Which Is Better for Your Business – Manual or AI Visual Content Moderation?

Which Is Better for Your Business – Manual or AI Visual Content Moderation?

  02:55    Silver Push

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Visual content moderation is important for businesses or brands, especially if they have to deal with a lot of user-generated visual content. Any association with inappropriate content can damage their reputation, weaken consumer trust and result in decrease in sales.

Traditionally, visual content classification and moderation has been done manually. But with the advent of AI, automated content moderation platforms have emerged. These platforms make use of computer vision and provide an effective way for image and video classification and moderation.       

Whether a brand or business should moderate visual content manually, use AI-powered automated content moderation or augment manual moderation with an automated one, depends on a number of factors. These factors are discussed here below –

Source of content

In order to build brand recognition and consumer trust, more and more brands are now allowing user-generated content on their own platforms. However, user-generated content is potentially risky and can include inappropriate matter that can be highly damaging for the brands. Although brands can dictate their content posting guidelines to users, they do not have actual control over what a user is posting. Moderating such content is a must for brands. As there are high chances of user-generated visual content being inappropriate or unsuitable, brands should opt for computer vision-powered video and image classification and moderation platform.

If in case, most of a brand’s visual content is not user-generated, but is sourced internally or from highly trust-worthy third parties, then for such a brand, video and image moderation can be performed manually by hiring human content moderators and there is a lesser need for an automated system.

Volume of content

For brands that have to deal with a good volume of visual content, especially user-generated content, manual moderation does not work effectively and efficiently. They should make use of computer vision-powered image and video moderation platforms.

AI-powered systems can tackle enormous content volume with a high degree of accuracy. Computer vision technology effectively classifies and tags visual content at scale. Such automated systems are not plagued by human errors, can work continuously unlike human beings, and their algorithms get self-trained from the data they handle.

Nature of content 

An automated AI content moderation platform can effectively filter out content such as “not safe for work” images and videos, and other forms of inappropriate, offensive or dangerous content, but it falls short when it comes to filtering out misinformation. Here, human intervention from human content moderators is required.

User-generated visual content can be highly mentally disturbing for human content moderators. Filtering out such content through automated computer vision powered content classification and moderation platform is the best way to prevent ill effects on mental health.  

Hiring a large number of human moderators is quite expensive and may not be feasible for businesses with small budgets. Also, in most of the cases, as discussed above, manual moderation is less effective than computer vision powered visual content moderation.

For brands or businesses that have to handle a large amount of user-generated visual content, computer vision-based content moderation is much better than manual moderation in terms of accuracy, effectiveness and efficiency.

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Our New research shows that 90% marketers consider brand safety a serious problem.

Our New research shows that 90% marketers consider brand safety a serious problem.

  02:38    Silver Push

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Brand marketers and Agency Heads across Southeast Asia believe ad placements across harmful content damage brand perception and result in revenue loss.

The brand safety crisis, that first caught the attention of advertisers in a major way back in 2017, is even more real today. With millions of pieces of user generated visual content added to video sharing platforms daily, brand safety has taken centre stage in the advertising world.

Like every crisis, this has also resulted in practical and workable solutions that have provided a semblance of control to advertisers in varying degrees. However, some of the most widely used brand safety measures including blocklists, whitelisted channels/pages, third-party measurement and brand safety specialists, bring along their own set of efficiencies and pitfalls. A debate that gained more weight recently as Coronavirus topped keyword blocklists, squeezing ad revenues and killing brand reach.

In In an attempt to understand how leading marketers and brands perceive and mitigate brand safety risks, we surveyed 160+ agency heads, business leads in media and brand marketers in Southeast Asia.

This survey report highlights some of the brands’ biggest challenges with available brand safety measures and a pulse on the growing importance of and readiness for brand suitability. Key highlights from the report include:

·       Video platforms offer more brand safety controls, but continue to remain brand unsafe, with Tik Tok leading followed by Facebook and YouTube. This was further solidified with another research when earlier this year Silverpush analysed ~15 million videos across video sharing and hosting platforms in SEA, and found nearly 8–9% of all content as brand unsafe: featuring violence, smoking, adult, and extremist content. Which means that 1 in every 10 video ad placements can potentially be across harmful and damaging content.

•         ~90% industry professionals believe unsafe exposure impacts brand perception negatively, and 62% believe the extent of this damage is highly negative.
•      ~60% respondents believe brand safety risks can result into revenue loss ranging from reduced buying to complete boycott of the brand
•       Blocklists and whitelists remain top brand safety measures. NLP based technologies and in-video context detection are emerging.
•       However, 60% said that using current brand safety measures result in inability to reach specific audience
•      ~63% industry professionals stated lack of customized exclusion filters that can meet unique brand needs as the most pressing brand safety challenge, highlighting the importance of brand suitability.

The report further talks about how challenges of the current brand safety measures resulted in killing reach and monetization during COVID-19. And further highlights the growing importance of brand suitability, solutions brand and agencies seek, and the emergence of AI powered context detection technology.

Access the full report here.

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Contextual Targeting Enables Marketers to Deal with Unpredictable Consumer Behavior

Contextual Targeting Enables Marketers to Deal with Unpredictable Consumer Behavior

  03:17    Silver Push

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Amid the coronavirus pandemic, marketers are witnessing a dramatic shift in the behavior of the consumers. Consumers are not behaving in the way that marketers have expected them to do. Their behavior has become unpredictable, inconsistent and erratic.

For example, consumers have stockpiled grocery items in their pantries during the lockdown period to the extent that demand has surpassed supply. Consumers have either made a large number of visits to grocery stores or frequently procured essential items from e-commerce websites. 

Marketers have observed that consumers are showing less loyalty to brands, as they are filling up their pantries by buying products they require from any brand. They just want to make sure that they have enough goods to meet their requirements for a long time.  

This new behavior pattern exhibited by consumers has a good amount of deviation from the normal consumer behavior that the marketers are accustomed to. Before the coronavirus pandemic, marketers could easily predict consumers’ behavior and show them ads on the basis of the behavioral data tracked and collected by them.

This form of advertising, known as behavioral advertising, makes use of third-party cookies and collects user data such as websites visited, webpages viewed, time spent on website/web pages, visit frequency, clicked links, products viewed, purchase history, etc. This data helps marketers to create rich profile of consumers. But in the difficult times such as the coronavirus pandemic, when consumers do not show consistent behavior, the third-party cookies and consumer profiles created by marketers fail to predict what a consumer will be interested in buying next.

Marketers also rely on geo-targeting for serving ads to users. Geo-targeting refers to the practice of delivering ads to consumers on the basis of their geographical locations. It is often used by marketers for advertising to local prospects and help local businesses that depend on foot traffic such as restaurants and brick-and-mortar stores to increase sales. But during the coronavirus crisis, geo-targeting is also not delivering success to marketers as people are refraining from going out of their homes except for essential items.

Thus, consumers’ behavioral and geographical data, which is considered highly valuable by marketers under normal circumstances, loses its importance during the times of a crisis, as its use fails marketers in achieving targeted results.  

In this scenario, it is the contextual targeting that acts as the savior for marketers. Contextual targeting has emerged as a very effective way of advertising. Contextual targeting involves placement of ads on the basis of the content the user is actively engaging with and has nothing to do with users’ past behavior, purchasing habits, and their locations. It makes use of the context of the digital content that a user is consuming rather than his or her data profile.

Traditional contextual targeting based on keywords and topics has produced results less than optimal as it involves contextual fails. For example, if an ad of a burger appears against the content talking about the harmful effects of fast food, then it will severely harm the image of the burger brand.

But this is not the case now; the advent of new technology has changed contextual targeting radically. AI-powered contextual advertising that makes use of computer vision has emerged as the smartest and the most effective way of using context for targeting audience. Through computer vision, in-video contexts such as faces, emotions, objects, logos, actions and scenes are detected with high accuracy, enabling marketers to serve ads on the basis of what the user is interested in at the moment. With computer vision powered contextual advertising, the chances of user clicking or viewing the ads are very bright. 

During the coronavirus pandemic, not only the consumers’ behavior has become erratic, they are also overwhelmed with the coronavirus content. They do not want to see brand messages appearing next to mortality-related coronavirus content. For marketers, it is a challenge to distinguish between safe and unsafe coronavirus content. Computer vision powered contextual advertising not only provides highly context relevant ad placement, but also accurately filters out harmful, unsafe or unsuitable content such as mortality-related coronavirus news. Apart from the recognized unsafe categories, marketers can custom define unsuitable contexts unique to each brand. Thus, computer vision-based targeting ensures true brand suitability.   

In crisis such as coronavirus pandemic, when consumers’ past behavior data becomes useless for marketers, computer vision-powered contextual targeting serves as the most effective way to serve ads in a truly brand suitable environment.   

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Achieving Intelligent Content Moderation Through Computer Vision

Achieving Intelligent Content Moderation Through Computer Vision

  04:30    Silver Push

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The quantity and diversity of content on the internet are increasing dramatically. Loads of content in the form of videos, images and text is daily added to the internet by the users around the world.

Online platforms such as social media sites, e-commerce sites, dating and matrimonial sites, online communities, chat rooms, forums, etc. make maximal use of the user-generated content.

For brands, user-generated content is an important tool for building recognition and trust, as content about brands generated by consumers is deemed highly trustworthy by other consumers. For brands, user-generated content can come in the form of social media posts, blog comments, posts on forums, feedback, testimonials, etc. User-generated content serve as a form of brand promotion by consumers themselves. Majority of consumers take user-generated content into account when judging the quality of a brand and while making a purchasing decision.

Although the user-generated content is the fuel that powers the internet, it carries an inherent risk of being inappropriate, objectionable, harmful, dangerous, or offensive. Such a content can hurt the sentiments of people, cause people to form negative opinions, promote terrorism, damage reputation of brands, and can have other dire consequences.

An effective solution to this problem is provided by content moderation. Content moderation involves scrutinizing and filtering the user-generated content according to the guidelines adopted by a web platform.

Although manual content moderation works fine when the volume of content to be moderated is small, the process becomes highly cumbersome when large volume of content loaded on a daily basis is to be handled. Moreover, manual text, image or video moderation is plagued by human errors.

Automated AI-powered content moderation platforms can easily handle very large volume of content with high accuracy. These automated platforms make use of computer vision for content classification at scale, significantly reducing human intervention. Computer vision detects faces, emotions, objects, on-screen texts, logos, activities, and scenes in images and videos with high accuracy.

Computer vision powered content moderation platforms involve building of image classification models for image moderation and video classification models for video moderation.

For image classification, several category-specific models are built. The classification component of each category model gives a binary prediction, i.e. it predicts whether an image should be classified as belonging to that category or not.

A category-specific model is trained on a very large number of images. Training, validation and test datasets with positive and negative examples for that category are deployed. For the purpose of training, labeled images are obtained from the following sources – collection of internally labeled images, search engine results for relevant queries with filter set to show only images labeled for reuse and modification, and synthesized images.

For testing these category-specific models, both offline and online cross-validation are performed. Offline testing is performed against a cleanly labeled and reliable dataset.

For evaluation of the performance of the classification models, the following metrics are considered - classification accuracy, precision, recall rate, false discovery rate, and false rejection rate. The recall rate denotes the accuracy in detecting images that should be flagged while the false rejection rate gives the percentage of the images that have been falsely rejected.

To make the final decision on an image, an ensemble model based on the aggregated predictions of different category-specific models is used. An image gets the final approval only after it has been approved by all of the category-specific models.

Based on the customers’ requirements, AI-powered content moderation solutions make use of custom-trained and tailor-made content classification models. This allows the use of customer-specific categories and tags.

Using computer vision technology, effective content moderation can be achieved at scale with minimal human intervention, while avoiding under- and over-moderation of content. 

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  03:34    Silver Push

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How Computer Vision is Transforming Marketing?

Computer vision is a powerful and revolutionary technology. Its use cases have emerged in various industries including retail, automotive, healthcare, defense, agriculture, banking and marketing.

Computer vision provides exciting capabilities to marketers. Computer vision enables computers to acquire visual understanding just like human beings use their eyes and brains to understand their surroundings. Computer vision works by scanning images and videos, and translating their contents into metadata. This data can be then organized and used by the marketers in varied ways.

Computer vision is transforming marketing in several ways. The following are some of the ways through which computer vision is reshaping the marketing landscape and providing awesome opportunities to marketers –

Content Generation Through Generative Adversarial Networks (GANs)

Creation of new content is one of the greatest challenges faced by online marketers. Generative Adversarial Network (GAN) is an approach to generative modelling, which implies that these networks can be used to generate or create new visual content.

A Generative Adversarial Network is trained using a generator model and discriminator model. The generator model learns to generate new examples, whereas the discriminator model learns to differentiate examples as either real or fake.

GANs can be used by marketers to create visual content such as videos, images and three-dimensional models. For example, GANs can be used to generate an image of a person in different poses. This is done by providing data on different poses to the system. GANs have also been used to generate realistic photos of fake fashion models. These examples show how GAN can help marketers easily create original visual content through computer vision.

Some of the other uses of GANs include generation of realistic photographs of human faces, creation of cartoon characters, image-to-image translation such as translation of photos of day to night or of summer to winter, translation of text to image, etc. Marketers can use GANs to create visual content at massive scale so as to fully meet their marketing requirements.

Product Search by Using Visual Similarity

Computer vision allows consumers to search a product without typing keywords or product-tags in the search bar. A buyer uploads an image of a product. The visual search technology shows him/her the same and similar products on the screen.

This computer vision powered visual search technology makes product finding easy and fast for consumers, and improves their shopping experience. This technology facilitates online shopping and leads to increase in sales.

Contextual Advertising

Computer vision-powered contextual advertising delivers very high degree of context relevance. Computer vision enables detection of contexts in video such as faces, emotions, logos, objects, scenes and activities with very high accuracy, allowing marketers to display in-video ads that are in line with the content the user is actively engaging with.

Computer vision powered contextual advertising is a boon for marketers as it allows them to achieve unprecedented reach and user engagement; not achievable by traditional methods of contextual advertising.

Understanding Images Through Screen Graphs

By using scene graphs, it is possible to retrieve images by describing their contents. For example, by using this technology, one can enter “a man wearing a black hat and sitting in a white car” in the search bar and all the relevant images will appear in the search results. Current image retrieval systems that do not deploy scene graphs cannot handle such complex queries.

Scene graphs represent objects, attributes of objects and relationships between the objects in images. The scene graph-based technology is a new field of applied computer vision. For marketers, this technology provides benefits such as contextual image generation and intelligent auto-captioning. Scene graphs enables marketers to make data-driven decisions around visual content.

Brand Safety

Computer vision-powered brand safety solutions enables marketers to effectively avoid ad placements against brand unsafe content. These solutions work by detecting harmful or unsuitable contexts in streaming videos. Computer vision offers a tailored approach to brands by enabling them to custom define unsuitable contexts, thus allowing them to run their ads in a truly brand suitable environment. Computer vision also overcomes the problems of content under- and over-blocking inherent in the traditional brand safety methods.

By integrating computer vision into their marketing efforts, marketers can easily generate visual content at scale, gain deep insights, enhance customer experiences, facilitate consumers’ buying, run highly successful campaigns, and ensure true brand suitability.

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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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Using Computer Vision for Effective Visual Content Strategy

Using Computer Vision for Effective Visual Content Strategy

  04:04    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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Sunlight x Mirrors – Video script

Sunlight x Mirrors – Video script

  04:00    Silver Push

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Energetic start

Unilever recently launched Sunlight, a leading brand of laundry soap sold globally, in Egypt. With the promise of a refreshing scent that lasts up to 7 days.

However, Sunlight was a late entrant in the market, where laundry soap is a highly mature and competitive category. Resulting in intense competition from pre-established local brands, that were heavily advertising on traditional media.

Sunlight found Digital media to be a highly relevant yet underutilized channel for advertising laundry soaps in Egypt.

With nearly 35% of Sunlight’s target audience present on social platforms, Sunlight chose YouTube to create brand awareness within the modern Egyptian woman, seeking more than just clean clothes from her soap. 

Slight pause, energetic start

The central idea was to not only reach the most relevant audience, but also reach at a moment they were most likely to engage.

However, advertising campaigns on YouTube are deployed only basis past searches and keyword mapping, resulting in random ad placements, and ad waste.

Hence, Sunlight turned to a contextual advertising strategy, which could go beyond demographics and affinities, to effectively reach the target audience creating higher engagement.

Slight pause, energetic start

This is where MIRRORS by SilverPush brings in the magic touch of artificial intelligence. 

MIRRORS, the world’s first in-video context detection platform, uses deep learning to identify scenes, logos, faces, objects and actions in a streaming video, to serve ads only against contextually relevant videos.

Mirrors targeted videos featuring celebrities (pause), competing brands (pause), allied products (pause), and actions.

Sunlight precisely aligned brand communication with this content, enhancing customer experience and engagement in the moment.

Mirrors further optimized the campaign for most responsive audience buckets.

Slight pause, energetic start

The campaign showed unprecedented results, with overall 2.3M views, 52% higher view rates and 28% higher click through rates compared to generic targeting.

Real-time optimization for audience buckets created significant results for contexts like celebrity faces, mud and dirt, competition, and complementary products.

Beyond outreach, the campaign helped Sunlight gain 2% market share in a highly saturated market in its first month.

Ad Recall Increased by 31.5% & Brand Awareness by 10.3%. Overall Sunlight over achieved the targeted business results by 21%, marking a huge success.

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Impact of Prevailing Notions and Culture on Brand Safety

Impact of Prevailing Notions and Culture on Brand Safety

  05:15    Silver Push

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Brand safety is a relative concept that changes with time and depends on the prevailing notions and culture. Today, in the digital times, changes in people’s thinking and cultural norms occur at a much faster rate, and therefore, brand safety is much more frequently affected.

Brands today advertise against different types of websites such as news sites and video hosting platforms. The content on these sites reflect the current notions. This poses a serious challenge to brands, as brands do not know against which content their ads may appear. For example, an ad of a car having a huge diesel engine may appear on a news website or YouTube against a story or video talking about the negative effects of climate change on the environment. A decade ago, this might not have posed any problem for the car brand, but in today’s time, climate change is regarded as a grave issue and consumers will form a negative impression of the brand.  

For brands, #metoo movement surfaced as a shift in the cultural norms that cast a huge impact on their advertising strategy. Brands refrained themselves from placing their ads next to the movement related content. Cultural brand safety norms vary from region to region. For example, brands in the United States are susceptible to opposing political views, while those in the Europe are vulnerable to fascist content. Thus, brands are required to devise their brand safety strategy according to the region in which they want to promote their products.

Research shows that consumers think that it is the responsibility of brands and their agencies to ensure their ads do not appear against harmful or inappropriate content. Brands should devise a proactive and elastic brand safety strategy that can keep them protected from changes in cultural norms that are bound to keep occurring.

Brands should continuously pay attention to real-world cultural, social and political events along with evolving behavior of consumers to keep themselves abreast of negative content and trends. This will save them from getting entangled in a brand safety crisis. Brands should pay attention to both local and global happenings in order to run successful cross-border campaigns.

A robust brand safety strategy would be that which can effectively deal with changing cultural norms, changing notions of people, and differences that exist across country lines. Brands should use a brand safety solution that works in real-time, provides a very high degree of context relevance while detecting harmful or inappropriate contexts, and offers custom controls.

Blanket exclusion methods based on keyword blacklists have limitations such as content under- and over-blocking, and lack the flexibility to offer custom controls to brands. AI-powered solutions have emerged that focus on providing high context relevance, but those based on machine learning, natural language processing, and semantic analysis are unable to truly understand the sub-text, nuanced contexts, and complex relationships between words.

True context relevance is only offered by brand safety solutions that make use of computer vision. Various contexts in online videos such as such as faces, objects, logos, on-screen text, emotions, scenes , and activities can be accurately detected by leveraging computer vision technology. By using a computer vision powered brand safety solution, brands can effectively get rid of harmful or unsuitable content, and ensure a true brand-suitable environment for achieving their video advertising goals.

To effectively deal with different notions and cultural norms in different times and regions, brands should adopt a fully context-relevant brand safety solution. 

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New Research Highlights the Importance of Brand Safety

New Research Highlights the Importance of Brand Safety

  02:13    Silver Push

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Placement of ads in a brand unsafe environment tarnishes a brand’s image, weakens consumer trust, and results in decrease in revenue. Ensuring brand safety is very important for brands, whether small or big. Like previous research, new research also fully backs this statement.

A recent survey conducted by GroupM, which included fourteen-thousand consumers in twenty-three countries, has shed light on the concerns that consumers have about digital marketing and advised important considerations for digital marketers. According to the survey, more than six in ten (64%) consumers would have a negative opinion of a brand displaying ads against inappropriate content. 37% respondents, i.e. over one-third of respondents, found digital ads to be highly intrusive.

75% of the survey respondents believed that the responsibility to stop harmful or inappropriate content from appearing rests with the digital platforms. They said that proactive steps should be taken by the marketers in order to make sure that parameters are set around ad placements for creating marketing effectiveness and affording protection to brand value.

The survey report found that the trust of consumers in digital marketing is less than expected. The trust factor is very important as brand value is directly correlated with the consumer trust. For brands, it means they should work on building a responsible digital marketing ecosystem that does not dampen consumer trust in brands. For keeping consumer trust intact, a brand should take measures to prevent ad placement against any type of harmful or unsuitable content.

The findings of the above survey are consistent with those of the survey conducted by the Trustworthy Accountability Group (TAG) and Brand Safety Institute (BSI) in 2019. The survey was conducted among the US consumers and included over one-thousand respondents. 90% of the respondents said that ensuring non-placement of ads against unsafe or inappropriate content is very or somewhat important for advertisers.

Over 80 percent said that they would reduce buying or would entirely stop purchasing a product, which they buy regularly, if in case, it is advertised against extreme or dangerous content. 90% respondents said that they would decrease their spending on the product advertised next to the content involving terrorist recruiting videos, while 67% said they would completely stop purchasing it. 70% respondents held advertisers responsible for ensuring ads do not run against unsafe or inappropriate content, while 68 percent held ad agency responsible.

To prevent placement of ads against unsafe content, majority of brands make use of traditional brand safety solutions such as keyword blocking and whitelisted channels. These solutions often limit the reach of the campaigns and hampers monetization.

AI-powered brand safety solutions have appeared in the market, but those dependent on machine learning, NLP and semantic analysis fall flat when it comes to comprehending the sub-text, nuanced contexts and complex relationships words have in written or spoken language.

The innovative AI-powered brand safety solutions that make use of computer vision offer unparalleled context relevance and overcome the limitations of other brand safety methods such as content under and over-blocking. Computer vision enables accurate detection of contexts in online videos such as faces, on-screen text, emotions, logos, objects, scenes and activities. Computer vision powered brand safety solutions contextually filter out harmful or unsuitable content, providing a truly suitable environment to brands for video advertising.

With research continuously backing the importance of brand safety, adopting an effective brand safety strategy is a must for brands.

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Mirrors Safe Goes Beyond Safety to Offer Brand Suitability

Mirrors Safe Goes Beyond Safety to Offer Brand Suitability

  01:22    Silver Push

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Programmatic advertising has made brands vulnerable to the risk of damage to their image. Placement of ads against unsuitable or harmful content can negatively impact the perception of a brand in the minds of consumers, which in turn can lead to decrease in sales.

Silverpush’s Mirror Safe is not only a highly effective brand safety solution, but a full-fledged context relevant brand suitability platform. It allows brands to run their video advertising campaigns in the most brand suitable environment without killing reach.

Mirrors Safe, powered by AI and computer vision, is trained on millions of pieces of visual content. It accurately detects brand unsafe contexts in video, preventing placement of ads against such content. It effectively overcomes the limitations of keyword and NLP-based blanket exclusion technologies such as content under- and over-blocking.

Mirrors Safe offers a tailored approach to brands by allowing them to custom define the harmful contexts unique to them. This brand-specific approach helps brands to move beyond just brand safety to a truly brand suitable environment.

Features of Mirrors Safe

Features of Mirrors Safe can be summarized below –

Contextual filtration of unsafe content

Mirrors Safe accurately detects contexts like faces, objects, on-screen text, logos, emotions, activities and scenes within a streaming video, and contextually filters out harmful content across an extensive set of brand unsafe categories. It allows exclusion of unsuitable contexts custom defined by a brand.

Brand suitability score

Mirrors Safe makes use of an advanced algorithm for the calculation of a comprehensive 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 and 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

Complete control over ad placement

Mirrors Safe ensures absolute brand safety and suitability by predicting and controlling each and every video ad placement before serving an ad impression.

Thorough analysis

Mirrors Safe allows marketers to have an accurate pre, mid and post campaign analysis. It provides sequence depth charts so that marketers can view predetermined parameters in the dashboard.

Mirrors Safe’s post-campaign analysis provides marketers useful information beyond where the in-video ads were placed. Mirrors Safe enables marketers to measure performance of their past video advertising campaigns by identifying ad placements across harmful or unsuitable content.

By identifying content that is brand safe in their current blocklists, marketers can enhance the performance of their campaigns by improving accuracy and reach.

Mirrors Safe optimizes advertising campaigns in real-time, and ensures safe and measurable ad placements. By using this brand suitability platform, marketers are saved from compromising between performance of campaigns and brand safety. They can achieve maximum reach while having absolute control over ad placements.

With no content under- and over-blocking along with custom defining of harmful contexts unique to each brand, Mirrors Safe offers an exceptional brand suitability platform to marketers.      

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