Read by JANE

USER RESEARCH

Before beginning user interviews we had many ideas about our would-be-user: a super-reader. Super-readers typically read about 10 or more books per year, however, this brought up several questions. All questions revolved around how they differ from average readers. With the idea that super-readers and average readers may differ in their shopping and reading habits, we wanted to explore both types of readers in user interviews to determine their habits and behaviors in book discovery, purchase, and overall what influences their decisions.

We were able to develop a number of insights from the common themes and trends, a few shown below:

• Although readers believe that book tastes are highly subjective, they value the opinions of their peers.

• Readers engage in research before reading or purchasing a book.

• Readers consult authoritative sources for book recommendations.

• Readers want to know what the popular books are but do not assume it means they’re good.

With the insights above we were able to form a persona, the primary representation of our user. Meet Maya, the Architect, New Yorker, and frustrated book lover.

To understand the issues Maya faces we mapped out her user journey. Through combining all of our interview notes and stories we found three common themes about a readers’ discovery process; passive, active, or a combination of both.

For Maya’s user journey we honed in on the final process of the combination of passive and active discovery. This enabled us to model the most common process as well as the most amount of problems a reader faces.

Maya’s journey goes from not knowing she was looking for a book, to receiving a recommendation from a friend, searching for a book that meets specific criteria and eventually landing back on the recommended book and purchasing it.

After mapping this out some clear areas of opportunity came to light for our client BookComb, a few shown below:

• Connect Maya to friend’s book recommendations

• Help Maya remain aware of books she wants to read

• Connect Maya to books based on her specific interests

• Provide Maya with curated recommendations

DESIGN

READ BY JANE

ENHANCED SEARCH

A search feature enhanced to find books aligned to specific sensibilities & interests.

RECOMMENDATION ENGINE

Book suggestions based on books you love.

TESTING

Before landing on JANE and a brand look, we had to complete a few rounds of usability testing to understand the ease of use of the home page and the two features. This was done in three rounds of testing, the first two in mid-fidelity and the last in high-fidelity. All tests were focused on our clients two main features, the filtered search and the book recommendation search, in order to validate or invalidate them.

TEST TAKEAWAYS:

• Users were confused by the names of each feature provided by the client

• Overall the home screen was well received

• Users were unclear on what to do during each task

• Users did not understand the categorization of the filters

• Users thought the book recommendation feature was too long of a process

TEST TAKEAWAYS:

• Users responded well to the new feature names; recommendation engine and enhanced search

• Users still find the enhanced search to be difficult

• Users expressed that they would not use the recommendation engine every time they look for a book due to the granularity level

• Users want to use the search bar in both tasks

LOOKING AHEAD

Although we updated a ton between rounds of testing there is still a lot that needs to be done. Before launching our client will need to engage in more testing, as well as consider adding features to her MVP product as we continuously heard that users would not want to use these features without being able to save their information to a personalized profile.