My role
Team
Timeline
Project overview
Product Introduction
Sleeper is building the leading platform to connect friends over sports.
Sleeper’s mission is to create a digital playground for sports fans (casuals and diehards) and their friends to hang out – on Sleeper, users can check scores, play games, chat, send memes to each other and engage in many other fun and interactive activities. Now they provide seasonal fantasy sport game and daily fantasy sport game.
DEFIning the challenge
Tackling User Drop-off
Our stakeholders approached us with a critical problem: our existing onboarding process was driving users away. The journey, described as too long and convoluted, led users who intended to explore our paid feature, Picks, to deflect toward the free-to-play options. The core objective was clear—reduce user drop-off while increasing the visibility and conversion potential of our Picks feature.
Stake Holder Prompt
Reduce first-time user drop-off and prevent user deflection from our paid features
Stakeholder Concerns & Business Imperatives
As we delved deeper into the issue, two distinct business challenges emerged:
Early stakeholder meetings highlighted a vague problem: Why were users abandoning the onboarding flow? Were they lost in unnecessary steps or overwhelmed? It was clear this issue hurt our conversion from free-to-play to Picks customers
We couldn't just streamline the onboarding flow. Legal discussions revealed key barriers—Picks faced restrictions in certain U.S. regions and for specific age groups, making location and age verification mandatory
The crux of the problem? We couldn’t reduce complexity without sacrificing compliance.
initial audit
Opportunities in the Existing Flow
Before proposing solutions, we undertook a comprehensive audit of the onboarding flow. Our goal was to trace every possible entry point for first-time users and dissect the journey step-by-step.
What we found confirmed the stakeholders’ concerns:
Multiple unnecessary diversion points: These diversions were unintentionally steering users away from the Picks feature, as free-to-play options were introduced too early.
Mandatory, but cumbersome steps: The age and location verification steps, although necessary, were being presented in a way that created significant friction. Users dropped off before even reaching the core features of the app.
existing flow
existing flow
preliminary research
Analyzing User Drop-Off Rates
Our first step was to analyze user behavior through available metrics to identify exactly where drop-offs occurred during the onboarding process. Collaborating closely with the analytics and research teams, we gathered multiple reports and visualized the user journey through a series of charts.
Through a series of data analyses and usability testing, we pinpointed two critical insights:
Step 1 to Step 2 Drop-off: The data showed that 16.9% of users dropped off within the first two steps. This was an early choke point, where the combination of legal disclosures and introductory screens overwhelmed the user.
Commitment Fatigue: Our research revealed a lack of early commitment from users, which compounded the drop-off issue. Once they faced friction early on, the incentive to complete onboarding dwindled.
Collaborative Insights
Informing Our Hypotheses
A close collaboration with product managers and engineers shaped our approach to addressing user retention. Through regular syncs and feedback loops, we collectively explored user motivations, onboarding friction points, and technical constraints, which informed our hypotheses
Addressing Commitment
We synthesized our findings into two hypotheses, both aimed at increasing user retention while staying compliant:
Personalization for Retention: We hypothesized that a more personalized onboarding experience could increase the user’s psychological commitment, encouraging them to stick with the process despite legal checks.
Window Shopping: Inspired by consumer behavior patterns in e-commerce, we explored the idea of allowing users to explore the app without signing up. By deferring the commitment, we could potentially lower initial friction and create a more enticing experience for users to engage with the app’s core features.
explorations
Personalization for Retention
We explored a more tailored onboarding experience by allowing users to select preferences, such as favorite sports or teams, to create a personalized journey. For free-to-play users, we ensured that Picks was not introduced prematurely, keeping their focus on features they were interested in. This approach helped maintain engagement while delaying the introduction of monetized features until users had invested time in the app, leading to a smoother experience.
Window Shopping
Our second approach aimed to reduce early friction by allowing users to complete their entire Picks journey without requiring a sign-up. Instead of prompting for signup at the start, we deferred this action until just before checkout, when users were already invested in the process. By letting users explore and interact with the app before committing, we increased engagement and reduced drop-offs, particularly at early stages of the onboarding flow.
Securing Stakeholder Buy-in
Aligning Strategy with Goals
After conducting our initial research and exploring both personalization and window shopping concepts, we presented our findings to the stakeholders. During this presentation, we demonstrated how the window shopping experience could streamline onboarding while improving retention by allowing users to explore the app before signing up. We emphasized how this approach would reduce drop-off, especially for first-time users hesitant to commit early.
Balancing Legal Constraints
One of the biggest challenges was integrating legal requirements without disrupting the flow. Certain states and age groups in the U.S. are legally restricted from participating in Picks, which made it necessary to include location and age verification early in the onboarding journey. This meant introducing additional steps before users could enter the window shopping experience.
For users who did not qualify for Picks based on their age or location, we designed an alternative flow that diverted them seamlessly to free-to-play fantasy games. This ensured they could still engage with the app, even if they weren't eligible for Picks.
At the same time, we needed to focus on converting free-to-play users to our flagship product. To do this, we designed incentives and marketing touchpoints during onboarding to nudge them toward Picks once they were eligible.
Key Design Decisions
Streamlining Mandatory Onboarding
To reduce user friction, we shortened the initial onboarding steps, making them feel virtually quicker. We condensed the process from 12 steps to 3 core actions, offering a success state after users completed these steps. This change created a perception of faster onboarding while ensuring all necessary steps were covered.
Incentives for Free-to-Play Users
For users entering through free-to-play entry points, we added coupon incentives that could be redeemed immediately. This not only rewarded users for completing onboarding but also increased the likelihood of them engaging with Picks later on.
Preventing Drop-Off for Non-Eligible Picks Users
For users who couldn't participate in Picks due to legal restrictions, we ensured they wouldn’t drop off by diverting them to free-to-play fantasy games. This diversion was designed to feel seamless, keeping the user engaged without causing frustration over eligibility.
Enhancing Window Shopping Awareness
For the window shopping experience, it was critical to maintain transparency. We added subtle notifications throughout the flow to inform users that they would need to sign up before checking out. This way, users were not blindsided by the signup prompt but were still able to explore the app’s core features before committing.
Retrospection
If I Had More Time
Given more time, I would have focused on further refining the segmentation of users based on their behavior in the app. By conducting deeper user testing, we could have tailored the onboarding experience even more for specific demographics, creating dynamic flows that adapted based on user interaction and preferences. Additionally, I would have liked to integrate A/B testing to better evaluate the performance of the personalization versus window shopping models, ensuring we had real-time data to guide future iterations.
Reflective Learnings
This project taught me the importance of balancing user experience with legal constraints while keeping business goals in mind. Navigating the complexities of onboarding while ensuring compliance and maintaining engagement challenged my ability to think holistically. The iterative feedback loop with stakeholders was crucial, as it underscored the value of cross-functional collaboration—particularly in aligning legal, product, and design priorities.
