Goodreads Redesign: Evaluating Behavioral Change
Redesign an existing product that can positively shape people’s behaviors while its effectiveness is proven by a real behavioral experiment which includes quantitative data and qualitative findings.
It was a 3-week project (2018 Fall) in the Behaviors course taught by Haakon Faste. It was an individual project but with some collaboration in the experiment with Jay Wang and Ruitao Liang.
Keywords: Behavioral Experiment, Qualitative & Quantitative Research, Ideation, Prototyping
Even though the current Goodreads reading challenge allows people to read more, it also:
Worsens the reading experience while focusing on the quantity over quality
Fails to help people develop regular reading habits
Creates a negative competitive environment
Redesign the Goodreads reading challenge experience by:
Evaluating outcomes through people’s reading frequency and time, not by amount of pages and books
Using line graphs to track people’s reading frequency and time regularly, not by using progress bars to track the number of books
Only letting people know the average reading time of the public, not by competing with individuals who they know in real life
Set up goal in terms of frequency , duration, and time slot
Reminders will show up before their reading time slot starts
Pick the book that is in the “currently reading” book shelf and start its timer while reading and your data will automatically save to your progress.
You will see your summary of progress, line graphs that show your daily progress with competitive analysis of your community, which you can choose on your own.
As one of the behaviors I wish to change myself is to read more for leisure, I did some research on the current reading related apps and found out that Goodreads was the only one that provides online book communities to encourage people to read more, allowing readers to track their reading progress and set up their reading challenges.
The current Goodreads reading challenge uses communities’ feeds, progress bars, and competition to motivate people to read more. To understand why it uses those elements, I did some academic research. The result proved that the elements that the current reading challenge uses are effective—visual feedback, connections, and competition can motivate people to read more.
However, as I tried to use Goodreads to track my reading activity, I found the ways that Goodreads evaluates people’s reading outcomes weren’t ideal. Evaluating people’s reading goals by the amount of pages and books they read encourages people to read more but also causes a few problems:
It may worsen the reading experience. Everyone has a different reading pace, preference, and life schedule. When the only way to evaluate the result is from pages and books, people may just focus on the quantity over quality to accomplish their goals.
It doesn’t help people develop regular reading habits. While it may help people read more occasionally, it doesn’t make people more consistent readers in the long run.
It may create a negative competitive environment. While competition will increase people’s motivation to read, competing with people who you know in real life might create social pressure and judgements.
Hence, rather than:
How might we encourage people to read more?
The real challenge is:
How might we encourage people to read more frequently by helping them to develop reading habits without sacrificing their reading experience and creating negative social pressure?
We turned our redesign ideas into an experiment.
In order to solve the current Goodreads problem, we came up with a few solutions to solve the problems:
Maintain reading experience by evaluating outcomes through people’s reading frequency and time since we believe having habits is more important than achieving certain quantities.
Develop regular reading habits by using line graphs to track people’s reading frequency and time everyday so people can see their progress from time to time.
Lessen negative competitive environment by only letting people know the average reading time of the public.
By sending participants (people who have leisure reading habits or are inclined to read more for leisure) visual line graphs of their reading progress and competitive analysis of all participants’ average reading time everyday, they will have a higher chance to read more frequently.
We turned our hypothesis into a 12-day experiment. We turned our hypothesis into a 12-day experiment. Before the experiment started, we invited people to fill out our survey and successfully recruited 28 participants who have leisure reading habits.
In the first week, participants self report their reading time everyday.
Based on their reading frequency, we distributed them into 3 groups—control group, testing group 1, and testing group 2.
In the second week, each group was assigned different nudges :
Control group only need to report their reading time
Test group 1 report their reading time and receive a line graph everyday that document their reading progress
Test group 2 report their reading time and receive a line graph everyday that document their reading progress and all participants’ average reading time
At the end of the experiment, we did surveys and interviews to synthesize results.
To evaluate participants’ reading frequency, we developed a formula to calculate their reading frequency rate (RFR).
We got interesting results since we not only successfully saw different results in different groups, but also saw a contradiction between what participants thought and what they actually did.
While the participants in the control group thought they read more frequently, the results showed that their Reading frequency rate (RFR) remained the same.
Testing group 1
While the participants in testing group 1 thought they read more frequently or at least remained the same, the results showed that their Reading frequency Rate (RFR) decreased.
Testing group 2
The participants in testing group 2 thought they read more frequently, which matched the results—their Reading frequency Rate (RFR) increased.
By comparing all the groups at once, we could see that the experiment proved our hypothesis.
As the results proved our hypothesis, I redesigned the Goodreads reading challenge experience by translating our experimental methods into 4 main features:
Reading goal: Because the experiment’s participants wrote down their goals on our survey before the experiment, users of the app will set up their goal in terms of frequency, duration, and time.
Reminder notification: Because we manually text or email participants everyday to ask for their reading time in the experiment, app users will set up reminders which show up before their reading time starts.
Record reading: Because we asked participants to record their reading time by themselves in the experiment, app users can pick the book that is in their “currently reading” book shelf and start its timer while reading and their data will automatically save to their progress.
Track progress: Because we used line graphs to show participants’ daily progress with competitive analysis in the experiment, app users will see the same thing. Additionally, they can see their overall summary of progress as well as their communities’, which they can choose by themselves.
What is the next step for Goodreads redesign?
The qualitative feedback we got from surveys and interviews, in addition to the quantitative data we got from the experiment, raised more design challenges and opportunities regarding people’s reading behaviors. Besides redesigning the Goodreads app, there is potential to do more research to design another solution.
Qualitative data and quantitative data are equally important
Humans behaviors are complex. As a designer, it is such a privilege to have the opportunity to not only understand cognitive psychology perspectives, but also design an experiment to observe human behaviors.
This was the first design project I did where it’s research is beyond qualitative interviews. By actually designing the experiment and seeing the quantitive results prove our hypothesis, it made me realize how impactful designers can be with their design products. I am happy to witness how my design changes behaviors, and to be aware of the professional power I have, which must be used carefully. While the quantitive data that reflects people’s behaviors is often considered as more objective and trustworthy, the qualitative feedback that reflects people’s feelings and thoughts are just as important as the quantitive ones. Quantitive data is the result, while qualitative data is the process. When two data points are conflicted, I hope we can all listen to qualitative ones first. While trying to change people’s behaviors, we shouldn’t forget what matters the most is our humanity — needs, struggles, feelings, and thoughts.
Under the current political and social climate that highly values tech cultures, there is no doubt that design has a significant power to influence or even manipulate people’s behaviors. Hopefully, every designer and company will use their power wisely both in practical and ethical perspectives.