WeightWatchers

Driving a 20% conversion lift with an end-to-end redesign of the WeightWatchers’ health assessment

While managing the growth design team at WeightWatchers, I relished my role as a player/coach, often taking on large, strategic projects as an IC, including this one—redesiginng the health assessment and product recommendation experience.

Before

After

Overview

Around 20% of all sign-ups touched the WeightWatchers health assessment, which quizzed users on health history, mindset, weight-loss goals and challenges before recommending a specific weight-loss program, based on their needs.

Taking a test-and-learn approach, I led a complete UI/UX overhaul of the assessment, defining key success metrics for a 30-day test against the existing experience.

ROLES:
IC/Design manager

RESPONSIBILITIES:
User research
Data analysis
Growth & testing strategy
Prototyping & testing
UX/UI Design
UX Writing
Design System

CROSS-FUNCTIONAL TEAMS:
Product
Marketing
Engineering
Behaviour Science
Legal Team
Data Science
Rapid Experimentation

TIMEFRAME:
Q2 2022

About WeightWatchers:

For millions of people around the world, WeightWatchers is a household name and a tried and tested approach to weight-loss. With 3.7 million subscribers and an expanding product offering, WeightWatchers provides nutrition, behavior-change and medication-assisted weight-loss programs.

Problem

More than 20% of users go through our health assessment and product recommendation flow monthly, but 50% dropped off before completing it due to lack of personalization and poor engagement. We were losing many customers who were interested in our product but never got to use it.

Results & Impact

By improving the visual design, personalization, usability and building trust among our users I was able to dramatically improve the health assessment’s performance across all key success metrics.

TLDR: Our test variant won and drove a 1.5% increase in LTV, a 20% lift in conversions and an 86% completion rate.

PROCESS

Research & Discovery

Understanding the user

Starting out with research, I wanted to get a clear understanding of how users felt while progressing through the health assessment, what their pain-points, goals and needs were. When I research problems, I always draw insights from a wide variety if sources to supplement generative research from user interviews. I employed all research methodologies below.

User Interviews
Understanding pain-points, motivations & decision-making

Past Research Reviews
Leveraging existing data to avoid redundant efforts.

Customer Insight Surveys
Identifying user expectations and preferences.

Product Analytics
Analyzing drop-off points and user behavior.

Competitor Analysis
A Benchmarking against industry leaders for best practices.

Usability Testing
A Benchmarking against industry leaders for best practices.

User interview highlights

Lack of personalization leading to mistrust

Interviewing 10 users, 5 lapsed and 5 new, I learned that they didn’t feel the product recommendation was personalized and they didn’t trust why the recommendation was being made. They also didn’t want to give out their email address as part of the flow and they didn’t want to see so many content-heavy info screens and wanted to move through the flow faster while knowing how much time it was going to take them to finish and get their result.

Analytics highlights

Critical Drop-off Points


Funnel analysis reveals two significant drop-off points in the user flow:

1. Weight & Height Input (50% drop): The first input screen showed a concerning 50% drop-off rate, indicating potential issues with form design, privacy concerns, or unclear value proposition.

2. Email Collection (30% drop): The final step lost 30% of remaining users, suggesting hesitation to share contact information possibly due to insufficient perceived value.

Consistent Drop-off Pattern
Between these critical points, each step shows a consistent 20-24% drop rate, resulting in only 20% of users completing the entire quiz. This suggests a fundamental issue with either quiz length, perceived value, or user engagement.
Recommendations

Competitor Research

Analyzing competitors provided valuable insights into industry standards and opportunities for improvement.

Foodvisor

Strengths:

  • Quick & Efficient: Shorter quiz length reduces drop-off risk.

  • Visual & Engaging: Uses appealing images and simple interfaces.

  • Primes for Onboarding : Sets clear expectations and value from the start.

Weaknesses:

  • Limited Depth: Shorter quizzes may lack the nuance of a more comprehensive assessment.

  • Less Educational Content: Misses an opportunity to reinforce expertise through educational elements.

  • Lower Commitment: Users may feel less invested due to the quick completion time.

Strengths:

  • Visually Appealing: Clean design and friendly visuals .

  • Multiple Recommendations: Suggesting a range of products increases the chance of conversion.

  • Engaging & User-Friendly: Simple interactions and smooth transitions.

Care of

Weaknesses:

  • Decision Paralysis: Too many recommendations might overwhelm users.

  • Lacks Focus: Broad suggestions may reduce the perceived specificity of the advice.

  • Completion Time: Visual-heavy design may slow down quiz completion.

Strengths:

  • Deep Personalization: Allows for highly tailored recommendations.

  • Builds Credibility: Signals a thorough, science-backed approach.

  • Increases Commitment: Users feel invested after completing a long quiz.

Noom

Weaknesses:

  • High Drop-Off Risk: Length may deter users.

  • Overwhelming: Excessive questions increase cognitive load.

  • Slow Value Delivery: Delayed gratification can reduce engagement.

Design exploration

EXPLORATION ONE

Chat-based quiz experience

Pros

  • More dynamic and interactive than a static question per screen.

  • Allows for more contextual responses, where the next message can be based on previous answers.

  • May feel more like a personal conversation rather than filling out a survey (novelty value)

Cons

  • May slow completion time while users wait for chat responses

  • Risk of feeling artificial

  • May reduce user control and limit ability to change previous responses

Pros

  • More engaging and fun - Score tracking and instant feedback make the experience enjoyable which may improve completion rates

  • Encourages learning – Users gain valuable insights about nutrition, weight loss, and motivation, making them feel empowered.

Gamified quiz experience

EXPLORATION TWO

Cons

  • Risk of feeling less serious – If too playful, it may reduce trust for users expecting a science-backed recommendation.

  • Complex implementation – Requires more design/dev resources to integrate interactive elements effectively.

EXPLORATION THREE

Traditional health assessment quiz

Pros

  • Builds trust and credibility – A structured, data-driven approach feels professional and science-backed, making users more likely to trust the product recommendation..

  • Appeals to users looking for a more clinical, credibility-focused experience.

Cons

  • Less engaging – Standard question-and-answer format may feel too formal or boring, leading to lower completion rates compared to more interactive options.

  • Higher drop-off risk – If the quiz appears too long or requires detailed inputs upfront, users may abandon it before reaching the final recommendation.

Selected variant

In exploring ways to enhance the quiz experience, we considered three distinct design approaches, each aimed at maximizing engagement, personalization, and conversion while aligning with user expectations and behaviors.

While a gamified quiz could increase engagement and a chat-based quiz could create a conversational feel, Exploration Three, the traditional health assessment quiz provided the right balance of credibility, efficiency, and conversion impact, making it the best choice for guiding users toward the most effective product recommendation.

Validation testing

Before launching the initial live test, we gathered a last round of feedback from users to increase our confidence in the final variant and identify any final iterations needed. The results were overwhelmingly positive and we were ready to put it out in the wild.

Design Solution

We made significant improvements to the visual design and modernized the branding by implementing styles and components from our new design system. Read on for highlights of the new design and key design decisions. 

Before
After

Reducing early drop-off, improving completion rates

1. Progressive disclosure on input fields

  • Reduced cognitive load

  • Perceived as more conversational

  • Improved mobile usability

2. Progress bar with sections & progress lines

  • Gave users a visual roadmap

  • Kept users more engaged

  • Set expectations for time to completion

Increasing trust and credibility

1. Using real data to show common goals

  • Created a sense of belonging and normalizes the goal

  • Reduced doubt and increased commitment

  • Leveraged social proof for motivation

  • Increased emotional connection and sense of community

1. Using real data to show time-to-goal-weight

  • Set clear expectations and reduced uncertainty

  • Increased trust with data-backed predictions

  • Personalized the experience for deeper engagement

  • Created a sense of progression and motivation

  • Promoted internal triggers and emotional response

Delivering personalization

Showcasing personalized recipes and weight loss coach matches:

  • Demonstrated key app features: Highlighted the app's diet and meal planning capabilities, showing users the value of WeightWatchers' curated recipes that aligned with their dietary preferences.

  • Strategic feature teasing: Instead of pushing coaching aggressively, the quiz planted a seed of awareness, so when users later explored the app, they were already familiar with this value-added service.

  • Increase feature awareness: Introduced users to the coaching support feature, making it clear that personalized coaching was an option if they needed more guidance or accountability.

Results and success metrics

The redesign significantly improved user engagement and business outcomes:

  • The test drove a 20% increase in conversion rates

  • Reduced early drop-off by 45%

  • The click-through rate (CTR) to the signup page reached 74%.

  • Increased completion rate to 80% at launch and 86% post-launch