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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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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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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Parallels & Prism – Redefine Real Time Marketing

Parallels & Prism – Redefine Real Time Marketing

  02:23    Silver Push

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Just Like consumers, the advertisers today are spoilt for choices when it comes to selecting the right media vehicle to reach the target audience. Given the scenario, being in the right place at the right time is an unparalleled advantage, when mastering the art of moment marketing.

Parallels from SilverPush offers the best solution to streamline digital ads across high reach digital platforms in sync with triggers like TV ads, shows, live events, sports, pollution & weather. These triggers assist the brand in capturing active audiences on digital platforms or screens who are dynamically seeking product info or engaging with the brand on social media, within the same time-stamp of the trigger being detected. Customized positioning based on multiple triggers like competitor’s TV presence & deployment of digital ads to counter their reach is a cakewalk with Parallels, making it the industry’s best marketing mix modelling tool.

Parallel : Marketing Mix Modelling

Research reveals that a healthy chunk of internet users i.e. 87% simultaneously use connected devices while watching TV. Second screen users when exposed to sync ads are exceedingly receptive, in comparison to an always on digital campaign.  The ensuing effect of this strategically timed digital communication is higher brand engagement & improved visibility. Real time cross-screen marketing has emerged as the single most holistic approach for cutting through the media clutter and achieving higher ROI on media spends.

Not yet majorly affected by the digital media onslaught, TV is still a favored medium for mass brands. However, TV ad measurement has been restricted to GRP’s & TRP’s which again are lagged and limited. With the power “to know more” now resting in the hands of the consumers, the immediate impact of the TV ads is most likely to be some form of digital engagements. This then shifts the advertiser’s focus on attribution models to leverage TV media spends.

Knowing the impact your TV ads can have on digital natives is essential in gathering actionable insights to better optimize digital spends while complementing the TV plan. Granular level data in terms of effective time-bands & digital platforms that the audience is available at, prove to be the most vital moment for marketers to deliver effective exposures to these users in real time. In other words, the advertisers can land an impact on consumers with parallel presences across TV & Digital.

Prism Attribution models also enable brands to map competition’s TV presences which can be effectively used to counter or alter the TV media spends & buys in the most optimum manner. Prism by SilverPush is a robust attribution tool that helps brands to monitor & optimize TV ads & their resultant impact on digital KPI’s in real time.Such enriched data allows advertisers to yield the most out of their TV spends & launch digital campaigns that facilitate more meaningful interactions with the consumers.

Multiple brands have experienced the benefits of Synchronized campaigns through Parallels & Prism. Digital experts on multiple occasions have recognized this technology, the latest achievements being the MOBEXX Advertising Excellence Awards 2017 for Parallels & Drum Digital Awards (APAC) 2017 – “Best Attribution Solution” for Prism.

Prism by Silverpush

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