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MarTech Advisor: my6sense Enhances its AI Technology to Augment Engagement and User Experience

The following article was published this week on MarTech Advisor

New York, NY: With native advertising expected to grow from $18.59 Billion in 2017 to $23.22 Billion in 2018 just in the USA alone – a 20% increase, according to eMarketer – organizations on both the supply and demand side of the digital marketing eco-system are looking for ways to increase ad revenue and raise user engagement levels while respecting the user’s time and experience.

my6sense, a comprehensive white label programmatic native ad platform and exchange, is enhancing its Artificial Intelligence (AI) technology, enabling over 100 ad serving and media group clients to improve performance and user experience through better optimization, relevancy and personalization.

Specifically, Digital Intuition®, the company’s advanced machine learning algorithms and optimization engine, learns user behaviors (content clicked, time spent, etc.), publisher patterns (trending content, performance patterns, etc.) and context (for targeted and source content) in order to best match the right content to the right users. Through the integration of rules-based criteria like geo-targeting and external data sources, Digital Intuition® is able to offer users content which is both more relevant and more personal, resulting in higher engagement rates for marketers and better revenue for publishers and supply partners.

For example, through my6sense’s personalization, the company was able to increase conversions for a leading brand of diapers. Unlike its traditional campaign targeting to young mothers, the optimization engine detected engaging response from fathers who purchase diapers in the evening on their way home, and seamlessly extended campaign serving to that audience to improve performance.

Beyond the company’s proprietary AI-based optimization technology, my6sense is the only fully programmatic native exchange solution for multi-item native ad units combined with comprehensive organic content recirculation. my6sense’s clients and partners can easily mix recommendation widgets, in-feed, in-ad and any other custom native ad units with any number of ad items and units on a page, knowing that the platform’s powerful AI and exchange technologies will optimize paid ads and organic content for the best performance and user experience.

The my6sense white label programmatic native solution includes a full native exchange working with native Demand Side Platforms (DSPs), such as StackAdapt, built-in direct campaign manager for advertisers and ad-unit & yield manager for publishers. In addition, the company’s solution provides hierarchical business management tools for ad networks and media & publisher groups as well as comprehensive ad quality classifications and control, performance optimization and advanced policy rules. Flexible configurations used by my6sense’s partners include a white labeled native ad network and exchange, owned managed private exchange for group publishers, and cross-network programmatically traded supply and demand.

“AI technology not only matters in self-driving cars and derivatives trading but also in programmatic native advertising, where our proprietary technology with combined user behavioral collaborative filtering and contextual models deliver greater engagement, more revenue and a better user experience,” said Avinoam Rubinstain, CEO & founder, my6sense.

The Year In Media: Founder Stories – Avinoam Rubinstein of My6Sense

My6sense is a native advertising platform that enables ad networks and media companies to build their own customized ads for mobile and web content. The company was founded by Avinoam Rubinstein, a serial entrepreneur with over 20 years of experience in the industry. He was previously CEO of Atrica (acquired by Nokia), and GM at NiceCom.

We sat down with Avinoam to learn about his company’s startup journey.

Tell us about your company’s beginnings. What did you see (or not see) in the market that led you to launch?

We established my6sense because we believed that digital platforms are best suited to provide users with personalized and relevant content. With the advancements driven by machine learning technology, including our own Digital Intuition® advanced machine learning algorithms and optimization engine, we’re even closer to realizing our technology-driven vision of enabling publishers to offer the right content to the right users at the right time.

Specifically, my6sense’s technology learns user behaviors, publisher patterns, and context in order to match the content with the user. Our Digital Intuition® technology is able to offer users content which is both more relevant and more personal. This enables publishers and supply partners to generate greater revenue as marketers benefit from higher engagement rates.

What were some of the biggest challenges that your team faced at zero stage?

As an engineer, the biggest challenge is always getting the technology right. Once the technology works, everything else falls into place because if you build it ‘right’, they really do come.

Digital publishing is a dynamic market which has evolved over the years, and today, we apply our AI and machine learning technology for our White Label Programmatic Native Ad Platform and Exchange, which enables more than 100 ad serving and media group clients to improve performance and user experience through better optimization, relevancy, and personalization of their native content and ads.

Let’s look at the science behind your product. What makes it different from other offerings in this space?

What sets my6sense apart among the native programmatic solutions is that ours is the only offering which is a fully programmatic native exchange solution for multi-item native ad units with comprehensive organic content recirculation. Only my6sense enables our clients and partners to mix a recommendation widget, like Outbrain or Taboola offer, with any in-feed, in-ad or other custom native unit with any number of ads and units on a given page. And our Digital Intuition® machine learning technology further differentiates our solution by enabling better matching of content to users, resulting in better user engagement metrics for marketers and greater revenue for our supply side partners.

The my6sense white label programmatic native solution provides hierarchical business management tools for ad networks and media & publisher groups as well as comprehensive ad quality classifications and control, performance optimization and advanced policy rules. For our Demand Side Platforms (DSPs), my6sense offers a full native exchange with a built-in direct campaign manager for advertisers and ad-unit & yield manager for publishers. Our partners make use of my6sense’s flexible configurations, including a white labeled native ad network and exchange, owned managed private exchange for group publishers, and cross-network programmatically traded supply and demand.

How do you translate your brand’s message in a way that gets you heard above the noise?

A lot of companies pivot towards what’s hot, so when ‘native advertising’ become hot a couple of years ago, a lot of ad tech companies jumped on the native advertising bandwagon. My6sense’s initial technology was developed to serve more personalized and relevant content to users, so we already had technology which was optimized for native advertising. In that sense, my6sense is a company whose technology is ‘built for’ and not ‘pivoted into’ native advertising. And that’s why our focus is exclusively on native advertising today. This differentiation, coupled with the mix of native ad formats we enable our partners to run and the efficacy of our technology enables my6sense to stand out.

Let’s talk about brand values. What means the most to your company besides industry success?

What means the most to my6sense is satisfying the user/consumer.

We make money by recommending contextually relevant native advertising and content for marketers via our partners, but if users don’t find the content and ads our technology recommends relevant, interesting and engaging, they won’t click. And if they don’t click and engage, our partners will select different technology to power their native advertising and content.

Therefore, it all goes back to our ability to satisfy the user and in turn our customers and business partners.

There’s always been this rivalry between Silicon Valley and NYC in tech. What are some tangible benefits to being based in NYC?

For my6sense, there are two major benefits to being based in NYC. First, as an advertising technology company, New York City is the center of both the advertising and publishing industries which means that all of our prospective partners and customers are either based in NYC or have a large presence in town. The second major benefit for my6sense is that NYC is closer to Tel Aviv, where our R&D center is located, both in terms of time zones (7 hour difference versus a 10 hour difference between Israel and the West Coast) and in terms of the number and frequency of direct flights.

Name one place in your company’s NYC neighborhood (restaurant, cafe, etc) that you and your team just can’t live without.

Easy: Katz’s Deli. There is nothing else like it anywhere, and yet it’s quintessentially New York. It’s a little out of our neighborhood but worth the effort.

5 Best Practices for Native Ad Networks

Native advertising is very different from traditional display ad advertising; Instead of trying to capture users’ attention by standing out as much as possible, native advertising tries to blend-in with the content. It strives to provide the same user experience as the content around it and optimizes for additional performance indicators such as user satisfaction, user retention, etc. This different approach poses new challenges for ad networks wishing to enter the native advertising market. In this article we have centralized some tips and best practices for these new native ad networks.

  1. Customize the widget/ad unit to blend with the publisher’s look and feel – The widget is the ad unit element on the publisher’s page that receives ads and displays them to the user. Usually these widgets can be customized in terms of their appearance and the functionality/behavior. The main theme here is “blend to get attention”. The following list of attributes are some of many that should be customized to match the page’s design and user experience:
    • Colors
    • Fonts
    • Widget title
    • Image sizes, aspect ratio, shape
    • Widget dimensions and responsive behavior for different screen sizes
    • Mouse roll-over behavior
    • Widget location in reference to the page content and layout
    • Mixing of organic and sponsored content (applicable in multi-item widgets)
    • and more…
  2. Place the ad units relatively high – the position of native ad units has an impact on its performance. Traditionally content recommendation widgets were usually placed at the end of the article (i.e. at the bottom of a page) as recommendations for additional content. However, native ads have since evolved into various additional ad formats, such as In-feed ads, In-ad, and more. These ad units have more freedom in terms of their position on the page. Placing them “above the fold” will most likely increase their performance. When placing the widget at the end of the article it should be placed as close to the article text as possible, before talkbacks and other navigation suggestions. In addition, try to place the ad units within a safe distance from other irrelevant elements, such as large images, other widgets, dense banners, promotion elements etc.
  3. Select the most appropriate ad unit format – as mentioned above, today native advertising includes many formats and new ones are gradually added. Each ad unit (widget) format has its own characteristic and advantages. The IAB has released a playbook for native ad units and another playbook that focuses on In-feed native ads.
  4. Less intervention is more – as opposed to the world of display advertising, native advertising platforms are equipped with advanced matching algorithms that decide in real time which ad / content to serve, based on a wide variety of factors. These decisions are based on a lot of data signals about the user, the publication and the content of the page. Many ad networks are very much accustomed to manual targeting or purchasing of an “audience”. However when a matching technology serves native advertising this type of control may be less effective in terms of its performance than the platform’s algorithms.
  5. Using iframe – in order to protect the page, some publishers require serving the ads inside an iframe (Inline Frame), an HTML document embedded inside another HTML document on a website. The challenge is that the iframe isolates the ad unit (widget) from the surrounding page and therefore it is not possible to match the ad to the context of the page. This of course derogates the performance of the ad as it may not be optimally targeted. If the publisher requires the use of iframes, it is recommended to use a “friendly iframe”, a commonly used iframe type which protects the page style and design yet provides the widget code with access to the page content

 

The secret ingredient behind effective native advertising

I have recently stumbled upon a video explaining about the ingredients in McDonald’s fries. Turns out that everyone’s favorite fast food includes 19 different ingredients. Putting aside the criticism and health-related concerns that spawned after the release of this video, it is clear that in order to create a consistent product with the same texture and taste all year long, McDonalds needed to include many additional ingredients on top of the simple potato. I think most customers understood that these fries have more than what meets the eye.

Okay, so you are thinking, what do french fries have to do with native advertising? Well, by looking at a publisher’s page with native ads and content recommendations you may not think much of these native ads just like you would not think much of the fries. However, the decision to deliver these native ads and recommendations is not coincidental. In a world where performance, engagement, and user experience are of upmost importance, the technology behind this decision can be the difference between success and failure. In this post, I will take a behind the scenes look at the various native advertising recommendation and matching technologies, as these are the secret ingredients behind effective native advertising.

Basic Campaign Parameters

Traditional advertising systems allow the advertiser (demand-side) to define various targeting rules for each campaign. These rules can be basic, such as publisher sites, audience and GEO, to more elaborate rules such as timing, location, demographic, and more. This type of targeting is, of course, very basic, as it makes broad assumptions regarding the relevancy of ads. On the other hand, they allow some degree of control as to where the ad will appear.

Contextual Matching

Contextual matching technologies analyze the content of the designated article or web page (on the publisher’s website) in order to match relevant ads. For example, a page that discusses a health-related topic may be relevant to health-related ads. Most native advertising platforms and networks use contextual matching as the means to deliver relevant native ads to users.

Of course, some technologies and algorithms may vary in their sophistication levels. Advanced technologies may make an inferred connection between the content of the page and the topic of the ad. For example, they may find a connection between the mentioned health-related page to a broader topic such as fitness. Broadening the scope of related topics is essential for the user experience (to avoid monolithic recommendations) and the utilization of the ads inventory.

Although this form of matching is effective to some extent, it is still restricted to the content of the page, which may not represent the entire spectrum of tastes and interests of the user.

User Behavioral Matching

This technology focuses on the user itself. It analyzes the users‘ behavior, from the feedback that users implicitly provide during content consumption throughout time to create a high-dimensional preference model for each individual user. The model is then used for real-time content and ad selection. This type of targeting is more effective, as it captures the user’s tastes and preferences, their moods in various times during the day, and the specific content consumption habits. The targeting is not only effective, it can utilize more campaigns and match them to the existing content. For example, a female user visiting a sports site may receive an ad about baby formula. Using only contextual matching, this ad will never appear on the sports site. However, through the use of user behavioral matching technology this is certainly possible.

Collaborative filtering and trend analysis

Collaborative filtering is a targeting method that is based on the collective preferences or taste information from many users. The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B’s opinion on a different issue x than to have the opinion on x of a person chosen randomly. This type of technology is commonly used in e-commerce sites, by displaying related items that interested other users. This method is of course also relevant for the matching of native advertising. In this case, items are replaced by content recommendations and native ads.
Collaborative data is also used to perform engagement trend analysis. For example, the technology understands which articles are “hot” or trending at a particular publisher’s site and then to factor-in this data in the targeting optimization.

External data signals

Advanced matching technologies also factor-in external signals from the publisher’s DMP, social networks, SSPs, etc. These signals can include demographic data, gender, commerce profiles, and more. These signals enrich the set of parameters used in the matching optimization. For example, data from the publisher’s DMP can distinguish between paying subscribers and non-paying users. By using this information, the technology can target specific native ads/content to the paying subscribers versus the non-paying ones, based on its optimization algorithms.

Mixing all the ingredients at once

As we can see, all the technologies (ingredients) mentioned above can contribute to the native ads matching optimization. However combining them all at once is extremely challenging. It’s like having multiple opinions to make a single decision, which ad to serve. my6sense’s native advertising platform uniquely combines all these ingredients in real-time. Every time an ad/content recommendation is served, a sophisticated algorithm uses all these ingredients to help make this decision, while applying different “weights” in order to maximize the monetization for publishers, performance for advertisers, and the best user experience for readers.

To learn more about my6sense’s technology – click here