About 2.0
My experiment turned static, ineffective content into a relevant rotating engine
What’s the project? A feature, similar to Instagram’s Notes, helps users quickly share availability or lightweight thoughts using a text + emoji format.
GTM campaign
Problem
Product problem:
The About feature was buried 2-3 taps deep in Settings, had no visibility where conversations actually happen.
Content problem:
Users did not understand what the feature was. The entry point text did not educate or incentivize use, it was impersonal and static.
A long list of presets caused cognitive overload and had no alignment to UXR-identified use cases.
TLDR:
Low awareness + adoption
Low feature comprehension
No content strategy to educate new users, re-engage existing ones, & keep the feature feeling in the moment
Solution + My Role
A lightweight emoji + text format with duration and surfaced About as a thought bubble in 1:1 chat.
Content: I created a rotating prompt engine for the entry point content & a universal preset list that spoke to primary use cases.
My Role:
Helped define user problem and design solutions
e2e content strategy + experimentation
Helped shape product & GTM positioning + naming
Collaborated on UXR planning
Established scalable logging
Impact
+.17% top-line metric
Exceeded adoption goals by 45% and retention by 50%
100% positive user sentiment
Before
After
Constraints
UXR showed that users lacked feature comprehension: “I don’t know what this feature is for?”
Users felt insecure or unsure of what to post for their About.
The feature needed clear distinction from WhatsApp’s other feature, ‘Status,’ which is a visual, broadcast, creation-heavy tool.
Universal — reach 180+ countries which varied significantly on comfortability with ‘expression’
My goal
Content was a primary lever in the usage of this feature. It needed to meet:
User need: feature education, use case (availability-expression), easy to use
Business need: Feature adoption, make About an everyday posting tool, and ultimately drive conversation.
My Approach
Principles
I represented a content POV in our UXR planning to craft principles from our insights. It grounded the feature in our product goals and a provided a framework to lean on when content was challenged.
Voice, tone and terminology framework
Always coming from a place of systems-thinking, I created a framework to ensure content upheld the feature’s primary function of ‘lightweight availability/expression’ and had clear distinction from WhatsApp’s other feature ‘Status,’ a richer, more full creation experience tool.
My content experimentation that turned the content from static and impersonal to dynamic and tailored
How I got buy in
I communicated the value of content and how it was going to significantly help reach product goals of adoption and retention and solve UXR-identified problems of comprehension.
The only way to prove which direction moved those numbers was to test what actually resonated.
Goal
Build an adaptive content engine that creates a feeling of freshness, drive in-the-moment posting, reduce anxiety, and educate new users —all tied directly to moving top-line metrics:
Group Sends per DAU
Send Session @ 14 (retention)
I led XFN collaboration with DS and Eng to create a 4-arm country test experimenting with 4 thematic approaches. To induce freshness aligned with the strategy, a user would see a new prompt every time they opened the app.
In-the-moment: DOW, Time of Day
Why?
The 2026 product strategy required shifting About from a weekly to a near-daily habit.
Emoji + music
Why?
I pulled data from Instagram’s Notes previous experiments that showed emoji and music prompts drove creation wins.
User Journey
Why?
UXR showed new users experienced blank-canvas anxiety, while existing users needed freshness to re-engage.
Expression
Why?
I wanted to validate a range of use cases to understand which was satisfying user need the most.
Results
My choice to create a rotating content engine successfully drove feature adoption, retention and message sends.
Interestingly, user-journey prompts did not perform well indicating less need to educate through content and more through design.
I chose the top performing prompts from in-the-moment, and emoji/music that gave users a new prompt every time they opened the feature.
Key decision - Although expression performed well, I made the tradeoff to lean away from this and into capturing in-the-moment posting. We were struggling with retention more than adoption at this point in time so the product direction was to make About an everyday feature, not a weekly one.
My content became a marketing lever
Because the strategy drove measurable lifts in adoption and retention, I partnered with PMM to extend it into app-wide marketing moments like World Cup 2026.
Presets
After
Before
A system, not a copy refresh
Problem: The presets needed to reinforce user comprehension of how to use the feature i.e. flex across different use cases — from hard availability to lightweight expression.
The fix: A spectrum, not a list, with each one solving for a specific user problem and need.
Educate first-time users + reduce blank-canvas anxiety: For new users, presets model the right length, tone, and specificity — lowering blank-canvas anxiety of what to post.
Universal to scale to 3B users: I chose situations and feelings that are globally recognizable, evergreen, and age-agnostic. Emoji choices were informed by Meta’s most used emojis data.
Reinforce conversation loop: Users were more likely to continue usage of the feature if they got replies. I shifted "Busy" → "Slow to respond" and "Available" → "Free to chat" to reinforce a conversation loop. Every preset gives a contact a reason to reply.
Novelty kept it alive: Low-performing presets were rotated out; seasonal presets rotated in — so the feature didn't go stale after week one.
Explorations - A methodical approach to finding the right range, tone and use cases that aligned with user need and product goals
Reducing cognitive over load - cutting the list in half
The initial preset list was long, exhaustive and did not align with our new product goals/primary user needs. I cut the list by half.
Why?
Users were already coming to About 2.0 with NO clear mental model of what the feature was, so presenting them with a wall of options would compound that confusion.
Posting anxiety was a core barrier to adoption, a long list risks the opposite of its intention — instead of helping users pick something, it paralyzes them.
Outcome:
Adoption of presets went up by 22% after cutting the list
UXR showed it helped with user education on how to use the feature
Implemented logging = improved optimization
Previous presets had NO logging. I changed this. I ensured logging was implemented with DS for us to understand the user-value of these and which ones were not resonating with users.