According to various estimates, internet users today attract an overwhelming number of advertisements (ads) as per their browsing behavior. Advertisements disrupt the users’ browsing experience by appearing on desktop and mobile devices in the form of pop-ups, banners, auto-play videos and full-screen images. Ad blocking tools have sprung up as a natural response for neutralizing them. Over 200 million users of such tools bear testimony to the fact that internet users dislike online ads.

 

Ad blockers are giving advertisers a run for their money by ensuring that ads never reach their intended audience. As a result, they have dented the revenues of online business entities that rely on them to sustain themselves. A 2016 estimate pegs global losses due to ad blockers at a whopping $40 billion.

 

Are Advertisers and Publishers Effectively Dealing with Ad Blockers?

Several websites block access to their content unless users deactivate ad blockers or pay to access content. It is an attempt to counter the revenue crunching effects of ad blockers.

 

Research shows that such measures are counter-productive. A huge chunk of internet users would rather stop visiting such sites than being arm-twisted into paying up or disabling these tools. Nonetheless, the majority of users are not averse to watching ads as long as they are not annoying. It sets a clear mandate for advertisers and publishers to serve ads that enrich users’ browsing experience.

 

Is There a Way to Enrich Users’ Viewing Experience?

Yes, advertisers and publishers can start by identifying non-performing ads. An ideal way of identifying non-performing ads is to map the browsing behavior of users with multiple ads. However, it is impossible to accomplish this task manually when you take browsing behavior of millions of users into account.

 

Our client who is a media conglomerate was facing a similar problem. One of their biggest advertisers wanted their ads to appear only in certain premium slots. Our client carried out a manual inspection and discovered that the advertiser’s ads appeared randomly in viewer streams. They were aware of the problem and had huge volumes of data at their disposal to affirm the veracity of the issue. In spite of this, they were unable to fix it. The reason behind this was the huge time and effort required for extracting insights from data rendered it un-actionable. They were in need of an automated solution to achieve efficiency and address the issue.

 

How Our Solution helps in Enhancing Viewing Experience

We wanted to make efficient use of the huge ad server data our client had at their disposal. We, therefore, built an automated BI mechanism that analyzes and reports ad server data in a meaningful way. Our solution addresses three major concerns –

 

1.      Repeated ads

When users see the same ad multiple times in quick succession, it leads to Ad Fatigue. Repeated ads become less effective as viewers learn to ignore them. Our solution helps in this regard by identifying ads that do not adhere to frequency limits set by ad publishers. Non-adherence with pre-set frequency limits indicates an issue with the ad server. Also, our solution helps account executives to serve advertisers better by highlighting ads pushed in the ad server without a frequency limit.

 

2.      Repeat ads of same industry category

Competitive ad separation is an essential requirement for online ad publishers. It dictates that two ads belonging to the same industry category should not appear during the same ad break. For instance, a McDonald’s ad should not immediately follow a KFC or Burger King ad during a viewer’s scheduled ad break. Our solution helps in avoiding such situations and helps publishers in drafting better ad media plans.

 

3.      Ad Load Latency

Ad Load Latency is the time it takes to load an ad at the start of an ad break or after a previous ad ends. While the latency is too small for most users to notice, it may become noticeable while rendering ads of heavier file size on networks with slow bandwidths. This issue has a significant impact on users’ viewing experience, and publishers strive to keep the latency as low as possible. Our solution tracks the latency of every ad with details about the resolution at which the rendering took place. It allows ad publishers to address the issue holistically.

 

The Impact of Our Solution

The following numbers give a statistical account of the impact made by our solution –

 

• 99% Reduction in offending ad views: An offending ad view occurs when the same user views a particular ad multiple times in the same viewing session on a particular site under analysis. Offending views dropped by 99% as compared to earlier, e., before implementation of a fix to the ad server based on our solution
• 85% improvement in problematic viewing sessions: A viewing session is a unique viewer’s single continuous viewing session. A problematic viewing session is one which has at least one offending Ad view as per the definition above
• Saved millions of dollars: A fix to the ad server based on suggestions from this solution’s implementation has saved millions of dollars in lost ad revenues on a yearly basis

Our solution has helped in demonstrating to our customer that effortless exploratory analysis of their ad server data is possible. It has helped them in discovering ad view patterns and identifying areas of revenue leakage. Apart from saving millions of dollars for our client and enhancing their users’ viewing experience, our solution has armed them with insights to create a formidable media brand.

 

As our client’s understanding of the possibilities unearthed by the solution continues to grow, so does our capability to guide them through the advanced stages of business intelligence.

As more consumers turn to on-demand video streaming services such as Netflix and Amazon, media entertainment companies are moving from the traditional DVD/Blu-ray market and further into online streaming.  But, with an almost endless amount of content choices available to consumers, media companies are looking for new ways to attract consumers’ attention to their unique content.

 

To help uplift and promote its home entertainment digital business, one multinational media company launched a new content initiative—developing bonus content like deleted scenes and wallpapers for the titles they have available for viewing on household devices and set top boxes.

 

Since this was a new initiative for the marketing and branding team, they had no system in place to track how the bonus content was performing and whether it was effective in increasing viewership of their content.

 

The Difficulty of Effectively Tracking Bonus Content

At the start of its bonus content initiative, the only data which the media company received from various streaming service providers was basic point of sale (POS) information that showed what movies or series was sold or rented and the number of sales. While this information helped the company determine what movies or series content was doing well, it didn’t allow them to see if the bonus content was helping boost their sales or not. And, even more specifically, what type of bonus content was most effective.

 

At the time, the company was using MicroStrategy as their reporting platform. However, they had limited knowledge of its capabilities and were only using its traditional form of grid style reports downloaded to Excel to determine high level behaviors of movie content. This functionality was not enough to allow them to track the user experience with bonus content. So for help, the media company turned to InfoCepts.

 

Using Meaningful Data and Visualization to Inform Decision-Making

Digging into the project, the InfoCepts team discovered that the web logs of the content servers could be used to identify the user behavior. This data could then be sourced to a pixel perfect dashboard on a daily basis to build pre-canned and meaningful visualization.

 

The InfoCepts team thus built a user-friendly pre-canned dashboard that integrates the data from web server logs and makes it easy to understand the user analytics and their experience with bonus content. The dashboard includes at-a-glance, visual metrics on a number of criteria, such as:

• Overall traffic analysis summary across bonus content
• Title performance analysis from historic period thru current date
• Popularity by content type
• Title performance comparison of current vs. previous month
• Total number of active days of the bonus content across a title or movie

This dashboard not only provided the media company with traffic analysis over hosted bonus content, but through visualization also helped the branding team to review their strategies and plan and release their budget for hosted bonus content based on the popularity and viewing experience of the users. By looking at the historic trend analytics on the dashboard, the team can determine what type of bonus content will perform best.

 

For example, the marketing and branding team discovered that videos and gallery content (wallpapers and screen savers) were the most popular types of content, while interactive was the least popular. Since interactive content is quite costly to develop, having these insights helped the team use their resources wisely and focus on the types of content that perform best for the titles that are most popular.

 

Additionally, the dashboard helped the content team track and direct their resources to popular titles rather than developing new bonus content for less popular or non-performing titles or movies, which is based on the historic trend of viewing experience of the bonus content.

 

Post Live Impacts of the Dashboard Analytics

The dashboard InfoCepts created for the marketing and branding team has been a powerful tool to help the team achieve its goals. Specifically, once the dashboard was implemented, the team was able to use analytics derived from it to:

 

• Substantiate ROI: The analytics dashboard allowed performance KPIs, such as hits/views and unique users, to be readily available to decision makers so they could measure marketing performance and justify the ROI of bonus content.

• React and answer in real time: Thanks to dashboard analytics, the branding team could respond to real-time data insights. For example, the team tracked performance KPIs for the new bonus content to understand how the content was performing during its initial stages of go live and was then able to make adjustments to the content based on the real-time data insights to boost hits and engagement with users.

• Fact-finding analysis: The dashboard also proved its value to perform investigative analysis. For example, spikes on certain days of the month or the far better performance of hits seen during summer vacation than over the Thanksgiving holiday helped the marketing team to effectively plan campaigns for the calendar year around these trends to boost sales.

 

More Insights to Come

The information the marketing and branding team is getting through the dashboard is already helping to drive more sales through better promotion and production of bonus content. However, there is still more personalized insights that additional data points could offer.

 

In the next phase of the project, the InfoCepts team will incorporate geolocation and time log data into the dashboard. This information will help the marketing and branding team create promotions and content for specific geographic regions or times of day.

 

For example, specific titles may do better in certain geographic regions than others or different times throughout the day may have higher viewership opportunities. With this information, the marketing and branding team can further personalize its promotions and content offered to sync better with geographic or time of day preferences.

 

Ultimately, thanks to the insights that the team has been able to extract, the marketing and branding team has been more successful in creating and promoting bonus content that achieves the team’s goals—engaging end users in a meaningful way that pushes them to stream their content over other choices available.

 

Does your marketing team have a complex data analytics problem it’s trying to solve? If so, let’s talk. We can help.