preference to display ads when it comes to using highly effective content formats - images and videos - for communication with the consumers? Research shows that using their own platforms allow them to exercise more control over their visual content in comparison to putting it out on the uncontrolled internet in the form of ads. There is enormous competition and it is hard for marketers to ensure that they are reaching their targets and drawing user engagement.

Another reason that marketers cite is of brand safety. Enormous amount of content is uploaded on the internet on daily basis and marketers have no idea against what content their ads would get displayed. On their own platforms, whole content is under their control.

Research shows that when it comes to using visual content for increasing user engagement, raising brand awareness and generating revenue, marketers face the following issues – insufficient viewability, contextual irrelevance, and ineffective demographic targeting. Data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), along with the gradual phasing-out of third-party cookies in Chrome by Google, have made practices like demographic targeting all the more difficult. 

The problems that hinder the use of visual formats by marketers in display advertising, namely – insufficient control over ad placement, insufficient user engagement, brand unsafe environment and data privacy laws – can be resolved through contextual targeting.

Contextual targeting involves placement of an ad against the content that is relevant to the ad, i.e. the ad is in line with the content that the user is currently interested in. Contextually targeted ads readily capture the attention of users and increase their chances of viewing or clicking them, as it is likely that users are already interested in the products or services being advertised.

Keywords-based contextual advertising often delivers sub-optimal results as keywords fail to fully reflect the user’s current state of mind, while AI-powered solutions that utilize technologies such as NLP and semantic analysis fail to understand nuanced contexts and complex relationships that exist between words.

The true contextual targeting can only be achieved through computer vision. By leveraging computer vision, marketers can take control of their visual content strategy and use visual formats to run highly effective video advertising campaigns, without worrying about data privacy and brand safety issues.

Computer vision is an advanced technology that enables computers to understand images and videos. Computer vision uses deep learning to make computers learn how to detect patterns in images and streaming videos.

Computer vision powered contextual advertising technology works by accurately detecting contexts in streaming videos in order to display in-video ads that are in line with what the user is actively engaging with. Any content that is unsafe or unsuitable is contextually filtered out to provide true brand suitability.

Computer vision enables marketers to embrace contextual targeting and fully utilize their visual content for achieving their marketing goals.

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Silverpush Launches AI-Powered Brand Suitability Platform – Mirrors Safe

Silverpush Launches AI-Powered Brand Suitability Platform – Mirrors Safe

  04:48    Silver Push

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Mirrors Safe uses computer vision to provide unequaled brand suitability for in-video ad placement.

Singapore, 20 May 2020: Silverpush has today announced the launch of its new AI-powered brand suitability platform – Mirrors Safe. Silverpush is well-acknowledged for its AI-powered contextual video advertising and real-time moment marketing products that enable brands to achieve unprecedented reach and user engagement.

Brand safety poses a serious risk to brands. Research shows that 80% of customers will not buy products at all or reduce their buying of products from brands that place ads across any type of harmful or offensive content. 70% of the customers hold the brand or agency for hurtful ad placement.

By using computer vision to detect contexts in video, Mirrors Safe overcomes the limitations of conventional brand safety methods such as keyword-based blacklists and whitelisted channels. It accurately detects contexts in videos such as faces, objects, logos, emotions, scenes and activities and filters out harmful content across a broad range of brand unsafe categories including terrorism, violence, nudity, hate speeches, smoking, etc.

Mirrors Safe makes use of an advanced algorithm for calculation of brand suitability score. This comprehensive score takes into account five parameters. This score measures safety and suitability of the content, page and channel. The five parameters are –

• Engagement: likes, dislikes & participation that the content generates
• Safety: exclusion through in-video context detection, on-screen text, and audio sentiment analysis
• Influence: organic influence that channel/page/content creates
• Relevance: how relevant is the content in terms of its peer channel/page category
• Momentum: consistency that channel/page maintains or grows in terms of engagement

Silverpush’s CRO, Kartik Mehta, said: “What sets Mirrors Safe apart is its ability to custom define the scope of harmful contexts, that are unique to every brand. Thus, helping brands move beyond just brand safety to a truly brand suitable environment. This is limited with existing keyword and natural language processing (NLP) based blanket exclusion technologies, as these often fail to understand the complex undertones and various contexts words can be used for”.

Silverpush used Mirrors Safe to analyze about 15 million videos across the largest video hosting and sharing platforms in the South East Asia region using Mirrors Safe. The analysis found 8% to 9% of the video content as brand unsafe, i.e roughly 1 in 10 videos has some type of brand damaging content.

Silverpush compared traditional brand safety measures with Mirrors Safe to identify nudity and adult contents in videos. Result was amazing as Mirrors Safe identified 300% more unsafe videos compared to conventional brand safety methods.

This finding brings into light the inefficacy of the traditional brand safety measures and the potential harm they can do to a brand’s image. The use of traditional measures has led to serious brand safety issues for some of the biggest video platforms.

“Mirrors Safe further addresses one of the most pressing brand safety challenges of content over-blocking – a result of blanket exclusion measures offered today. This significantly limits campaign performance and often forces marketers to switch off controls in favor of reach. Mirrors Safe’s in-video context detection technology prevents over-blocking and only filters videos that actually feature unsafe contexts, ensuring brand safety without hampering monetization and performance” – Mehta added.

Visit silverpush.co/mirrors-safe/ to know more about Mirrors Safe.

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  05:50    Silver Push

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Contextual Targeting Boosts Ad Relevance, User Engagement and Campaign Performance

Contextual targeting offers a smart and powerful way to advertisers for promoting brands and their products, while to publishers, it offers an effective way for monetizing their content. In the era of online privacy regulations and cookie-less advertising, industry experts predict it to become the mainstream advertising approach.     

Contextual targeting involves placement of those ads that are in line with the context of the content the user is engaging with. Whether an ad is placed against a video or textual content, if it is in context to what a user is watching or reading, he/she finds it relevant, less annoying, and more appealing to see it or click it.

Traditionally, contextual advertising is practiced by placing ads on the basis of keywords and topics. But this approach has its own shortcomings, for example, keywords cannot fully reflect the users’ current state of mind. Keyword-based contextual advertising often results in sub-optimal campaigns.

Using artificial intelligence for contextual targeting offers far better results in comparison to conventional keyword-based ad targeting. This is because AI advertising does not follow the keyword-based approach, but rather uses machine learning and computer vision to identify contexts in content, and then serve the ads that are contextually relevant. Thus, contextual targeting using artificial intelligence advertising technology boosts the relevance factor in ad targeting.

Video is becoming the most sought-after content on the internet. For marketers, video advertising is becoming one of the most effective ways to reach consumers. Computer vision powered in-video contextual advertising has emerged as the most effective and safe way to promote brands and their products by leveraging the popularity and reach of online video content

Computer vision powered in-video contextualadvertising works by identifying contexts in videos such as faces, emotions, objects, logos, scenes and activities. Relevant ads are served that are in line with the detected contexts, thus making the ads more engaging to the users of online video platforms such as YouTube.

Research has shown significantly positive impact of the contextual video advertising on the performance of ad campaigns in comparison to displaying ads irrespective of the context of the video content being watched.

Contextual in-video advertising offers significant benefits over non-contextual video advertising including the following -

·       When contextually relevant in-video ad is served, the ad receives higher attention from users in comparison to random ads. The chances of user interaction with the ad increases.  

          Contextual advertising enhances a brand’s outreach, awareness and perception. A brand’s image improves in terms of quality, value for money, and appeal. Users find the brand to be more reliable and authentic. 

·       By displaying contextually relevant in-video ads, which are more engaging and less annoying than random ads, the perception of the websites or online video platforms that host videos also improves in the minds of users.    

·       Brands are able to convert more people into customers, sales increase, and return on investment (ROI) gets boosted.      

Contextual advertising dramatically increases user engagement and reach for brands by serving consumers ads that are relevant to the content they are engaging with. Overall, contextual targeting is beneficial for all - brands, agencies, publishers, and consumers.   

   

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AI to Bridge the gap between TV and Digital Devices in Real Time

AI to Bridge the gap between TV and Digital Devices in Real Time

  01:58    Silver Push

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The scenarios have changed! Today when someone watches television, he or she is also glued to their smartphones that are constantly in their hands. Thus, creating some serious challenges for the marketers, who despite spending too much cost on TV ads are not being able to reach their potential customers in real time. Capturing the viewers' attention when it shifts from one screen to another in real time is the prime target for advertisers and marketers. This is where SilverPush steps in. Founded in 2012, SilverPush helps brands reach their target audience in real time by real-time TV tracking and synchronization from Tv to digital through contextual marketing.

SilverPush intends to expand in Southeast Asia to help the brands operating there, facing the problem of reaching their multi-screening clients effectively with digital contextual advertising on multiple screening platforms. Kartik Mehta, CRO of SilverPush, said in one of his interviews that Southeast Asia has become SilverPush’s topmost priority as it has become one of the top leagues of the world with one of the world’s highest internet and mobile penetration rates. A report released by ‘We are Social and Hootsuite, the internet penetration rate of Southeast Asia was 58% as compared to the world average of 55%.

In-video Contextual Ads

SilverPush has expanded from India to 12 different countries when it first entered this market in 2017 and began operations in Thailand. Some very big brand names like Unilever, Nestle, KFC, Coca-Cola, Samsung, Ford, and also, some regional brands have been associated as clients with SilverPush. For a very long time TV has been the dominant platform for the advertisers to reach out to their customers. But, now the gap between smartphones and the TV has been narrowed, making it a challenge for the advertisers to reach their potential clients in real time. The ‘TV Sync’ technology was pioneered by SilverPush in India, three years ago. SilverPush has also patented video fingerprinting and content recognition technology. SilverPush’s real aim is to further develop real time solutions and help brands create contextual video ads and normal ads, to raise audience engagement.

Contextual Marketing Platform

According to SilverPush, the real challenge for the brands today is where to put their resources like money, time and people. The immense amount of money spent on TV ads today, doesn’t seem as effective as it used to a few years back. Zenith, a multinational media specialist has predicted that according to them mobile advertising will overtake TV by 2021, with the current growth rate of 21% every year, and since smartphones have become the first point of online access for millions of users, this prediction could possibly be true. Whenever a commercial comes up on television, the viewers quickly shift their eyeballs from the TV to their mobile phones. This is where SilverPush uses the data along with the AI and connects the advertisement seen by the consumers on the TV to an engagement option on the viewers’ mobile phones at the right time, in the form of an interactive advertisement or a content that the viewers can share on social media. This is the ‘Moment Marketing Strategy’.

To provide insights about this technology, Mr. Mehta explained that AI detects ‘triggers’ on TV like when a team scores a goal, AI driven video context detection, will select pre-prepared social media messages with client representations like logos, etc to help the brands be there when the viewers interact with the triggers.

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