Effective Ways for Marketers for YouTube Targeting

Effective Ways for Marketers for YouTube Targeting

  06:05    Silver Push

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YouTube is the most popular video hosting website in the world with most extensive catalog of online videos. The number of monthly logged-in users on YouTube is about 2 billion. YouTube offers tremendous opportunities to advertisers.

YouTube provides a wide range of ad formats and varied targeting options to marketers, enabling them to effectively and easily reach their target audience. It is important for marketers to use the right YouTubetargeting options in order to drive success to their YouTube advertising campaigns. Using basic keyword and topic targeting at the start may not result in success. Below are discussed some of the hand-picked ways that you as a marketer can use for targeting on YouTube -

Custom Intent Audiences

This targeting option helps marketers in reaching new customers on YouTube on the basis of the keywords used by the users to search for products and services on Google.com. It is not necessary that these audiences have any previous interaction with your brand. These are built from users who have recently searched the keywords that were used by you for to creating your audience.

Some of the great custom intent audiences that you can test out for your video advertising campaigns on YouTube are -

·       Converting search queries - You can use your list of converting queries from your search campaigns to display video ads to users who have searched for these queries.

·       Converting keywords – These keywords are not the same as converting search queries. You can create a different audience based on these.

·       Competitor terms - These allow you to show your video ads to users that are actively searching for your competitors. 

·       Best-selling products - You can create an audience from your best-selling products. You can easily find these products from the sales report of your e-commerce platform. 

Life Events

Life Events can prove to be a great targeting option. It allows you to show ads to customers during life milestones such as starting a business, graduating from college, changing or starting a job, getting married, purchasing a house, retiring from job, etc.

These life events provide a great opportunity to brands, which offer products or services needed in these moments, to emotionally connect with consumers. For effective targeting via life events, your video creative should clearly show consumers how your brand can help them go through these milestones smoothly.

TrueView Discovery Ads

TrueView discovery ads appear on the search results and watch pages on YouTube. YouTube is a huge search engine, next only to Google.com. It allows users to conduct search for specific videos that they are interested in. TrueView discovery ads campaign is unique as a video advertising campaign as it is the only one in Google Ads that allows marketers to target just the YouTube's search results page.

Combining keyword targeting with TrueView discovery campaigns offers a great way to capitalize on user intent. By running TrueView discovery ad campaigns, marketers can take over the top spot of the search results page on YouTube.

Computer Vision-Powered Contextual Targeting

This is a highly effective YouTube targeting method that enables marketers to dramatically boost the performance of their YouTube video advertising campaigns. Computer vision enables AI advertising platforms to detect and understand contexts in online videos. Faces, emotions, logos, objects, activities and scenes in online videos can be detected with high accuracy.

By using computer vision powered contextual targeting, you can place your ad against the video content that is highly relevant to the ad, i.e. your ad is fully in line with the content the user is actively engaging with. As the ad shown matches the current interest of the user, the chances of user viewing or clicking the ad are very high.

AI contextual targeting powered by computer vision offers a very high degree of contextual relevance unmatched by other methods of contextual advertising such as keyword targeting, which fail to fully reflect the user’s current state of mind and cannot understand nuances in context.

The above-mentioned, hand-picked ways for YouTube targeting will help marketers in effectively achieving their YouTube advertising goals.    

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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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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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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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Protecting brand reputation with AI

Protecting brand reputation with AI

  04:27    Silver Push

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We just learned something quite distressing – that one in 10 videos out in the online jungle we call the internet – can contain something potentially ‘harmful’ and ‘damaging’.

What we mean by this is that some videos contain certain elements that may not be accurately reflected by the title or tags associated with it. This poses a problem for brands who may not want to be associated with adult themes or extreme violence.

To find out what this means for brands and what options they have, we spoke to Kartik Mehta, Chief Revenue Officer, SilverPush. With their new product Mirrors Safe, the brand offers an AI-Powered context-relevant brand suitability platform to help prevent unwanted associations.

In order to build this product and understand they extent of the issue, Silverpush reviewed 15 million videos across the largest video hosting and sharing platforms in the SEA region using Mirrors Safe. With one in 10 or 10% containing images or negative associations (according to Silverpush criteria), there is definitely a need for a solution.

Congrats on the launch of Mirrors Safe. How do you see this new product helping brands and their advertisements?

Brands today are faced with different types of risks – financial risks, legal risks, and I guess the most important part of it is the reputation risk which could have larger concerns and probably a long-lasting impact on the overall brand equity. The existing brand safety measures like blocklists and whitelists are primarily focused on the principle of exclusion, which does protect brands to an extent but can also lead to one of the most pressing brand safety related concerns of over-blocking. Which can lead to brands missing the opportunity of engaging with the audiences across the right kind of content. 

Whereas Mirrors Safe’s computer vision powered in-video context detection identifies faces, actions, scenes, emotions, on-screen-text in a streaming video to detect content that features violence, smoking, nudity, arms & guns and more. It detects these contexts only when they feature in a video, and not just by relying on keywords used to describe the video – which often times are misleading and can result both in unsafe placements as well as over blocking. 

For instance, a video featuring smoking or violence might not be described so in its title, description or meta tags. There is no way for keyword-based solutions to identify these damaging contexts to filter out this video. Which can lead to household brands advertising across content which is highly unsuitable for their brand image. On the other hand, keywords like shoot, kill, crash, and even gun (some of the most blocked keywords) can easily be used within perfectly safe contexts, (like movies and songs). 

Moreover, Mirrors Safe’s context detection makes it possible to offer brand suitability that can be customized for each brand or each category without following the blanket exclusion principles. 

According to your research, 1 in 10 videos are deemed to be associated with dangerous or damaging content. How were you able to analyze around 15 million videos to generate this data?

Silverpush churned approximately 15 million videos across the largest video hosting and sharing platforms in the SEA region using Mirrors Safe. We used a randomly chosen inventory across platforms from our existing database – previously used to run campaigns using our video advertising platform Mirrors.  

It was found that nearly 8-9% of analyzed content to be deemed brand unsafe. This means these videos featured one or more unsafe contexts like nudity, smoking, violence, arms and guns, and more. 

However, a bigger discovery was the difference between the results found by exclusion through traditional methods like keyword lists vs. Mirror Safe’s in-video context detection technology.

For instance, when we used both methods to identify unsafe videos for one of the top brand unsafe categories – nudity and adult content, Mirrors Safe (through its frame-by-frame parsing) identified 300% more video content featuring unsafe context in this category, compared to exclusion through keyword lists.

A single damaging ad placement can harm brand perception in the consumer’s mind. This discovery highlights the potential harm that existing traditional measures are unable to detect. This has been witnessed time and time again, with some of the largest video advertising platforms being unable to keep brands safe from damaging content.

Have you been able to measure or estimate the negative impact of these associations for the brand? 

There is already a plethora of information available on how even a single ad placement across harmful content can irreparably damage brand perception for a long time in the consumers mind. A 2019 study Trustworthy Accountability Group & Brand Safety Institute found that 80% consumers will stop or reduce buying products advertised against extreme or violent content. And, 70% believe advertiser and the agency are most responsible for a brand’s ad placements.

With our platform Mirrors, we have been serving contextually targeted video advertising across platforms since 2018. And we identified the challenge posed by traditional brand safety measures while serving our clients. We realized that ensuring brand safety is even more of a challenge across video formats, as NLP based technologies that work for other formats are ineffective in gauging the right context featured in video content. 

Conversations and feedback from partners first led us to introduce a safety feature in Mirrors, where brands working with us did not report a single unsafe exposure since the launch of the feature. This further led us to launch Mirrors Safe, which can be deployed as a standalone context relevant suitability platform. 

How has this solution helped brands so far? Do you have any initial test cases or beta usage that you can share?

I will start with the most interesting use case, that is highly relevant today.  

Helped brands navigate the extreme over-blocking of COVID-19 related content 

As brands and platforms rapidly add terms associated with COVID-19 to their keyword block, Coronavirus has become one of the most blocked keywords today. We have found advertisers looking to avoid unsafe brand exposure around this sensitive topic are forced to exclude news entirely from their list of targeted channels and publishers. However, excluding news and related channels entirely from advertising strategies across platforms is killing reach for brands. 

One of the key factors behind extreme COVID-19 related over-blocking is the inability to detect if the COVID-19 related stories are informative Vs. stories that can harm brands – leading to blanket exclusions. Mirrors Safe identified what the video content features to differentiate the stories, in the following ways: 

• On-screen text recognition: Mirrors Safe identifies and filters out videos that have related text written on the screen (e.g. Coronavirus or COVID-19). And can help differentiate between morbidity related stories Vs. more positive stories around for instance precautions. 
• Object and action detection: the system can identify objects like masks, stretchers, and actions like coughing and sneezing and understand the concentration of this content within a single video through frame by frame parsing.
• Faces: with this outbreak certain public figures are also on brands’ blocklists (yes, Trump). In-video context detection can accurately identify faces to filter out related content.

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  22:06    Silver Push

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Top Applications of Face Recognition Technology

The work on face recognition technology started decades ago, but only recently this technology has achieved widespread use. A facial recognition system is used to identify a person from his face. A person can be identified when he is present physically, or from his photograph or video.

Once face detection and analysis was considered a part of science fiction. But, now with the introduction of this technology in the smartphones, many people have become well acquainted with its use. There are many use cases deploying facial recognition technology, some of them are given here below:     

Access control

Whether it is about having access to a smartphone, or to a building, or crossing a country’s border, facial recognition technology is there to make it as secure as possible. By deploying this technology places such as a school, workplace, residence, etc. become highly secure as only authorized persons can enter into the premises.

Along with sensor-based automatic doors, face recognition allows touchless entry and exit for employees. This will enable employers to ensure employee health and safety at post Covid-19 contactless workplaces.

Crime prevention and identification of criminals

Facial recognition technology is deployed for conducting police checks. In the U.S, law enforcement agencies use this technology to run searches against licensed drivers’ database. To identify a suspect in a huge crowd, a large aerial camera fitted on a drone and connected to a face detection system can be used. In retail outlets, this technology can identify a person with a history of shoplifting right at the time when he is entering the premises.

Facial recognition-based CCTV systems can be used to find missing children, victims of human trafficking, and criminals. In 2018, Delhi police identified 2930 missing children while test running a new facial recognition software.

Attendance tracking

Although fingerprint-based biometric attendance systems have proved to be effective at workplaces, they carry an inherent risk of transmission of contagious diseases such as Covid-19. Being touch-based, they can easily transfer viruses and bacteria from one person to another. Face recognition attendance systems, powered by computer vision, work in a contactless manner, thus providing an edge over the fingerprint-based systems. They will help prevent spread of infectious diseases at post Covid-19 workplaces.

Video advertising

Computer vision powered face detection has revolutionized the video advertising industry. By recognizing faces of the characters in the online videos, this technology enables placing of in-video ads that are in line with what a user is watching. Besides faces, computer vision technology can easily identify emotions, objects, scenes and activities in video. This advanced form of in-video contextual advertising is highly effective, allowing brands to achieve unprecedented reach and user engagement.   

Health

Face recognition has been used to diagnose diseases. A face detection software has been used by the researchers at the National Human Genome Research Institute (NHGRI) in the United States to successfully diagnose a rare, genetic condition known as DiGeorge syndrome. Facial analysis has made it possible to track medication use by a patient in a more accurate manner. This technology has also been used in the assessment of pain levels in order to support pain management.

From contactless attendance to video advertising, there are varied uses of the face recognition technology. More of its use cases will surface in the near future as this technology is progressing at a fast pace.

M-Shield, developed by Silverpush, is an AI-powered facial recognition-based attendance, access management and human monitoring system. This social distancing platform makes workplaces and public spaces safe by preventing the spread of contagious diseases such as Covid-19.

M-Shield makes use of facial recognition technology, powered by computer vision, for contactless attendance, entry/exit access management, and mask and social distancing compliance. Its touchless attendance tracking system accurately identifies the faces of employees, even if they are wearing masks. It ensures mask compliance and detects whether the mask is properly worn or not. M-Shield ensures workplace social distancing by tracking minimum distance requirements between employees. Its contactless temperature monitoring technology detects any anomaly in body temperature. It offers added safety by generating an alert if someone coughs, sneezes, or do a handshake.

M-Shield will help employers re-introduce workforce back into offices while ensuring employee health and safety, and compliance with Covid-19 related policies. It will enable government to ensure public safety, when the Covid-19 lockdown lifts.

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  21:56    Silver Push

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Is Computer Vision the Sure-Shot Solution to Brand Safety Woes?

Today, brands are not only concerned about the return on investment (ROI) when they run an advertising campaign, but also about where their ads are appearing. They don’t want their ads to be placed against any sort of harmful, unsafe or inappropriate content, as any single placement of an ad against such content can critically damage brand image. The scale and speed at which the programmatic advertising works has made it quite difficult for brands to ensure brand safety.

Brand safety has become a major concern since few years back, when one after another disastrous ad placement issues came into light. It was found that some famous brands were unknowingly supporting terrorism by ad placement against hate videos on YouTube. Another finding that shook the video advertising world was that ads of some of the biggest brands were seen running against the videos of child exploitation.

Brand safety poses a serious challenge to brands. The placement of ads against unsafe video content not only puts a brand’s reputation at stake, but it also leads to loss of consumers’ trust in the brand. This, in turn, leads to brand avoidance and decrease in sales.   

There are some brand safety measures that advertisers have been using, but these methods are quite far from being fully reliable and effective. A keyword blacklist details words and phrases that describe content against which a brand does not want to have its ads placed. But this keyword-based brand safety method does not take into account nuances and context, thereby letting in some unsafe placements or blocking some safe placements.

Using whitelisted channels limits the reach that a brand can achieve through social media platforms. Another reason that makes whitelisted channels a less sought-after option is that this method is quite expensive.

Using manual methods for filtering out unsafe content is not feasible keeping in view the enormous volume of video content that is uploaded on an hourly basis.

By bringing in context to advertising, artificial intelligence offers a remedy to brand safety woes. Although artificialintelligence advertising solutions that use machine learning (ML), natural language processing (NLP) and semantic analysis, work by understanding the context of a webpage and automatically regarding content as unsafe or appropriate, they fail to effectively ensure brand safety, especially, in video advertising.

The true remedy to brand safety woes is provided by AI advertising technology that makes use of computer vision. Computer vision enables detection of contexts in video with high accuracy, thus allowing advertisers to display context-relevant in-video ads in a brand safe manner.  

By using computer vision, computers are able to see, identify and process images and videos just like human beings do or even better than that. Computer vision based in-video context detection technology can easily and accurately identify faces, objects, emotions, logos, activities and scenes in videos. This enables advertisers to display in-video ads that are fully in line with the video content that a user is watching, while strictly avoiding ad placement against any content that has been regarded inappropriate or unsafe by a brand.

The computer vision based in-video context detection technology provides double benefits – firstly, it displays ads against the relevant video content, thus boosting the chances of a user’s engagement with the ad, and secondly, it effectively avoids ad placement against brand unsafe content. With computer vision, brands can really play safe when it comes to displaying in-video ads on online video platforms.     

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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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Mirrors; AI-powered context detection technology by Silverpush

Mirrors; AI-powered context detection technology by Silverpush

  23:59    Silver Push

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The world today is cluttered with advertisements making it difficult for the advertisers to target the right audience. To solve this problem, Silverpush- the leading global platform for cross-screen marketing technology has launched the company’s artificial intelligence (AI) driven context detection technology; Mirrors. Mirrors is able to identify logos, emotions, objects, and faces in the videos by using AI with computer vision. It detects the context in a video that helps the advertisers in contextual targeting. As video is predicted to become the next universal format for content, Mirrors is Silverpush’s response to the industry problem of random ads placement in videos.

Mirrors identifies objects and persons in the video content with its powerful detection technology and AI, and then assists in the implementation and placement of contextual advertising. Relevant ads are shown in context to the content that is being consumed by the people, strategically showing them the context that they might be interested in. This helps the advertisers in enhancing the user experience and targets their messages more efficiently.

contextual video ads

Mr. Kartik Mehta, Chief Revenue Officer of SilverPush, said, “Mirrors is the culmination of years of research to understand how users are engaging with brands when watching online video content. With Mirrors, we are creating endless contextual possibilities and are exploring every possible avenue to elevate the user experience for consumers; while helping established and emerging brands operating in APAC target their messages more effectively.” Mr. Mehta also added that this technology, although is a B2B solution for contextualin-video advertising right now, it can possibly reach a stage where consumer interactions start taking place, as well.

contextual marketing platform

The internet and smartphone penetration rates are rising with each passing day, letting people consume more online video acrossthe region. In this scenario, where brands are looking to interact with consumers in a better way and initiate contextual ads, Mirror’s technology will be one relevant platform for that. According to the data analytics, the forecasted worth for pay-tv and OTT video will be worth $77.4 billion by 2021.

(SilverPush has been recently awarded the Best Cross-Platform Campaign at The Drum Digital Trading Awards APAC 2018 for their work with Unilever. They also won Smarties Bronze at MMA APAC awards for Coca-Cola.) 

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