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How technology is helping with brand safety

by Mateusz Jędrocha

April 5, 2022

In modern digital advertising, brands need to take steps to protect their image by controlling where their ads appear, especially in a world where media consumption is ever-changing and news headlines are unpredictable. After all, there is nothing more valuable than brand image, it's the most important intangible asset a company owns. Thanks to the advancements in technology, it's now even easier to meet brand safety expectations through AI-powered campaigns.

The importance of brand safety

Did you know that 71% of consumers are more likely to buy from a brand name that they recognize, and around 81% of consumers consider brand trust to be a key factor when deciding to make a purchase?

According to the Interactive Advertising Bureau's (IAB) general recommendations, brand safety should be applied to every advertising campaign to protect a brand’s image and reputation and not fund any illegal or dangerous content providers.

However, policing where content is displayed is becoming more challenging as more ads move online and ad-bidding networks become increasingly large. To this end, the IAB has defined thirteen unsafe digital environments. Previously referred to as the ‘dirty dozen’, a fake news category is the 13th and most recent addition to the list.

Ads appearing next to controversial topics still remains a hot button issue, and approximately two-thirds of consumers believe that ads shouldn’t appear next to controversial news topics. More worryingly, studies have found that many consumers believe that brands are intentionally placing content next to negative content.

Current brand safety solutions

There are already several steps that marketers take to ensure ads aren’t placed in unsafe locations.

  • Pre-bid blocking – is used by DSPs (demand-side platforms) to evaluate impressions before the auction happens and allows brands and advertisers to eliminate impressions from an unsafe environment at a very early stage. Any solutions used here should be both accurate and efficient, to maximize a campaign’s potential and ensure brands don’t miss out on valuable impressions.
  • Post bid pre-impression blocking - these solutions are an insurance policy for cases where pre-bid blocking solutions may have failed or are not available with the current vendor. As the banner loads, brand safety is checked, and, in cases of brand safety violation, the brand’s content is hidden.
  • Post-impression measurement - measuring impression and reporting on the brand safety level is a great way to evaluate and adjust brand safety rules to the brand’s needs. The best ad tech partners can provide tiered brand safety reporting, which is more insightful and allows for a flexible approach towards excluding different types of content.

The most advanced platforms can combine different solutions to ensure safe and scalable campaign delivery. With AI algorithms greatly improving the efficiency of such solutions, technology advancement should be one of the key factors in choosing the right partner. At RTB House, we combine sophisticated AI-powered pre-bid blocking with measurement coming from industry-leading providers to ensure the highest standards of campaign delivery.

Things to consider when deciding on what’s unsafe:

  • Identifying words and content that are inappropriate for the brand.
  • Choosing language that serves the ad and its potential placement. Many words can have more than one meaning so it’s important to decide whether (a) it is appropriate, or (b) if there is a clearer alternative. For example, the word ‘leave’ has four very different meanings; to go away from somewhere, to remain, to deposit, or to be absent from work.
  • Checking what language the audience speaks. For example, the word ‘mist’ in English means hazy, unclear and is generally used in reference to the weather, whereas in Germany, the same word refers to manure, dung and general garbage.
  • Using brand safety dashboards that help brands monitor their campaigns and see real-time results and metrics across all channels.

However, no matter how dedicated you are, there will always be a risk of your content being shown alongside negative content.

Now you could check each website individually, but that is an impossible task.

So what’s the solution? Technology is the key.

AI Technology and brand safety

Many ad tech firms are already utilizing the power of AI to help brands conduct precise inspections at the page levels. This includes inspecting URLs, content, and metadata before allowing an ad to be placed there. The best algorithms use Deep Learning and Natural Language Processing to recognize unwanted keywords, language, context and graphics. For example, RTB House’s Brand Safety Solution blocks, on average, around 80 million harmful impressions per day.

This new technology is helping ad tech firms give brands more choice and better control over where their ads appear, which is very important in maintaining a positive brand image.

Using AI means that brand safety can be fully customized for each brand. This helps brands avoid risk in real-time, and at the same time, a tailored plan adheres to individual brand safety policies. Additionally, many technological solutions filter all traffic before buying ad placements, which can significantly lower budget waste.

AI tools can also help predict the impact on the traffic of a campaign before applying policies and, in many cases, works on transparent, semi-transparent, and non-transparent URLs.

What brand safety features can technology power?

When it comes to content, modern technology can help brands filter:

  • SSP and third-party solutions
    The filtering of any illegal or offensive websites, detecting and blocking unsafe web pages that contain malware or phishing content (e.g. Google Play Protect, SSP built-in brand safety mechanism).
  • Publisher category filters
    Helps brands avoid content categories that may be in poor taste or damage brand standards.
  • Custom keyword blocking
    AI technology can rapidly and comprehensively inspect a URL and content and automatically block websites, including selected keywords. Given how rich open market inventory is, AI algorithms are the most powerful way of reliably blocking unsafe content. This includes blocking unsafe content in multiple languages, taking into account conjugation and broader context, where potential harmful phrases are located. An AI-driven approach is the best way to minimize threats without sacrificing safe and valuable touchpoints with the users.
  • Custom URL, app ID, allowed and not allowed List
    This type of functionality can block any domain, app, URL, or page content and helps brands avoid ad placements that are not in line with their brand safety policy. When combined with positive ContextAI targeting, data sets can be targeted that meet both ContextAI and brand safety requirements.

Make brand safety your priority

The last thing a brand wants is to be displayed in a compromising position that might go viral. After all, brand image and reputation can make or break a company. An article by Marketing Dive revealed that when ads appear near negative content: “Consumers' brand quality perception decreases by 7 times, and consumers are 50% less likely to recommend the brand.”

With the need for brands to take care of their image more than ever before, it’s important for advertisers and brands to source reliable partners and begin using brand safety solutions sooner rather than later.

How RTB House can help you

RTB House’s Brand Safety Solution is a standard component with all our offerings and gives advertisers and agencies an unparalleled amount of control over their or their client’s brand safety, and provides them with the tools to prove it. Through the use of algorithms, RTB House can remove selective unwanted contexts without limiting the scale of the campaign.

To discover how you can benefit from our expertise, and how our Brand Safety Solution can help your brand maintain a positive image, contact RTB House today.

Tags

Digital Advertising
brand safety
Deep learning
AI