Moving from Video Interruption to Integration Through Contextual Advertising

Moving from Video Interruption to Integration Through Contextual Advertising

  00:32    Silver Push

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Moving from Video Interruption to Integration Through Contextual Advertising

Video advertising has become a highly effective tool for brands and marketers to communicate their messages to online audiences. In the year 2018, $10.228bn were spent in digital video advertising in the United States alone.

Since its inception, video advertising has faced the criticism of being inherently interruptive in nature. Online users tend to skip or ignore ads that appear to them as annoying or interruptive. This non-engagement of users with the ads is a serious headache for marketers, and they are struggling to find the way out.        

Research shows that brands are now increasingly concerned about the interruptive user experience when running their video advertising campaigns. For brands, user experience has become a significant factor to consider while devising their video advertising strategy. Brands are trying to make in-video ads more engaging and less annoying for users. In order to draw effective user engagement, ads should be able to capture the user attention in the first few seconds.

Video ad formats also play an important role in driving better user experience and engagement. By deploying better formats, marketers can make their advertising strategy more effective. According to eMarketer, sixty percent of video ads fall in the in-stream category. Majority of the marketers are currently deploying interruptive in-stream ad formats that can be easily skipped or ignored. Pre-roll and post-roll are used more often over other formats such as overlay video and mid-roll. Overlay ad format, which places an ad over the video content, offers an advantage over other in-stream formats of not overtly interrupting the underlying video content being watched by the user. Thus, overlay ads are less annoying and tend to provide better user experience.

Mid-roll format is least favored by the marketers, especially for short video content, because it is highly interruptive. Therefore, marketers are currently mainly utilizing pre- and post-roll in-stream ad formats for achieving their advertising goals and the whole video content itself is being left uncapitalized. Here, the overlay format comes to rescue. It allows marketers to capitalize on the unutilized video content without being interruptive to users.  

Brands have identified certain obstacles in the path of video ad innovation. These include inadequate budget, lack of in-house expertise, misaligned in-house teams, lack of agency relationship, and lack of strong external technology partnership. To achieve their video advertising goals, brands and marketers should focus on innovative, non-interrupting ad formats. But only using the right in-video ad format will not work, marketers should make use of robust, efficient and effective advertising technology such as AI advertising, and give utter importance to context. They should serve contextually relevant in-video ads that are well-aligned with the content the user is consuming.

According to the industry experts, video advertising strategy in the future will be impacted by factors such as demand for non-interruptive ads, growth of over-the-top and connected TV ad formats, growth of social networking and video sharing advertising formats, and demand for contextual advertising.

Users’ demand for non-interruptive ads on connected TV has grown over the years in sync with the growth in its viewership. Currently, in-stream video advertising on this platform is largely interruptive in nature. Contextual video advertising offers an effective solution to marketers to mitigate this interruption on connected TV and enhance user experience. 

By using the right mix of in-video ad formats and contextually aligned ads, advertisers can turn upside down the user experience from being interruptive to engaging. To display contextually aligned video ads, a highly effective solution is afforded by AI powered in-video context detection technology.

However, AI-powered solutions that are dependent on machine learning, NLP and semantic analysis, miss the mark when it comes to understanding the sub-text, nuanced contexts and complex relationships words have in written or spoken language.   

Computer vision powered contextual advertising technology provides very high degree of context relevance. This technology works by accurately detecting contexts in video such as faces, emotions, logos, objects, scenes and activities in order to display in-video ads that are in line with what the user is actively engaging with. Highly contextually relevant ads appear non-interruptive and appealing to users, and boost the chances of users watching or clicking them.     

With AI-powered contextual video advertising, marketers can seamlessly integrate ads with the video content the user is watching, thereby providing a non-interruptive, highly engaging user experience.      

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Choosing Between Contextual Advertising and Audience Targeting – Which is Better?

Choosing Between Contextual Advertising and Audience Targeting – Which is Better?

  01:02    Silver Push

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Choosing Between Contextual Advertising and Audience Targeting – Which is Better?

For marketers, choosing contextual advertising over audience targeting or vice versa has always been dilemmatic. Contextual advertising involves placement of ads on the basis of the content the user is engaging with, whereas audience targeting involves use of cookies to track users and display ads. Here, we will try to provide a solution to this dilemma. We will consider the data available from different studies and draw a conclusion.

Balancing audience and context is difficult for the majority of marketers, shows a survey conducted by Sizmek. The solution to this problem is not just as simple as targeting both audience and context, as for 8 in 10 of the survey participants, targeting both has a negative impact on the advertising campaign.

90% of the participants said that they expect to increase the scale of their contextual targeting and find new audiences. 80% said that in the coming year, it will be either a critical or high priority for them to achieve both audience and contextual targeting at scale.

For the direct response campaigns, 42% of the participants said that their targeting strategy involves only audience or gives more importance to audience over context, while 37% said audience and context are equally important. 21% said that their targeting strategy involves only context or gives more importance to context over audience.

For the brand campaigns, 37% of the participants said that their targeting strategy involves only audience or gives more importance to audience over context, while 35% said audience and context are equally important. 28% of the respondents said their targeting strategy involves only context or gives more importance to context over audience.

75% of the respondents agreed that deploying artificial intelligence in advertising increases the performance of the ad campaigns. 81% said that they plan to increase the use of artificial intelligence in advertising.

With the coming in effect of the data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), and the gradual phasing-out of third-party cookies in Chrome by Google, a major shift from cookie-based audience targeting to contextual advertising has begun.

77% of the survey’s participants anticipated that the GDPR and other privacy laws will make it harder for marketers to target audience through the means of third-party data. 80% of the respondents said they will improve their contextual targeting.

Industry experts think that AI-powered contextual advertising will become the preferred mode of online advertising as it does not rely on users’ data.

Another study published on The Drum compared contextual targeting to audience targeting in terms of attention, cost and brand lift. It found that the average time spent by individuals on ads in the case of contextual advertising was 12% greater than when audience targeting was used. On a CPM basis, audience targeting cost was found to be 4.7 times more expensive than contextual targeting. Apart from the data costs, the media costs incurred in the case of contextual targeting were 6.3% lesser than audience targeting.

Brand lift per second of attention was 2.1% in the case of audience targeting, while it was 3% for contextual targeting. At developing brand impact per second of attention, contextual targeting came out to be 43% more efficient than audience targeting. The study found that for every dollar spent, contextual targeting is 7.5 times more efficient than audience targeting in achieving brand lift.

From the above two studies, two conclusions can be drawn – firstly, contextual advertising is much more effective and efficient than audience targeting, and secondly, marketers are increasingly considering contextual targeting, especially AI-powered contextual advertising.

However, most of the AI powered solutions currently available in the market depend on the use of machine learning (ML), natural language processing (NLP) and semantic analysis. These solutions fail to truly understand the sub-text, nuanced contexts, and complex relationship words have in written or spoken language.  

AI-powered contextual advertising solutions that use computer vision are far more effective and accurate, offering a higher degree of context relevance that overcomes the limitations of traditional keyword targeting and NLP based technologies. Computer vision enables detection of contexts in video such as faces, emotions, logos, objects, scenes and activities with high accuracy, thus allowing advertisers to display in-video ads that are in line with what the user is actively engaging with. Computer vision powered contextual advertising allows brands to achieve unparalleled user engagement, reach and ROI.

The benefits offered by AI and computer vision powered contextual targeting are not limited to boosting performance of ad campaigns, but also include successful resolution of the grave problem of brand safety. Accurate detection of unsafe or inappropriate contexts in video enables marketers to effectively avoid any sort of undesired ad placement, thus ensuring brand safety.

For marketers seeking an advertising strategy that is compliant with privacy regulations, cookieless, highly effective and brand safe, AI and computer vision powered contextual advertising surpasses audience targeting.  

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Silverpush Launches Mirrors Safe, An AI-Powered Context-Relevant Brand Suitability Platform

Silverpush Launches Mirrors Safe, An AI-Powered Context-Relevant Brand Suitability Platform

  06:09    Silver Push

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Silverpush Launches Mirrors Safe, An AI-Powered Context-Relevant Brand Suitability Platform

Silverpush, a leading AI-powered technology solutions company, today announced the launch of their new brand suitability platform Mirrors Safe. This product forms a part of the company’s computer vision-enabled video intelligence product suite, which is transforming how some of the largest brands globally reach their most engaged audience in a brand-safe environment. 

Brands today face a significant financial risk from a potential crisis involving their advertising. A 2019 study by Trustworthy Accountability Group (TAG) and Brand Safety Institute (BSI) found that 80% of customers will stop or reduce buying products that place advertisements across harmful or offensive content, and around 70% blame the brand or the agency for such placement. This survey exhibits the real and measurable risk that a preventable brand safety crisis can pose. 

Through the use of AI-powered in-video context detection, Mirrors Safe transcends challenges of traditional brand safety measures like keyword-based technologies and whitelisted channels. Trained with millions of pieces of visual content, the platform identifies in-video contexts like objects, actions, faces, and scenes within a streaming video, and contextually filters out harmful content across an extensive set of brand unsafe categories including nudity, smoking, violence, crashes, and much more. 

Mirrors Safe’s advanced algorithm uses five parameters to calculate a comprehensive brand suitability score, that measures not only safety and suitability of content, but also of the page and the channel. These include: 

• 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. 

Kartik Mehta, CRO at Silverpush 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”. 

In a testing phase, Silverpush analysed ~15 million videos across the largest video hosting and sharing platforms in South East Asia using Mirrors Safe. Overall, 8-9% of content was found to be brand unsafe across multiple categories, indicating that nearly 1 in every 9 video ad placements are placed around harmful and damaging content. Top unsafe content categories identified include smoking, violence, adult, and extremist content. 

To further showcase the impact context-relevant brand safety can bring in, the company used both traditional measures and Mirrors Safe’s in-video context detection to identify content for one of the top brand unsafe categories – nudity and adult content. Mirrors Safe, through its frame by frame parsing of video content, identified 300% more videos featuring in-video unsafe contexts in this category, compared to traditional methods. 

Even a single misplaced ad can harm brand perception for a long time in the consumer’s mind– this discovery points out the inefficacy and potential harm that existing measures can have, which has been witnessed time and again from continued failures of some of the largest video advertising platforms in keeping brands safe. 

Kartik further added, “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 favour 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 monetisation and performance.”

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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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  01:56    Silver Push

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How are brands responding to COVID-19? A brand marketer survey across SEA market

Brands have been profoundly affected by the coronavirus pandemic. Brands’ response to the coronavirus pandemic not only impacts consumers’ trust today, but it will also significantly impact future purchasing decisions. Moreover, brands could face irreparable damage to their reputation due to brand safety risks associated with COVID-19 related content.

To gain insight into how brands are responding to COVID-19 pandemic, Silverpush conducted a survey of 150+ agency heads, business leads in media, and brand marketers in the SEA region in April 2020.

The survey aimed to understand how brands are adapting their marketing strategies to the impact of the COVID-19 outbreak and how they are mitigating the very real brand safety risks the rapidly growing coronavirus related content consumption poses.

How are brands re-imaging and engaging consumers in light of the pandemic?

The survey found that in the light of the pandemic, brands are reimaging by adapting their marketing tone and initiatives to consumer expectations. Only 5% respondents reported no change in brand positioning pre and post COVID-19, whereas 95% reported a distinct shift that resonates with government policies, and responds to the new consumer expectation.

Ad spending poised to decline

The industries heavily impacted by coronavirus outbreak such as travel, hospitality, physical retail and more have and will continue to paused marketing initiatives. Only 16% respondents said these industries will protect marketing budgets for a stronger comeback later.

Moreover, the survey indicates that it is unlikely that the industries such as health and FMCG that are currently experiencing higher demand will increase marketing spend to capture the demand more aggressively. Even though past recessions have shown that aggressive cuts in ad spends can lead to longer recovery cycles.

Ad Spends are shifting to digital channels

Even with significantly increased TV viewership across SEA, boosted due to government-imposed lockdowns across the region, and various studies indicating curtailed TV ad spends can adversely affect brand health measures - only 2% respondents said brands are spending more on TV and mainstream media, and a large percentage indicated rapid shift to various digital channels.

Brand safety is a key concern, and is driving ad spend cuts

Industries, except few such as health, hygiene, pharma, etc., are stringently avoiding advertising across COVID-19 related content. Publisher news sites and news channels on platforms like YouTube are facing advertisers’ block-lists due to coronavirus-related coverage.

A measure of advertisers’ confidence on brand safety tools is depicted by how despite using third party tools to ensure safe ad placements, brands are reducing marketing budgets and pausing advertising specifically to avoid association with Coronavirus related content.

71% respondents reported brands are reducing marketing budgets ranging from complete halt of marketing spends leading to up-to 80% budget cuts, in order to avoid running ads across coronavirus related content

Can context relevance be the answer?  

Emerging AI powered solutions are increasingly focusing on providing context relevance, and are fast becoming an answer to brand safety woes. AI enables processing of large volumes of data at speed, with better context, at higher scale and improved targeting efficiencies.

However, most of these contextual targeting solutions still depend on the use of NLP and semantic analysis, not truly understanding the sub-text, nuanced contexts, and complex relationship words have in written or spoken language.

AI and computer vision-powered video advertising solutions can detect in-video contexts, offering a higher degree of context relevance that surpasses limitations of traditional keyword targeting and NLP based technologies. They offer unparalleled insight for advertisers to place context-relevant in video ads and exclude unsafe content in a highly structured manner, and at the scale programmatic has traditionally offered.

You can access the full report ‘Brand Response to COVID-19 in SEA’ for detailed insights from the survey. 

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  23:28    Silver Push

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Understanding the Post Covid-19 Contactless Workplaces

The coronavirus pandemic has changed many aspects of human lives. Many people are now working from home. The post Covid-19 workplaces will not be the same conventional workplaces that people have been familiar with for years. Workplace safety will take priority over other matters.

The workplaces will undergo a radical shift from touch-based to touchless. Things like fingerprint-based biometric devices, touch-based screens for booking conference and meeting rooms, kiosks for guest check-in, handle-operated doors, etc. will have to be replaced with viable alternatives to prevent the spread of infectious diseases and ensure employee safety.

The contactless workplaces will make use of automation and touchless technology. Normal doors will be replaced by automatic doors, elevators will be voice-controlled, lighting system will adjust brightness automatically according to the time of the day, temperature control system will be adjusted by gestures or voice, and water dispensers will automatically pour water on keeping a bottle or glass below the tap.

The washrooms will have touchless automatic faucets, hand-free soap dispensers, and automatic flush powered by infrared technology. These products will not only reduce spread of germs, but also save water and soap. Along with touchless hand dryer, touchless paper towel dispenser will also be provided.

The contactless workplaces will make use of contactless attendance system powered by facial recognition technology in place of fingerprint-based biometric attendance system. A computer vision powered face-recognition-based attendance system can easily recognize the faces of employees for the purpose of attendance. Computer vision is an advanced field of artificial intelligence that enables computers to see like human beings and easily identify visual content in images and videos.

Besides powering the face recognition biometric systems, computer vision powered solutions can help employers ensure wearing of face masks by employees and enforce workplace social distancing and sanitization compliance. This technology can also bring into notice if an employee coughs or sneezes.

As an employee health and safety measure, workplaces will have to make use of touchless temperature recording technology. This technology will work by using thermal sensors for recording temperature of both employees and visitors. If any anomaly is detected, it will be instantly reported to the concerned department.

Post Covid-19 workplaces will not allow sharing of accessories such as headphones and each employee will be provided individual accessories along with laptops or desktops. Professional cleaning and sanitization protocols will be regularly implemented for workstations, devices, conference and meeting rooms, reception area, cafes, etc. Easy access to hand sanitizers will be provided throughout the workplace for both employees and visitors. The sensor-based touchless garbage bins will provide a safe way to dispose of garbage.

Workplaces will incorporate antimicrobial materials into interior design elements such as wall paints, door sheets, window shades, etc. Such materials will resist the growth of microbes, thus providing a cleaner surface. To ensure workplace social distancing, workplaces will follow a de-clustering approach by keeping individual employee desks wide apart from each other. This can be achieved by using a larger office area or by creating branch offices. Encasing individual desks will provide added protection from transmission through respiratory droplets. Post Covid-19 workplaces will require advanced air filtration technology in order to effectively filter out disease causing microorganisms.

The post Covid-19 contactless workplaces, by making use of advanced technologies, will help employers to carry out their business, while ensuring employee health and safety.

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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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