General

AYLIEN Customer Case Study – Complex Media

About

Complex is a New York-based media platform for youth culture which was founded as a bi-monthly magazine by fashion designer Marc Ecko. Complex reports on trends in style, pop culture, music, sports and sneakers with a focus on niche cultures such as streetwear, sneaker culture, hip-hop, and graphic art. Complex currently reaches over 120 million unique users per month across its owned and operated and partner sites, socials and YouTube channels”

complex

Background

Digital ad sales is big business. How big? Well, we’re about to see digital ad spending in the US surpass TV for the first time, representing 37% of all US ad spending going in to 2017. That big! For publishers like Complex, engaged visitors means a greater exposure to ads, higher click rates and, as a result, they are able to generate a sustainable revenue stream across their publishing network.

A large, active and engaged target audience is exactly what advertisers like to see. As a result, publishers focus their efforts on providing unique, engaging and relevant content to their readers, which helps keep them active on their site, promotes future return visits and increases brand recognition.

The Challenge

The Complex Media Network welcomes more than 120 million unique visitors each month. Ultimately, the goal is to serve each and every individual user with relevant ads based on the content they are viewing and/or based on a number of other factors such as geographic location, demographic profile, device type, time of day and many more.

Complex offer varied ad-targeting features across their network, enabling ad partners to target readers based on the previously mentioned factors and triggers. However, until recently, they had little to offer partners on contextually placed advertisements.

A contextual advertising system analyzes website text for keywords and returns advertisements to the webpage based on those keywords. For example, if a visitor is reading an article about fashion, they can be targeted with ads for related products or services, such as clothes and sneakers.

The need for digital ads to become contextually relevant is greater than ever before as web users move away from engaging with online advertisements. These stats and figures certainly confirm the challenge faced by online publishers and emphasize the need for more targeted ad campaigns;

  • In a study, only 2.8% of participants thought that ads on websites were relevant to them.
  • A January 2014 study found that 18 to 34 year olds were far more likely to ignore online ads, such as banners and those on social media and search engines, than they were traditional TV, radio and newspaper ads.This is a huge chunk of the Complex target market.
  • The average clickthrough rate of display ads across all formats and placements is just 0.06%
  • Users who are retargeted to are 70% more likely to convert.

In particular, Complex were looking to automate video insertion in articles to help scale views across their sites. They already had an automatic video insertion widget in place, however the information being fed into it was the result of a manual process that ultimately proved to be unreliable. They required an intelligent automation of this process.

The Solution

With up to 70,000 web pages being analyzed on a daily basis, Complex needed to automate their processes by automatically categorizing and tagging articles based on topics, keywords and mentions of specific entities. This data could then be fed into their video insertion widget.

Complex display content-relevant videos towards the end of many of their articles. These videos contain pre-roll ads that are targeted specifically to the reader. For example, I was reading an article about Frank Ocean and at the bottom of this article I was offered a video related to singer.

If I’m reading a story about a certain person or topic, the chances are that I’ll be interested in viewing a related video. When I clicked play I was fed a pre-roll ad about mortgages from a bank here in Ireland. Yep, I’m currently house-hunting so this targeted ad was bang on the money!

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Complex display content-relevant videos towards the end of many of their articles

Endpoints used

Complex are using our Concept Extraction endpoint to extract and disambiguate mentions of celebrities, companies, brands and locations from online content and our Classification endpoint to then categorize this content for indexing among their various publications and channels. Let’s take a closer look at each endpoint and how Complex use them to improve their processes;

Concept Extraction

The Concept Extraction endpoint extracts named entities mentioned in a document, disambiguates and cross-links them to DBpedia and Linked Data entities, along with their semantic types (including DBpedia and schema.org types). By extracting concepts, Complex could easily understand what people, places, organizations and brands are mentioned in the articles they publish and were then able to produce a rich tagging system to assist with their ad targeting.

Here’s an example from our live demo. We entered the URL for an article about rappers Lil Wayne, Birdman and Tyga and received the following results;

Screen Shot 2016-09-20 at 18.22.43

Classification

Originally, Complex were using keywords in their video insertion widget that were manually entered by editors via their CMS. However these proved to be unreliable and insufficient so they decided to automatically extract them using our Classification endpoint.

The Classification endpoint classifies, or categorizes, a piece of text according to your choice of taxonomy, either IPTC Subject Codes or IAB QAG. Complex classify their articles according to IAB QAG.

Using the same article, we analyzed the URL and received the following results;

Screen Shot 2016-09-20 at 18.29.42

The first category returned was Celebrity Fan/Gossip which fits the bill perfectly in this instance. Note how confidence in the other categories gradually declines. While still somewhat relevant, we declare our lack of confidence in them. By providing confidence scores, users can define their own parameters in terms of what confidence levels to accept, decline or review.

“Since working with the AYLIEN Text Analysis API, we have seen great improvement in CTRs on our video widget, which translates to preroll revenue.” – Ronit Shaham, Complex

The outcome

Understanding content at this depth has enabled Complex to place pin-point accurate videos and creative ads throughout their content in a semantic, less intrusive way. The concepts, categories and data points extracted are used to organize this content while being fed into an intelligent contextual ad recommendation engine and video insertion widget, which has led to a significant improvement in Click Through Rates from videos embedded within the content. This increase in CTRs has naturally boosted pre-roll revenues for Complex.

In particular, Complex found the extracted keywords to be most accurate among a number of solutions they trialled, which ultimately led them to choosing the AYLIEN Text Analysis API.

Complex Summary colour (1)

As consumers of online content become more and more immune to the effects of online ads, marketers and publishers are having to find ways to connect with them on a more personal level. Through a combination of data collection, text analysis and machine learning techniques, highly-personalized and targeted ads can now be served instantly, based on the content itself and viewer demographics. This really is a win-win for all involved as the visitor sees useful material, the publisher sees higher CTRs and the advertiser receives more traffic coming in from these clicks.

Wanna learn more semantics in advertising? Check out our blog post – Semantic Advertising and Text Analysis gives more targeted ad campaigns

 




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Noel Bambrick

Customer Success Manager @ AYLIEN A graduate of the Dublin Institute of Technology and Digital Marketing Institute in Ireland, Noel heads up Customer Success here at AYLIEN. A keen runner, writer and traveller, Noel joined the team having previously gained experience with SaaS companies in Australia and Canada. Twitter: @noelbambrick