GrammarlyGO
- Problem Framing
- Research process
- Insight Analysis
To be very transparent, this is a mock case study. The purpose of this case study is to showcase my process from the ground up. The main highlights are how I frame a problem, tie it to business outcomes, my entire research process, and how I synthesize my findings and turn them into insights and actionable solutions.
This case study does not include visual mockups of the solutions as I have not actually worked with this product. However, I am very comfortable with technical design aspect's and design tools in order to develop and deliver solutions. If you'd like to see more design and developer heavy work I've done, please go to jakecochran.com
Framework:Double Diamond
Prompt
Improving GrammarlyGO Retention: Turning One-Time AI Users into Loyal Daily Writers
GrammarlyGO is Grammarly’s embedded AI assistant designed to speed up content creation and help users write more confidently across platforms. It can draft email replies, rewrite sentences for tone or clarity, or brainstorm content ideas using preset prompts or custom instructions.
Despite high brand trust and broad distribution, GrammarlyGO suffers from low re-engagement:
- 60% of users try GrammarlyGO once but do not return in the next 7 days.
- Premium users show only slightly higher engagement than free users.
- Feedback suggests confusion about what the AI assistant does differently from standard Grammarly corrections.
My Role
- Why new users disengage after first use
- What’s missing in the current onboarding or interaction model
- What keeps some users returning and others not
- What changes could make GrammarlyGO feel essential rather than optional
Business Goals
- Improve GrammarlyGO retention by 25% within 6 months
- Increase AI prompt usage per active user
- Reduce user confusion between GrammarlyGO and core Grammarly features
Discover
Frame the Problem
60% of users disengage after first use. This indicates that GrammarlyGO either:
1. Has too much friction to be worth using
2. Isn’t perceived as useful in the first place
Result:
A 60% disengagement rate means major revenue loss via reduced Customer Acquisition Cost (CAC) efficiency and Lifetime Value (LTV).
Cost Breakdown
Expense
Spend
CAC = $3,000,000 ÷ 250,000 = $12 per user
Lifetime Value (LTV)
Premium Subscription: $12/month = $144/year
Avg. Subscription Duration: 1.5 years
Gross Revenue Per User: $216
Profit Margin (Post-Ops): 80%
LTV = $216 × 0.8 = $172.80
Cost of Disengagement
If a user tries GrammarlyGO once but doesn’t engage:
- They’re less likely to renew
- Their average lifetime drops to 1 year
- New revenue = $144 * 0.8 = $115.20 LTV
Engaged vs. Disengaged Users
Engaged User
Disengaged User
Result: A disengaged user generates 36% less profit.
At scale, if 100,000 users disengage after first use, Grammarly loses over $5.7 million in potential long-term value.
Premium users only displaying slightly higher engagement brings the same issues, there is either too much friction for them to use GrammarlyGO frequently, or the premium features are not useful enough to warrant using. This means lost money through the same means above.
Finally, users are not able to differentiate GrammarlyGO from classic Grammarly, this means wasted production cost of features, and losing the AI competition with other companies because Grammarly cannot develop a useful AI product.
Identifying Priority Segments
60% of users researched should be those that have used GrammarlyGO once and then disengaged. 20% Should be power users, users that have used GrammarlyGO for 3 months minimum, and use it daily. The last 20% Should be users that have unsubscribed from GrammarlyGO, and ideally have switched to another competitor.
It’s important that all users should be familiar with classic Grammarly, and are in an environment where GrammarlyGO is intended to be most useful (the user types a lot through the day). This is so we can isolate the problem to GrammarlyGO, instead of receiving feedback irrelevant to the problem.
Possible Hypothesis
- I suspect GrammarlyGO is too similar to Grammarly, users do not see a difference between the two and do not see a reason to engage with GrammarlyGO.
- I suspect that GrammarlyGO has too much user friction, the process of using the features takes too long for the user to use it seamlessly.
- I suspect that GrammarlyGO’s features are not useful enough, the user would rather opt to keep their writing as is, or take the time to revise it themselves.
Define
Methods Selected
Method
Type
Goal
Recruitment Plan
Segment
%
Description
Sourcing
- Internal CRM data (usage tracking via product analytics)
- Email outreach + incentives ($25 gift card)
- For surveys: Random sample triggered in-product after first use
Conducted Research
1. Product Analytics Review (Amplitude)
Step
Completion Rate
Behavior Segments:
- Users using "Shorten" or "Rewrite Tone" features were 70% of returners
- “Professional tone” was selected 3× more than other tones
- 33% of first-time users exited before selecting a writing mode
2. Unmoderated Usability Testing (Maze)
Scenario Tasks:
- Rewrite a Slack message to sound more confident
- Summarize a paragraph to be more concise
- Brainstorm talking points for an email
Key Observations (15 participants):
- 9/15 didn’t notice the GrammarlyGO icon was different from classic Grammarly
- 7/15 clicked the standard Grammarly "correct" button instead of the AI rewrite tool
- 10/15 completed the task but took longer than expected (avg. 2.4 minutes/task)
- 5/15 expected a full-chat interface like ChatGPT
- 4/15 were unsure if their tone setting had any real effect
Quotes:
- “I thought this would be a chatbot like ChatGPT.”
- “I wasn’t sure if it actually changed the tone or just fixed grammar.”
- “Why do I have to re-highlight every time? That’s annoying.”
3. User Interviews (10 participants: 6 disengaged, 2 power, 2 churned)
Themes from Semi-Structured Questions:
- What were you hoping GrammarlyGo would help with?
- What was confusing or frustrating about your last use?
- When do you choose to use GrammarlyGO instead of classic Grammarly?
- What would make this worth using more often?
Highlights
- Expectation mismatch: 7/10 thought GrammarlyGO was a chatbot experience
- Usefulness gap: 5/10 didn’t feel the output added much value beyond their own edits
- Frustration with UI: 6/10 found the controls non-intuitive
- Power users appreciated tone control and speed, but set up their own shortcuts
- Churned users switched to ChatGPT for more flexible responses
Quotes:
- “It felt like Grammarly but slightly smarter, not something I’d pay extra for.”
- “I’d use it more if I didn’t have to dig around to find it.”
- “I wanted suggestions, not full rewrites that sound robotic.”
4. In-Product Intercept Survey (Hotjar | n = 342 responses)
Q1: What were you trying to do with GrammarlyGO?
- Speed up writing: 45%
- Improve tone: 30%
- Brainstorm: 17%
- Not sure / exploring: 8%
Q2: Did it help you accomplish your goal?
- Yes: 19%
- Partially: 42%
- No: 39%
Q3 (Open-ended, sampled):
- “I couldn’t tell the difference between GrammarlyGO and normal Grammarly.”
- “It was hard to find tone controls.”
- “I wanted suggestions, not full rewrites that sound robotic.”
Synthesize Findings: Insight Clusters
Theme
Supporting Evidence
What it Means
Prioritized Problem Areas
Problem Area
Priority
Rationale
1. Clarify Value Prop of GrammarlyGO vs. Classic Grammarly
Bottom Line Impact
- Reduces churn after first use → protects LTV
- Increases adoption of GrammarlyGO among paying users → drives usage-based retention
- Increases adoption of GrammarlyGO among paying users → drives usage-based retention
- Boosts conversion from free to Premium, if GrammarlyGO is perceived as a clear differentiator
If 60% of first-time users don’t understand the difference, they see no reason to stay or upgrade, this tanks ROI on AI investments and inflates CAC per retained user.
2. Improve Discoverability in Product UI
Bottom Line Impact
- Increases repeat usage → key LTV driver (returning users stay longer)
- Reduces support costs → fewer users get lost or confused
- Drives feature stickiness → improves engagement scores used in renewal models
- Amplifies freemium funnel performance → more value in early days = more upgrades
If users don’t know where GrammarlyGO lives or how to trigger it, they won’t use it again — and every dollar spent acquiring them becomes less efficient.
3. Add Lightweight Editing Tools (Undo, Rephrase, Tone Presets)
Bottom Line Impact
- Builds trust in AI output → increases usage frequency
- Reduces task abandonment → improves session quality
- Differentiates GrammarlyGO from competitors → improves retention in a crowded AI market
Lack of user control breaks trust. When users don’t feel they can shape the output, they stop relying on the tool, which shrinks active user base and weakens LTV.
4. Personalize Onboarding Based on User Goal (speed, tone, brainstorm)
Bottom Line Impact
- Increases activation rate → users reach their “aha” moment faster
- Improves first-week retention → strongest predictor of long-term LTV
- Reduces cognitive friction → boosts satisfaction (CSAT), especially among new users
Users who get immediate value are more likely to return and upgrade, increasing organic growth and decreasing CAC per active user.
Develop
Solutions (No Mockups)
1. Contextual GrammarlyGO Activation
Instead of onboarding screens, trigger GrammarlyGO at moments of struggle.
- Use ML to detect hesitation, backspacing, or long idle time.
- Offer GrammarlyGO with contextual suggestions based on what the user is writing.
Why it's better:
- Activated when it’s needed, not before. Respects user flow.
- Ties AI to solving an immediate pain point, which builds trust.
Metric to track:
- GrammarlyGO activation rate during "writer’s block" moments
2. Live Preview Panel with Transparent Output Logic
Show GrammarlyGO's draft evolving in real time with a “why we chose this” explanation.
- Let users preview multiple tones or structures without rerunning prompts.
- Display output as a “guided draft,” not a black-box result.
Why it's better:
- Increases transparency and control.
- Mimics how tools like GitHub Copilot and ChatGPT let users feel more involved.
Metric to track:
- Reduction in AI output abandonment
- Increase in “output edited and used” rate
3. Mini AI Editor Mode
Instead of just rewriting, offer an optional GrammarlyGO side editor.
- Users can write a rough draft, toggle a GrammarlyGO editor view, and see enhanced versions or paragraph-level suggestions.
Why it's better:
- Gives GrammarlyGO a defined space rather than injecting into main editor.
- Helps with long-form content where users want help structuring or revising.
Metric to track:
- Session length with AI Editor open
- Feature satisfaction score
4. "Start with AI" Smart Templates
Offer AI-first document templates for common use cases.
- Email, outreach, follow-up, project summary
- Prompted with a short form like: Who is this for? What’s your message?
Why it's better:
- Helps users generate from a blank page, the hardest moment
- Tailors output based on input, which avoids generic results
Metric to track:
- Conversion rate from template to completed doc
- GrammarlyGO usage rate for new users