WeightWatchers

Driving a 10% cLTV increase with a simpler, more personalized cancellation experience.

While managing the growth design team at WeightWatchers, I led the effort to reduce churn and increase retention through a novel, AI-supported, and greatly simplified, cancellation experience.

Overview

Our churn rate was increasing as users signed up for longer plan lengths and our cancellation experience lacked effective ways to retain people once they’d entered the funnel.

As part of the growth design team’s rapid experimentation efforts, we shortened and simplified our cancellation funnel while also leveraging AI for churn prevention, with dramatic results.

ROLES:
Design manager

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

CROSS-FUNCTIONAL TEAMS:
Product
Customer Support
Engineering
AI Engineering
Data Science
Rapid Experimentation

TIMEFRAME
6 weeks, Q3 2024

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

A large percentage of members were cancelling their membership immediately after signing-up and immediately after their plan was set to auto-renew at a higher monthly price. Churn rates peaked at up to 40% when a member’s initial 3 or 6-month long-term-commitment (LTC) ended, especially within the first month post-LTC.

While we couldn’t control subscription lengths, we needed to incentivize users to stick with their plan, accept retention offers at higher rates, or re-engage with the product in a meaningful way via the cancellation funnel.

Results & Impact

By halving the number of steps in the cancellation flow, presenting users with reminders of features they had used and others they had missed, and presenting AI-generated, highly-personalized retention offers we were able to achieve a 300% increase in retained customer revenue, resulting in a 10% increase in LTV while driving increased product usage immediately after saving users.

PROCESS

Research & Discovery

Cancel Reason Data Analysis

We reviewed months and years worth of data on cancellation trends across different memberships as well as save offers and tactics which had previously reduced churn. We also reviewed countless surveys and user interviews on cancellation reasons, giving insights into the emotional and psychological factors contributing to churn.

30%

Financial reasons
30% of users choose ‘financial reasons‘ when asked in the cancellation flow. Users reported that WW was “out of their budget“ and they couldn’t justify the cost because they’d fallen off their program.

45%

Churn post-recur bill (RB)
45% churn rate for people who were transitioning from LTC to RB. This number had been increasing as the business has drove more LTC subscriptions, indicating users would stay for the promotional price they’d signed up with.

18%

Save offer acceptance rate
18% of users accepted a 30-day free time credit when offer was presented in the cancellation experience but 80% of those who enter the cancel funnel were “strugglers“ and not using the product, indicating that they wanted to stay but needed to re-engage in their weight-loss program and the product.

Competitor Research

Analyzing competitors provided valuable insights into best practices and common patterns. Some competitors were clearly optimizing for re-subscription vs churn prevention but our biggest opportunity for immediate impact was to shorten the flow and focus on churn prevention.

HelloFresh

Strengths:

  • Asks questions that clearly reflect user research

  • Offers are compelling and attractive

Weaknesses:

  • 8+ steps depending on reasons

  • Long scrolling screens with high cognitive load and too many options

Headspace

Strengths:

  • Appealing visual design

  • Value props restated (FOMO)

Weaknesses:

  • Requires written response after selecting cancel reason

  • Does not have offer monetary incentive

Noom

Strengths:

  • 3 steps to cancel

  • 1 save offer at last step

Weaknesses:

  • Feels impersonal

  • Asks only one cancel reason question

User Flow Analysis

This flowchart illustrates the steps a user takes during the cancellation process, from initial log in to cancellation confirmation or membership retention. We knew the highest number of users selected "“financial reasons” when cancelling, meaning the risk of removing some survey questions was likely low.

Reduce steps and shorten flow.

Analyzing the shortest path to cancel prevention

This screen-flow shows the existing design and top-3 cancel reasons and only save offer. This flow and save offer (2 months free) had a 9% acceptance rate. We believed we could increase retention offer acceptance by highlighting key features based on users in-product behavior or features they hadn’t used which could help them get back on track and exit the cancellation funnel.

Design exploration

EXPLORATION ONE

Cancel survey & feature/benefit emphasis and AI feature offers

Pros

  • Mixing user-centered cancel reasons with AI-generated offers.

  • Reminds users of and product value/features they may not use.

  • Does not rely only on monetary offer.

Cons

  • May redirect users out of cancel flow and cause frustration

  • May add steps lengthen user flow

EXPLORATION TWO

Cancel survey with branching & AI generated offer as last step

Pros

  • Engages users through a more personalized cancel flow.

  • Creates more opportunities for various save offers and tactics

Cons

  • Requires more development effort.

  • More complex and design-intensive, high-risk for an MVP experiment

EXPLORATION THREE

No survey, only AI-generated save offers

Pros

  • Quick and straightforward, reducing speed to testing AI offers.

  • Targets 30% of users cancelling for financial reasons.

Cons

  • Overly simplistic and not user-centered

  • Sacrifices learnings from self-service cancel surveys

  • Mostly designed to test the AI bandit.

Selected variant

The cancellation reasons survey and offer flows were chosen for optimization because these interactions had the highest potential for retention success versus focusing on switch plan, pause or downgrade options as part of the MVP. In addition to the design team’s UX solutions, we also determined how best to leverage AI, train the AI bandit and use in-product behavior attributes to offer timely discounts, recommendations, or extensions that might appeal to members.

Despite some lively debate, we ultimately chose the 3rd option, removing all cancel survey questions and immediately presenting users with a feature benefit highlight (based on their past in-product behavior) and then a final AI-determined discount offer. The intention here was to support the Loss Aversion hypothesis, while recognizing users were attempting to avoid the ramp-up price after the end of their existing subscription.

Design Solution

Subscription details and feature list

Step 1 of the new cancel flow simply starts by displaying the user’s active subscription and the features and tools of their program. Not shown here are the landing pages and log-in loops which we eliminated from the new experience with help from engineering. We reduced the number of screens a user would move through before reaching step 1 of the cancel flow, reducing frustration, and exposed subscription details which had previously been hidden, building trust and transparency.

Loss aversion and exit levers

Based on a user’s engagement status, past in-product feature use and the growing sophistication of the AI bandit (which would be learning through the test), we chose two feature highlights to encourage people to re-explore the app and exit the cancel funnel.

AI-selected save offers

For users with high-intent to cancel, low engagement status and minimal app feature adoption, discount offers were presented first, optimized for plan extensions and long-term commitments at a lower monthly price with higher potential impact on LTV.

Results

Initial results after 30-day test delivered a dramatic reduction in Cancel Conversion Rate, an increase in Save Offer Acceptance and an increase in Membership Revenue Impact and Membership Duration Extension.

~ 300%

Retained Customer Revenue

Positive impact across all success metrics indicated an early estimate of 200% increase in retained customer value which, by the end of the test period, jumped to an estimated 300% in retained customer value.

+ $4M

Annual Revenue

Variant 3 ultimately added several million in annual revenue with the AI-driven offers and feature highlight redirects deemed a huge success.

~ 10%

Lift in LTV

Variant 3 ultimately added several million in annual revenue with the AI-driven offers and feature highlight redirects deemed a huge success.

Takeaways

Diagnosing with data: The vast quantity of funnel data and in-product user behavior analytics allowed us to clearly determine the opportunities to improve the performance of the cancellation experience and quickly develop design iterations and variants to test. Qualitative data from user interviews and cancel surveys helped us validate our assumptions and identify user attributes to help train the AI bandit and make it more effective.

Disagreeing and committing: Although the design team were supportive of a more personalized user survey in combination with AI driven offers, the product team’s preferred option to better evaluate the potential for AI was still a success and delivered the intended results and a new experience for further experimentation on.