This was an evaluative research study meant to uncover and assess the discoverability, understandability, and effectiveness of a new content recommendation feature on the HBO Max homepage, Recommended by Humans.

Two design variations of the component was tested with HBO Max subscribers across the United States. Following the conclusion of my internship, these findings informed design decisions and impacted iterations of Recommended by Humans.

In 2019, HBO Max launched Recommended by Humans as a "human-powered" recommendation tool, distinguishing itself from other algorithmic-based competitors.

The goal of Recommended by Humans? To leverage the power of recommendations from real humans — not algorithms — to “showcase the emotional connection HBO viewers have with the network's programming” (The Verge, 2019).

This study focused on evaluating the Recommended by Humans Feature Component sat on the HBO Max homepage, visible to viewers worldwide.

Imagine yourself on the HBO Max homepage — perhaps you're scrolling through, uncertain about what to watch next. This study focused on the Recommended by Humans feature component, sat on the homepage, meant to direct viewers to a new content recommendation as well as introduce this new recommendation engine.

Within 5 weeks, the product team wanted to learn more about this feature component's...

Let's break this down into specific research goals to uncover.

• Evaluate the discoverability of this feature component on the homepage.
• Evaluate the understandability this feature component’s purpose and promotion.
• Assess whether this feature component is an effective way to display (UI), promote (content meaning), and provide access (function) to featured HBO Max content.

Part of evaluating the feature component included understanding the efficacy of it's individual parts. Such as: What title is best? What image is best? Are the buttons clear?

To unpack this further, I spent time in 1:1 meetings with designers to unpack the purpose of each individual part of the component. In order to test the effectiveness of these parts with participants, I gathered from my design stakeholders two differing design variations (Design A and Design B). For example, see how the titles for both contrast — Design A has a standard title, while Design B is a quote title.

To ensure easy insight organization and delivery, I started off by gathering existing hypotheses from stakeholders in a "traffic-light" denotation system.

Next, I met with Product Managers and Designers in a series of stakeholder interviews to understand the problem space more deeply. In addition to unpacking research questions, I collaborated with stakeholders to uncover their hypotheses concerning the component. By the end of this week, I had a list of hypotheses to test with participants!

Over a week, I conducted 60-minute interview sessions with 8 HBO Max subscribers. These sessions were structured in two back-to-back parts:

Part I: Blind Cognitive Walkthrough of Prototypes

‍Using screen-share, participants walked through a homepage prototype containing one of the two (prototype A or B) Recommended by Humans feature components. These two prototypes differed in the design variations of individual component elements, allowing for an evaluation of the efficacy of these individual elements.

Part II: A Build-Your-Own Feature Component Activity

‍The second half of this study let participants provide feedback in a fun and engaging way, where they were able to take on their “designer” cap and tinker around with the prototypes themselves. Using a Miro board I had created, participants built their own idealized Recommended by Humans feature component by choosing from multiple variations of the elements (Images, Titles, CTAs, etc.). They dragged and dropped on Miro, explaining their design decisions, while I followed along and probed into why selections were or were not made.

On the last day of my internship, I presented a detailed report of findings to the team. Here’s a sample; please reach out for further insights.

Please reach out to cynthia_chen@berkeley.edu for more insights.

SAMPLE INSIGHT

Being shown an image of the recommender in the component increases trust in the authenticity of a recommendation, resulting in an increased value proposition.

Having an image of the recommender allows the recommendation feel more personal, authentic, and reinforces the purpose of the Recommended by Humans as a human-powered recommendation system. Design variations of the feature component with an image of the recommender were received more positively than those without, as participants cited that they were given more insight into the recommender, overall bringing a higher value proposition and increased trust in the recommendation.