Audible Content Discovery
Audible is an audiobook product by Amazon, with over 350,000 titles of premium audiobooks and spoken word content. It is the largest audiobook producer and retailer in the United States, providing high quality and a large variety of books to millions of users around the world.
Mission
After observing the data analytics and customer voice of existing product over a period of time, we identified opportunities/product gaps, then I started having conversations with the business stakeholders and subject matter experts to formulate a project plan.
Problem statement
There is a lack of engagement with product offerings and website functionalities, user couldn’t find what they want from our website.
After users arrived audible.com from search engine or amazon.com, data showed that a significant amount of the prospect users couldn’t find content that interest them and left the site. With user research and interviews, we found that users didn’t understand how did the membership work, within the member sign up, high percentage of the new members forgot about the free trial end date, some didn’t know how to choose and setup audiobooks on their devices, 1/3 of new members did not actually started the audible experience before they decided to cancel.
The search module is the most popular functionality used by Audible users across the customer journey, while data is showing a lack of engagement from search functionality, 98% of users left our site because they couldn’t find content that interest them, some of our existing customer were relying on Google Search to find an audiobook within Audible, instead of using Audible’s internal search feature, we know that there is a huge gap in our Search module.
Research
Existing Customer Journey
Conducted a holistic research for all Audible touch points, to see how user interact with the product from awareness to experiencing audiobooks to advocacy or leaving the experience. Both qualitative and quantitative research was conducted, including
Data Analysis
User Interviews
Customer Care Shadowing
Field Observations
A/B Testing (web lab)
The customer journey map was to communicate to stakeholders and project team members for what we observed throughout the research process, what worked well and didn't work well, as well as how customers respond to what we offered to them.
Customer care shadowing
Apart from analysing the data insight we got from Audible’s A/B testing environment, I scheduled a shadowing session at Audible Customer Care centre, listen to conversations between Audible users and our staffs, to better understand their pain points and problems they experience using the product, as well as knowing what customers need help with.
Ideation
Data insights showed that a large group of users left during the initial content discovery because they can't find books that interest them. The goal of the Content Discovery project is to design a new experience to help users find contents that they would love and engage with, to reduce the drop-off rates during discovery stage. We decided to have design exercise for brainstorming and collaborate with teams of designers, developers and product managers.
Collaborations
Users’ needs varies depending on the devices and platform they are using, and these service touch points construct the entire customer experience, I collaborate with designers and product managers who works on different platform, to share the insights we found, decisions made for different use cases, and update on each other about the design and interaction patterns, so that the experience will be consistent across devices and platform.
The new “search” experience
User flow
A user flow is created to communicate to team members from development, product, marketing and design, for us to understand the new user interaction flow, for the team to understand high level benefit and effort for the new experience.
Empty State
I introduced a search suggestion in the "empty state" of Audible's "Search module", to help user find the books that they were looking for.
Why?
Data analytics had showed that the top 150 search queries were book series and author, I designed the empty state so Audible can making suggestions based on the the top search queries we received from data analytics. Before user start searching, they can see the "last viewed" pages, the most searched queries and top performing pages, these components were to make the popular contents more accessible and assist users to get to the list of content that they are looking for.
Search suggestions
Once user put in a keyword, an auto-complete or predictive search term will appear to assist the user to go to the search results without completing the full keyword, suggestion cards will appear to help user go directly to the detail page. For instance, if a user typed in a book name that's part of a series, a suggestion card of the full series will appear.
Why?
The search engine did not know if a user is search for “business”, do they mean book titles that contains business, or are they looking for content in the business category.
In the search result, I placed the tabs at the top of the page, the user can easily navigate to the content that they intended to search, or continue the browse experience following components that are related to their search.
Why?
Data showed that every millisecond of latency increase drop-off rate, not seconds. We assume that reducing the time and effort that it takes for user to reach their desired content, or servicing content faster, can technically avoid user left our site, or save them time from opening other search engine to find our content.
If a user is searching for an author / series / book title / category / collection...
For example, if user search for an author, below is what they can see in the search result. They can see All, author, series, categories, book titles relevant to the searched author name.
If the user can't find what they want from the search result, I put in a module that will allow users to perform a natural language search, i.e. "I am looking for a book about mysteries and thrillers that are trending, narrated by multi-voices, to listen during my morning commute."
Why?
Usage pattern that I observed from data and A/B testing insights shows that around 40% of usage of our product is during users’ commute and 48% while doing household chores. Depending on the time of the day, and activities that they are on while listening, they would consume different types of audiobooks, either search or listen. Therefore I designed this module, so user can go specific about what they are looking for and when do they want to listen to the book.
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Search by voice
With the launch of Amazon Echo, there was an opportunity to integrate Audible to Alexa commands, I tried to incorporate voice and image search features to Audible's search module. For voice search, it is so that users can read a natural sentence to get a list of interesting content, image search is so that if use sees a book they want, they can quickly see if Audible offers that audiobook.
Why
Before designing the full spectrum of the functionality, I want to quickly mock up the feature, and test to see if it is desirable or needed by our customers, and what/how the design concept could be improved. This is tested together with Amazon echo.
Search by image
With the launch of Amazon’s product image search feature, we have an opportunity to integrate the same technology, which can take us one step further in helping user quickly search for the audiobook version of the physical books they see.
Why
We heard from lots of our loyal customers say, when they see a physical book that interest them, the first thing they do is to go online and see if there is a audiobook version of it. By servicing image search, user can just open the Audible camera, and know if we have the book to offer.
Search result redesign - Desktop
User testing
Apart from bringing our designs to the A/B testing environment, observing mass users' response in a large scale, I felt the need to learn more contextual insights around users through face to face conversations.
I led and worked with the researcher to come up with the testing strategy to learn more about how users perceived the existing content discovery flow (homepage > search & browse > collection > product detail page), and recruited users that had no prior experience with Audible. This would give us first-hand experience of how customers expect to connect with content, and potentially a fresh take on opportunities within the acquisition and content discovery phases of the journey.
Next steps
I presented the design to and research insights to stakeholders and cross-functional teams, some of the new design modules were implemented and currently being tested in the A/B testing environment, some were work in progress and some of them were put on the backlogs of the development teams.
Tools
Data analytics:
Amazon Feature Analyzer
Amazon Journey Analyzer
Adobe Analytics
Design:
Invision
Sketch
Axure