Audibud
An AI chatbot designed to enhance the audiobook listening experience and increase user retention on Audible. It provides personalized recommendations, interactive summaries, and narration customization, making the overall Audible experience more intuitive and enjoyable.
Problem Statement
How might we retain Audible users by increasing their satisfaction and immersive experience on the app using AI?
Problem Background
Audible is the leading audiobook, podcast, and spoken word entertainment provider. In 2008, Amazon acquired it, helping to expand its reach by integrating it into Amazon’s ecosystem. The app has increased in popularity over the years due to its convenience, which allows people to enjoy books on the go, whether during commutes, workouts, or simply relaxing. Audible’s unique selling point is its extensive library of audiobooks, exclusive content, and user-focused listening experience. While users appreciate these advancements, some believe the subscription model and pricing do not justify the price. With other competitors such as Audiobooks.com, Hoopla, Spotify, etc., Audible needs to leverage more AI practices to enhance user experiences to remain competitive and retain users.
Research Insights
User Pain Points
The study employed a qualitative research approach, gathering insights through mostly virtual user interviews with a sample size of 8 people (6 women and 2 men). Participants were regular listeners of audiobooks at various frequencies. While the focus product is Audible, we wanted to discover the pain points of similar audiobook applications. The research questions asked were broad by asking about audiobook experiences generally. The sole use of interviews for this research was to gain deeper insights into the pain points of audiobook users and to explore AI advancements with contextual understanding in a time-efficient manner.
User Pain Points
The top pain points mentioned during research are as follows:
- Unnatural/monotone/lower-quality/inconsistent narration
- Lack of personalized recommendations
- Low-quality audio
- Doesn’t want to pay extra for audiobooks/Expensive Audiobook Services
Supporting Data
- 87% of the people we spoke to said they were unhappy with the narration of their audiobooks, stating it’s unnatural, monotone, low-quality, and inconsistent.
- 75% of audiobook users we interviewed felt a lack of personalized recommendations
- 37.5% of audiobook listeners interviewed felt the low-quality audio was a huge pain in the listening experience and would/could completely turn them off the book they were reading.
- 37% of the research participants did not think that the current audiobook experiences that they received from apps like Spotify or YouTube were worth paying for.
The following features gained the most interest from participants:
- 81.25% of participants seemed interested in AI-generated summaries and highlights/Personalized Summaries
- 75% wanted to have better personalized recommendations based on their preferences and niche interests.
- 75% stated that an ability to control narration style and voices/characters would be useful.
- 50% of listeners found value in a feature to highlight and revisit important sections/AI-powered note-taking and bookmarking
- 50% AI-powered audio and narration adjustments (tone, bass, pitch)
Feedback
- Quality of Narration Matters: Nearly 88% of users found the narration to be inconsistent or unnatural (monotone), emphasizing the need for customizable narration options, including AI-powered voice modulation and multilingual narration.
- Personalization Drives Engagement: A significant percentage of users (75%) expressed dissatisfaction with current recommendation systems, suggesting that improved AI-driven personalization may lead to higher engagement and retention.
- Cost vs. Value Perception: Subscription hesitancy (37.5%) suggests that users find the current pricing structure unappealing, especially when compared to free alternatives such as YouTube or library apps. This serves as evidence that AI-generated value-added features like personalized content summaries and hands-free note-taking, which 81.25% of participants felt would be an important AI feature, could help justify the cost.
- Low-quality Audio: Users expressed a main pain point being a lack of quality in audio, which diminishes the experience. (37.5%). AI-driven immersive experiences could address this gap.
Landing on the Solution
Based on our target users’ pain points, we knew we wanted to work on the following features:
- AI-Driven Recommendations to ensure personalized book discovery, eliminating the frustration of generic suggestions.
- Narration Customization & Immersive Soundscapes to give users control over their listening experience, making audiobooks more engaging.
- AI-Generated Summaries & Learning Tools to help users retain key insights and enhance their learning efficiency.
Explanation of Solution
Through user research and feedback, we confirmed that personalization, efficiency, and engagement were key priorities for audiobook listeners. Users expressed a strong desire for AI-driven recommendations that better aligned with their interests, as well as tools to help them retain key takeaways more effectively. The ability to customize narration styles and integrate immersive soundscapes resonated with those seeking a more engaging and flexible listening experience. Additionally, authors and publishers showed enthusiasm for real-time engagement insights, recognizing the potential to refine content strategies based on listener behavior. These insights have guided our development priorities as we move toward building and testing our prototype.
User Flows/Mockups (Optional but recommended)
Future Steps
While most feedback centered on personalization, navigation, and cost concerns, some unique perspectives emerged such as language barriers. Customers expressed interest in AI-powered translation. This could be a possible additional feature to implement in the future.
Learnings
Product Manager Learnings:
Khadijah Banfield
- This Co. Lab experience was really rewarding! I learned a lot about how to shift my thinking as a PM when it comes to implementing AI.
- I learned a lot of new AI tools to facilitate some of the more time-consuming aspects of product management.
- One major key takeaway was discovering how an AI feature can truly enhance the experience for the user, and to use this as the guiding star always, with data as the foundation.
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.