Hulu

Hulu wanted to promote its Hulu with Live TV streaming service using our AI-powered ads. Our team concepted an ad that offers movie and film recommendations based off a user’s mood, who they plan on watching TV with, the time of day and sentiment analysis based on how users answered the randomized open-ended question. The open-ended questions gave Hulu insights on how consumers approached their TV watching. From there, we matched users’ moods with the appropriate content.

But there was a lot of work behind the scenes to make this happen.

For this project, I created the recommender flow, ran sentiment scoring on nearly 100 Hulu films and movies (so the bot could match the right content with the right moods) and created an FAQ corpus/flow based on Hulu’s website, which focused on general Q&A about Hulu’s streaming packages, how to sign up, etc. I also mapped out the types of genres associated with the time of day based on studies that found most people enjoy light entertainment earlier in the day and more deeper content as the day goes on.

Campagin results:

44M total impressions served
18K active user sessions
3.5K conversations
14K total button clicks with 3K clicks to hulu.com
3.7x more time spent in the unit vs. Google Rich Media interaction rate benchmark

In addition, the chatbot was able to provide users with an in-scope response to their query 99% of the time, leading to:

1.3x more activations
1.1x more conversations
1.1x higher active user sessions on The Weather Channel Mobile App
1.7x higher active user sessions on weather.com Desktop

Abbreviated sample flow

Start of conversation (above)

Open-ended questions (above)

The user was served one of three open-ended questions. Questions were randomized so users wouldn't get the same question back to back if they went through the experience again immediately after. Users also had the option of skipping the question entirely.

Available recommendations (above)

Conversation Flow In Unit

Sample FAQ Flow from User Question