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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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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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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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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  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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  03:24    Silver Push

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Contextual Targeting Offers the Most Viable Advertising Strategy in the GDPR Era

The General Data Protection Regulation (GDPR) has changed the way businesses can handle the personal data of the citizens of the European Union. Any individual, company or organization, whether located in EU or not, that stores or processes EU citizens’ personal data must comply with the GDPR. The GDPR, which came into effect on 25 May 2018, enables EU citizens to exercise control over their personal data.

Article 4(1), defines personal data as follows –

“Personal data means any information relating to an identified or identifiable natural person (data subject); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.”

Online advertising industry is one of the most affected industries by the GDPR. Online advertisers use third-party cookies as the main tool to track users’ online activities for serving them highly specific ads. Cookies are small text files that gets stored in the users’ web browsers.

Third-party cookies serve as a trace for advertisers. Through third-party cookies, advertisers are able to create a rich profile of users that include the websites they visit, their interests, products they buy, and more. Third-party cookies store enough user data to come under the GDPR scanner. 

According to the CIGI-Ipsos Global Survey on Internet Security and Trust 2019, in which more than 25,000 internet users participated from twenty-five countries across the globe, - In 2019, 78% of survey respondents said they were very concerned or somewhat concerned about their online privacy. 53% said they were much more concerned or somewhat more concerned than they were a year ago. While 78% of all respondents in 2019 were concerned about their online privacy, 90% or more were concerned in Egypt, Hong Kong, India, Nigeria, and Mexico, with more than 85% concerned in South Africa, Indonesia, and South Korea.

With rising privacy concerns among the consumers, coming into effect of the GDPR and the California Consumer Privacy Act (CCPA), and the gradual phasing-out of third-party cookies in Chrome by Google, the digital marketers have started looking into alternate ways delivering online ads to consumers that are both effective and compliant with the personal data protection regulations. 

In the era of GDPR and other online privacy laws, contextualtargeting offers an effective way for advertisers to display online ads, while being compliant with the privacy regulation. Contextual advertising allows advertisers to display ads on a website by targeting its content. Ads are displayed on the basis of keywords or topics. This method, therefore, displays ads that are relevant to the content, and hence, increases the chances of users clicking on the ads. For example, if a brand wants to sell smartphones, then it can have its ads placed on the websites that have content about smartphones, gadgets, technology, etc.

An advanced form of contextual advertising involves semantic targeting, which makes use of machine learning algorithms to understand the meaning of each page of content on a website, rather than just looking for keywords placed on a web page.

As with text content, contextual targeting offers a GDPR compliant advertising solution for video content. Conventional contextual video advertising works by identifying keywords. This often results in placement of irrelevant ads. 

The innovative artificial intelligence and computer vision powered in-video contextual advertising technology overcomes the limitations of traditional contextual advertising. It offers an effective, GDPR-compliant solution to advertisers for displaying contextually relevant in-video ads to users. It works by detecting faces, objects, emotions, logos, activities and scenes in video content. It then serves the ads that are fully in line with what the user is currently watching, thus allowing for a very high chance of user engagement.       

The contextual, artificial intelligence advertising does not collect, store or utilize users’ personal data for displaying ads, thus offering a GDPR-compliant approach. It only considers what a user is currently engaging with and serves him the contextually relevant ads.

The AI-powered contextual targeting is highly effective for the advertisers, non-annoying for the consumers, and in compliance with the GDPR.     

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PRISM : Real-Time Analytics & Reporting Tool

PRISM : Real-Time Analytics & Reporting Tool

  23:42    Silver Push

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The Internet era has had disruptive as well as fundamental changes in TV advertisement. Earlier the effective TV reach was limited to measuring TRPs and G Rps, which is now translated into digital footprints and can be measured & thoroughly analysed. As per various reports, an estimated 87% of users nowadays are simultaneously busy on a second screen while watching TV. These reports highlight the fact, TV watching is in transition form an active to passive activity, with the rise of simultaneous device usage making media fragmentation a stark reality for advertisers & marketers to take a note of.

This media fragmentation has made marketers realise the importance of attribution model led approach for better understanding & reworking TV strategies. Once the real time impact is analysed, further course correction ensures higher impressions, leads & conversions. Prism by SilverPush is industry’s best reporting & analytics tool, serving over 1000 clients, in 6 countries. Prism is propelled by proprietary algorithms to track ads in real time across more than 200 channels, and then map their digital KPIs simultaneously. Granular level data gathered by Prism lets the marketers plan & buy TV media by providing most effective time slots &channels, which yield the maximum results. The data provides marketers performance details in terms of daily surge timings, most effective day’s week wise, demographic break downs & other such important filters to thoroughly analyse the digital impact.

PRISM By Silverpush

Once such robust data is available, the resultant TV ad planning & buying leads to guaranteed higher online visits, installs, leads, sign ups, sales, search & social media traction. Prism also helps understand brands to counter their competition by tracking their ads & analyse the same to provide better understanding of the bigger picture & crucial actionable insights.

These performance parameters can be customised as per requirement, all of which are available in a single dashboard to easily understand the results. Along with TV ads, Prism also uses public & private sets of data to analyse digital activity to help brands plan & buy TV ads from the different required platforms. Prism has emerged as the go to reporting & analytics tool for advertisers aiming to make the most of cross-screen marketing.

To summarize, attribution models are going to redefine TV advertisement, for it to remain effective & engaging with viewers, and be the preferred medium of mass advertisement for brands to rely on.

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