Tuesday, May 3, 2016

Selecting the best artwork for videos through A/B testing

At Netflix, we are constantly looking at ways to help our 81.5M members discover great stories that they will love.  A big part of that is creating a user experience that is intuitive, fun, and meaningfully helps members find and enjoy stories on Netflix as fast as possible.  


This blog post and the corresponding non-technical blog by my Creative Services colleague Nick Nelson take a deeper look at the key findings from our work in image selection -- focusing on how we learned, how we improved the service and how we are constantly developing new technologies to make Netflix better for our members.

Gone in 90 seconds

Broadly, we know that if you don’t capture a member’s attention within 90 seconds, that member will likely lose interest and move onto another activity. Such failed sessions could at times be because we did not show the right content or because we did show the right content but did not provide sufficient evidence as to why our member should watch it. How can we make it easy for our members to evaluate if a piece of content is of interest to them quickly?  


As the old saying goes, a picture is worth a thousand words.  Neuroscientists have discovered that the human brain can process an image in as little as 13 milliseconds, and that across the board, it takes much longer to process text compared to visual information.  Will we be able to improve the experience by improving the images we display in the Netflix experience?


This blog post sheds light on the groundbreaking series of A/B tests Netflix did which resulted in increased member engagement.  Our goals were the following:
  1. Identify artwork that enabled members to find a story they wanted to watch faster.
  2. Ensure that our members increase engagement with each title and also watch more in aggregate.
  3. Ensure that we don’t misrepresent titles as we evaluate multiple images.  


The series of tests we ran is not unlike any other area of the product -- where we relentlessly test our way to a better member experience with an increasingly complex set of hypotheses using the insights we have gained along the way.

Background and motivation

When a typical member comes to the above homepage the member glances at several details for each title including the display artwork (e.g. highlighted “Narcos” artwork in the “Popular on Netflix” row), title (“Narcos”), movie ratings (TV-MA), synopsis, star rating, etc. Through various studies, we found that our members look at the artwork first and then decide whether to look at additional details.  Knowing that, we asked ourselves if we could improve the click-through rate for that first glance?  To answer this question, we sought the support of our Creative Services team who work on creating compelling pieces of artwork that convey the emotion of the entire title in a single image, while staying true to the spirit.  The Creative Services team worked with our studio partners and at times with our internal design team to create multiple artwork variants.


     
Examples of artwork that were used in other contexts that don’t naturally lend themselves to be used on the Netflix service.


Historically, this was a largely unexplored area at Netflix and in the industry in general.  Netflix would get title images from our studio partners that were originally created for a variety of purposes. Some were intended for roadside billboards where they don’t live alongside other titles.  Other images were sourced from DVD cover art which don’t work well in a grid layout in multiple form factors (TV, mobile, etc.).  Knowing that, we set out to develop a data driven framework through which we can find the best artwork for each video, both in the context of the Netflix experience and with the goal of increasing overall engagement -- not just move engagement from one title to another.

Testing our way into a better product

Broadly, Netflix’s A/B testing philosophy is about building incrementally, using data to drive decisions, and failing fast.  When we have a complex area of testing such as image selection, we seek to prove out the hypothesis in incremental steps with increasing rigor and sophistication.


Experiment 1 (single title test with multiple test cells)



One of the earliest tests we ran was on the single title “The Short Game” - an inspiring story about several grade school students competing with each other in the game of golf.   If you see the default artwork for this title you might not realize easily that it is about kids and skip right past it.  Could we create a few artwork variants that increase the audience for a title?


Cells
Cell 1 (Control)
Cell 2
Cell 3
Box Art
Default artwork
14% better take rate
6% better take rate


To answer this question, we built a very simple A/B test where members in each test cell get a different image for that title.  We measured the engagement with the title for each variant - click through rate, aggregate play duration, fraction of plays with short duration, fraction of content viewed (how far did you get through a movie or series), etc.  Sure enough, we saw that we could widen the audience and increase engagement by using different artwork.


A skeptic might say that we may have simply moved hours to this title from other titles on the service.  However, it was an early signal that members are sensitive to artwork changes.  It was also a signal that there were better ways we could help our members find the types of stories they were looking for within the Netflix experience. Knowing this, we embarked on an incrementally larger test to see if we could build a similar positive effect on a larger set of titles.  

Experiment 2 (multi-cell explore-exploit test)

The next experiment ran with a significantly larger set of titles across the popularity spectrum - both blockbusters and niche titles.  The hypothesis for this test was that we can improve aggregate streaming hours for a large member allocation by selecting the best artwork across each of these titles.  


This test was constructed as a two part explore-exploit test.  The “explore” test measured engagement of each candidate artwork for a set of titles.  The “exploit” test served the most engaging artwork (from explore test) for future users and see if we can improve aggregate streaming hours.


Explore test cells
Control cell
Explore Cell 1
Explore Cell 2
Explore cell 3

Serve default artwork for all titles
Serve artwork variant 1 for all titles
Serve artwork variant 2 for all titles
Serve artwork variant 3 for all titles
Measure best artwork variant for each title over 35 days and feed the exploit test.


Exploit test cells
Control cell
Exploit Cell 1
Exploit Cell 2
Exploit cell 3

Serve default artwork for the title
Serve winning artwork for the title based on metric 1
Serve winning artwork for the title based on metric 2
Serve winning artwork for the title based on metric 3
Compare by cell the “Total streaming hours” and “Hour share of the titles we tested”


Using the explore member population, we measured the take rate (click-through rate) of all artwork variants for each title.  We computed take rate by dividing number of plays (barring very short plays) by the number of impressions on the device.  We had several choices for the take rate metric across different grains:
  • Should we include members who watch a few minutes of a title, or just those who watched an entire episode, or those who watched the entire show?
  • Should we aggregate take rate at the country level, region level, or across global population?


Using offline modeling, we narrowed our choices to 3 different take rate metrics using a combination of the above factors.  Here is a pictorial summary of how the two tests were connected.




The results from this test were unambiguous - we significantly raised view share of the titles testing multiple variants of the artwork and we were also able to raise aggregate streaming hours.  It proved that we weren't simply shifting hours.  Showing members more relevant artwork drove them to watch more of something they have not discovered earlier.  We also verified that we did not negatively affect secondary metrics like short duration plays, fraction of content viewed, etc.  We did additional longitudinal A/B tests over many months to ensure that simply changing artwork periodically is not as good as finding a better performing artwork and demonstrated the gains don’t just come from changing the artwork.


There were engineering challenges as we pursued this test.  We had to invest in two major areas - collecting impression data consistently across devices at scale and across time.


1. Client side impression tracking:  One of the key components to measuring take rate is knowing how often a title image came into the viewport on the device (impressions).  This meant that every major device platform needed to track every image that came into the viewport when a member stopped to consider it even for a fraction of a second.  Every one of these micro-events is compacted and sent periodically as a part of the member session data.  Every device should consistently measure impressions even though scrolling on an iPad is very different than the navigation on a TV.  We collect billions of such impressions daily with low loss rate across every stage in the network - a low storage device might evict events before successfully sending them, lossiness on the network, etc.


2. Stable identifiers for each artwork:  An area that was surprisingly challenging was creating stable unique ids for each artwork.  Our Creative Services team steadily makes changes to the artwork - changing title treatment, touching up to improve quality, sourcing higher resolution artwork, etc.  
house-of-cards-blog-details.png
The above diagram shows the anatomy of the artwork - it contains the background image, a localized title treatment in most languages we support, an optional ‘new episode’ badge, and a Netflix logo for any of our original content.


These two images have different aspect ratios and localized title treatments but have the same lineage ID.


So, we created a system that automatically grouped artwork that had different aspect ratios, crops, touch ups, localized title treatments but had the same background image.  Images that share the same background image were associated with the same “lineage ID”.


Even as Creative Services changed the title treatment and the crop, we logged the data using the lineage ID of the artwork.  Our algorithms can combine data from our global member base even as their preferred locale varied.  This improved our data particularly in smaller countries and less common languages.

Experiment 3 (single cell title level explore test)

While the earlier experiment was successful, there are faster and more equitable ways to learn the performance of an artwork.  We wish to impose on the fewest number of randomly selected members for the least amount of time before we can confidently determine the best artwork for every title on the service.  


Experiment 2 pre-allocated each title into several equal sized cells -- one per artwork variant. We potentially wasted impressions because every image, including known under-performing ones, continue to get impressions for many days.  Also, based on the allocation size, say 2 million members, we would accurately detect performance of images for popular titles but not for niche titles due to sample size.  If we allocated a lot more members, say 20 million members, then we would accurately learn performance of artwork for niche titles but we would be over exposing poor performing artwork of the popular titles.


Experiment 2 did not handle dynamic changes to the number of images that needed evaluation.  i.e. we could not evaluate 10 images for a popular title while evaluating just 2 for another.


We tried to address all of these limitations in the design for a new “title level explore test”.  In this new experiment, all members of the explore population are in a single cell.  We dynamically assign an artwork variant for every (member, title) pair just before the title gets shown to the member.  In essence, we are performing the A/B test for every title with a cell for each artwork.  Since the allocation happens at the title level, we are now able to accommodate different number of artwork variants per title.


This new test design allowed us to get results even faster than experiment 2 since the first N members, say 1 million, who see a title could be used to evaluate performance of its image variants.  We continue to stay in explore phase as long as it takes for us to determine a significant winner -- typically a few days.  After that, we exploit the win and all members enjoy the benefit by seeing the winning artwork.


Here are some screenshots from the tool that we use to track relative artwork performance.
Dragons: Race to the Edge: the two marked images below significantly outperformed all others.

Conclusion

Over the course of this series of tests, we have found many interesting trends among the winning images as detailed in this blog post.  Images that have expressive facial emotion that conveys the tone of the title do particularly well.  Our framework needs to account for the fact that winning images might be quite different in various parts of the world.  Artwork featuring recognizable or polarizing characters from the title tend to do well.  Selecting the best artwork has improved the Netflix product experience in material ways.  We were able to help our members find and enjoy titles faster.


We are far from done when it comes to improving artwork selection.  We have several dimensions along which we continue to experiment.  Can we move beyond artwork and optimize across all asset types (artwork, motion billboards, trailers, montages, etc.)  and choose between the best asset types for a title on a single canvas?


This project brought together the many strengths of Netflix including a deep partnership between best-in-class engineering teams, our Creative Services design team, and our studio partners.  If you are interested in joining us on such exciting pursuits, then please look at our open job descriptions around product innovation and machine learning.


(on behalf of the teams that collaborated)