Video Programming Essentials: Understanding Your Video Data

by | Jul 31, 2019 | Infographics, Video Programming Insights

In this post, we cover everything you need to know about video data and how to best optimize your video strategy, no matter your experience level or role.

In the ever-evolving publishing industry, roles and responsibilities and team structures are subject to change, sometimes with little notice or ramp-up time. Teams often have to do more with fewer resources; editorial folks have to take the lead on data-driven projects, or new roles have to be filled to manage a rapidly growing video business. Whether you are new to your role and need a video data crash course, or have been working with video for decades, understanding video data can seem daunting, yet important for ensuring that KPIs are met. Below, you will see a series of content pieces to keep you well informed.

 

Topics Covered:


 

Metadata

What is metadata?

Metadata is detailed content information (title, category, tags, file size, video length, keywords, etc.) that communicates between the content and the means of discovery: search, recommendations, and personalization. The more encompassing and consistently structured the metadata, the more likely the asset will be discovered and viewed by the right user. Metadata makes content findable and understandable to a human or machine.

Over the past ten years, metadata has transitioned from being largely viewed as an outsourced and/or “intern task” into one of the most important components for intelligently managing online video. Healthy metadata is critical to maximizing user engagement from video personalization as well as customizing programming so that publishers can adhere to editorial or business rules.

Many publishers fall into one of three categories when it comes to metadata:

  • Very little or no metadata exists for the vast majority of their digital assets,
  • Some metadata is available but it hasn’t been updated or improved in a while,
  • Tons of metadata exists but may not be consistent and/or accurate

It can seem daunting for many teams to approach their digital library with this in mind but don’t fear it, it’s not as painful as you might think. Below are a few best practices to remember when you take a look at your metadata health. For more, see our updated guide

Get started by checking your metadata health with the checklist below:

Check out these helpful links for more on metadata:

Taxonomy

What is Taxonomy?

Taxonomy, as it relates to video content, is a way of describing and classifying your digital assets. A well-tailored taxonomy is the foundation of any successful publisher’s video strategy. If metadata is the lifeblood of content discovery, taxonomy is the circulatory system. A taxonomy’s configuration of categories and subcategories provides context for the discovery and recommendation algorithm which interprets a video’s more granular associated metadata (keywords/tags, title, description, etc.) to facilitate video recommendations. Taxonomy’s purpose is classification. Consider the relationship between taxonomy and metadata this way: taxonomy organizes information while metadata describes it.

No matter what AI-based video programming platform you use, all algorithmic-based systems work best when video asset metadata is structured in a category taxonomy.

A well-constructed taxonomy not only supports discovery and recommendations but also enables you to have more precise video programming controls as well as gain actionable insight from analytics.

  • Editorial teams want the benefits of automation, while still having the ability to override recommendations according to business rules and editorial standards.
  • Content Acquisition wants to better understand the supply and demand of their assets.
  • Audience Development and Marketing teams want to know what categories are resonating with their new, returning, and loyal audiences.
  • Revenue teams want to be able to better package up inventory and optimize their branded campaigns.

Without a video taxonomy, you cannot do any of these things effectively.

See an example of a taxonomy below (from our Publisher’s Guide to the Best Video Taxonomy)

 

The real ROI comes from a publisher’s ability to create a taxonomy that simultaneously takes into account its business goals (branded video campaigns, increasing views/viewer retention, etc.) while also considering the subtleties of the brand’s editorial that resonate most with their target audience.

Metadata Enrichment

Now that you know what metadata is and why healthy and consistent metadata is critical for an optimized video strategy, let’s discuss metadata enrichment. 

Metadata enrichment is a system that can create categories and keywords where none exist and enrich existing taxonomical structures. If a publisher has sparse or nonexistent data, IRIS.TV’s data ingestion and automated structuring system, Asset IQ™ can enhance or create the missing data. 

 

 

Essential Video Metrics and How to Apply Them

Below is an excerpt from our comprehensive post, Top Video Metrics and How to Take Action From Them.

When it comes to measuring the success of your video business, each team within your organization will measure performance based on different metrics. Product teams will look at user experience, Editorial will pay attention to content performance, while Revenue teams will look at advertising and subscriber yield.

Because revenue, product, and editorial teams approach and define success differently within their own unique set of goals, we work with publishers to apply video metrics cross-functionally. In this first part of an ongoing series on video metrics, we’ll go over the basics of how our clients optimize their video performance and which metrics they are looking at to track their overall success to tell their unique story.

Key Metrics

  • Experience: A playback of an IRIS.TV stream of videos, continuing until the user either exits the player, abandons the stream or clicks on a non-recommended video.
  • Initial Views: The first video played by click-to-play and autoplay. Since an initial view signals the start of a new experience, initial views = experiences.
  • Recommended Views: A video programmed by IRIS.TV Adaptive Stream™ that follows an initial view in a linear stream or in a carousel thumbnail.
  • Total Views: The sum of Initial Views and Recommended Views.
  • Video Lift: The percentage increase in video views as a result of IRIS.TV video recommendations. This is calculated by dividing Recommended Views by Initial Views.
  • Bounce Rate: The percentage of experiences that do not result in the presentation of a recommended view.

See below for more helpful resources to accompany this post:

To learn more about video data and how to better enable it for video programming, contact us today.