Solving the Check-In Problem

Overview

My Role: Lead Product Designer | Team: Product Manager, Engineering Lead, Data Team

The Challenge

Gympass users consistently failed to check into the app when attending gym sessions, creating a cascade of problems: partners couldn't verify attendance, retroactive payments became necessary, and the user experience felt broken. Despite paying for convenient gym access, users weren't engaging with the core validation system.

  • Business Impact: Fraud detection issues, inaccurate gym payments, operational overhead
  • User Impact: Frustrated users forgetting to check-in or leaving phones at home
  • The question: If users expect to use an app for paid services, why were they avoiding check-in?

Discovery & Research

Understanding the User Journey

I built Service Blueprints to document the complete user journey and identify points of friction. These blueprints mapped the overall data flow for end-user tasks, partner systems and responsibilities, and Gympass support processes. The steps taken before attending gyms began to reveal insights into why users might not be checking-in.


Key Discovery: The booking and validation integrations are independent processes, configured on a gym-by-gym basis to onboard gyms as quickly as possible. This created an inconsistent experience between gyms that users visit.

Mixed-Method Research Approach

Competitive Analysis: I compared existing services to understand how purchases are validated and how users get authorization to use services they've already purchased. This helped categorize different validation methods and identify where Gympass sat in the landscape.

Quantitative Analysis: Working with our data team, I analyzed Tableau dashboards to understand check-in patterns across different variables:

  • Check-in rates by business type (gym chains vs. independent)
  • Geographic patterns across countries
  • User type behaviors (new vs. returning members)
  • Drop-off points in the booking-to-check-in flow

Data synthesis was done in Excel with regular stakeholder check-ins to validate findings.


Qualitative Insights: User interviews revealed two primary behavioral patterns:

  • Forgetfulness: Users simply forgot to check-in once at the gym
  • Friction: Users intentionally left phones at home during workouts

Partner Feedback: Gym staff provided ground-truth perspective on operational challenges

Key Findings

Research revealed that user behavior wasn't the real problem—system inconsistency was. The service blueprint analysis uncovered the scope of integration complexity across our partner network.

The Core Problem

Analysis revealed that only 7% of gyms (3,500 of 52,000) had integrated booking, and validation methods varied dramatically across partners. This created an inconsistent experience where users couldn't predict what would be required at each gym.

Root Cause Analysis

Retroactive payment data revealed the systemic issues driving check-in failures:

Key Issues Identified:

  • Partner confusion: Front desk staff unaware of validation processes
  • User friction: Users forgetting to check-in or leaving phones at home
  • Technical barriers: Connectivity issues and app login problems
  • Fraud prevention trade-offs: Easy-to-fake confirmation screens

Problem 2: Inconsistent Validation Systems

The competitive analysis revealed that Gympass had evolved into a complex hybrid system. Unlike other services that use single validation methods, Gympass users faced multiple scenarios validation methods:

  • Access Code (7,508 partners): Front desk manually validates a code, which counts as a "Gympass check-in"
  • Confirmation Screen: "Smart check-in" automatically validates users in the Gympass system, displaying a confirmation screen to show gym staff
  • Scan Code: Users see QR codes, barcodes, or fingerprint instructions with countdown timers requiring scanning within time limits
  • Name to Front Desk: Depending on gym integration, staff check users in via third-party systems, scan codes, or use Gympass manual validation

The Core Issue: Every Gympass user must check-in with the Gympass app AND use the gym's access control method—creating a mandatory dual-step process that no competitor required.

User impact: Users couldn't predict what would be required, leading to frustration and abandonment of the check-in process entirely.

The Solution Strategy

Rather than forcing users to adapt to inconsistent systems, I designed solutions that addressed the root causes identified in the service blueprint analysis.

Push Notifications for Booked Classes (Immediate)

Send targeted reminders to users with confirmed bookings to check-in when they arrive at the gym.

  • Impact: Addresses forgetfulness, the primary user behavior issue
  • Implementation: Leverages existing booking data and push notification infrastructure
  • Scalability: Works across all gym types regardless of integration level

Reactive Check-In (Medium-Term)

When users can't bring phones or face technical issues, front desk staff submit user ID in the gym portal. Users receive push notifications later to confirm their gym visit.

  • Impact: Solves connectivity issues and creates fraud prevention through delayed confirmation
  • Implementation: Requires gym portal access but builds on existing validation systems
  • Benefits: Reduces retroactive payments while maintaining user verification

Automatic Check-In (Long-Term)

Machine learning model analyzes user check-in history, motion data, and location patterns to predict and automate check-ins, cross-referenced with retroactive payment patterns for fraud detection.

  • Impact: Eliminates user friction while improving fraud prevention
  • Implementation: Advanced ML model with privacy considerations
  • Vision: Seamless experience that works invisibly in the background

Impact & Outcomes

Context

This project was significantly impacted by COVID-19 gym closures in early 2020. While I transitioned from Gympass before full implementation, the research framework and solution strategy informed the team's approach to check-in improvements as gyms reopened.

Design & Strategic Impact

  • Research Foundation: Established comprehensive baseline metrics across check-in rates by business type, country, and user segments
  • Solution Framework: Created prioritized roadmap from immediate fixes to long-term automation
  • Cross-functional Alignment: Unified PM, engineering, and data teams around user-centered approach to systemic problem

What I Learned

This project reinforced that user behavior problems often stem from system design inconsistencies. By focusing on the operational reality rather than trying to change user habits, we created solutions that worked for everyone in the ecosystem—users, partners, and the business.

Key Insights

  • Global data revealed macro patterns that weren't visible at the local level—country-specific gym cultures significantly impacted check-in behaviors
  • User behavior was rational given the system constraints—they weren't being difficult, they were adapting to inconsistent requirements
  • Partner operations varied more than expected across different gym types and regions

What I'd Do Differently

  • Bring surveys directly to gyms for real-time user feedback during their workout sessions
  • Prototype solutions faster with select gym partners before full rollout
  • Include gym staff in the design process earlier to understand operational constraints

Design Process Evolution

This project taught me the importance of looking beyond user behavior to understand the systems that shape that behavior. The most elegant solutions often come from addressing root causes rather than symptoms.

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