Our NPS survey results came back with a pattern I couldn't ignore. Promoters loved us—9s and 10s across the board. Detractors hated us—1s and 2s.
But the comments from detractors all said the same thing: "Too complicated." "Took too long to see results." "Gave up before it started working."
These people weren't complaining about missing features or poor support. They were churning before they ever experienced the product's core value.
I pulled the usage data. The average time from signup to first meaningful result: 14 days.
Our trial period? 14 days.
We'd built a product where most users' trials expired right as they were about to see value.
This wasn't a feature problem or a pricing problem. It was a time-to-value problem. And fixing it became the most impactful project I've ever worked on.
The Two-Week Onboarding Death March
I started by interviewing 20 users who'd signed up in the past 60 days—half who activated successfully, half who churned during trial.
The pattern was stark.
Successful users described a first-week experience like this:
"I signed up, imported my data, configured a few settings, and within 2-3 days I got my first report. Once I saw that working, I went deeper into the product."
Churned users described this:
"I signed up, spent a day trying to figure out how to import my data, got confused about the configuration options, set it aside to deal with later, and then forgot about it. By the time I came back, the trial had expired."
Same product. Same onboarding flow. Completely different outcomes.
The difference? Time to first value.
Users who got value in the first 3 days almost always converted. Users who took longer than 7 days almost always churned.
Our onboarding flow was designed to be comprehensive. We walked users through every feature, every setting, every configuration option. The assumption was: more complete onboarding = better long-term success.
The data said otherwise.
Comprehensive onboarding was killing activation because it delayed value. Users who spent a week "learning the product" never got to the part where the product solved their problem.
Finding the Minimum Path to Value
I needed to understand: What's the absolute minimum a user needs to do to get value from this product?
Our product was an analytics platform. The core value proposition: "Get insights from your data faster than building custom reports."
The existing onboarding flow required:
- Connect data sources (could take 2-3 days depending on technical complexity)
- Map data fields to our schema (1-2 days of configuration)
- Set up custom dashboards (1-2 days of learning our dashboard builder)
- Wait for enough data to accumulate (3-7 days depending on data volume)
- Generate first report
Total time to value: 8-14 days on average.
I asked a different question: What if a user just wanted to see one useful insight as fast as possible?
Could we deliver value in day 1? Hour 1? Minute 1?
I sketched a new flow:
- Connect one data source (we'd prioritize the easiest integration)
- Skip custom configuration—use smart defaults
- Skip dashboard building—show pre-built templates
- Use sample data to show what insights look like immediately
- Generate first insight within minutes, not days
This was controversial.
The product team argued: "Users won't get the full value without proper configuration."
The sales team worried: "Pre-built templates won't match their specific use case."
The data team pointed out: "Sample data isn't their real data. That's not real value."
They were all right. And they were all missing the point.
The goal wasn't to deliver perfect, comprehensive value on day 1. The goal was to deliver enough value to prove the product could work, so users stayed engaged long enough to invest in proper setup.
The New Onboarding Flow
We rebuilt onboarding around a simple principle: Get users to their first "aha moment" in under 30 minutes, even if it's imperfect.
Old flow: Complete setup → Wait for data → Get value New flow: See value immediately → Get invested → Complete setup properly
Here's what changed:
Immediate Value with Sample Data
When users signed up, we immediately showed them what our product could do using sample data from their industry.
SaaS company signs up? We show them sample MRR analysis, churn cohorts, and expansion revenue tracking using realistic (but fake) SaaS data.
Ecommerce company signs up? We show them sample customer lifetime value analysis, purchase behavior segmentation, and inventory insights.
This was psychologically crucial. Users could see what "good" looked like before investing time in setup. They knew what they were working toward.
Detractors said: "This isn't real. They can see it's demo data."
They missed the point. Users didn't need it to be their data yet. They needed to see that the product could generate the insights they cared about. Sample data proved that in 60 seconds.
One-Click Quick Start vs. Custom Setup
Instead of forcing every user through comprehensive setup, we offered two paths:
Path 1: Quick Start (Recommended) "Connect your [easiest data source] and see your first insights in under 5 minutes using our pre-built templates."
Path 2: Custom Setup "Configure everything exactly how you want it. Takes 1-2 hours."
90% of users chose Quick Start.
The Quick Start path:
- Only asked for the single easiest integration to connect
- Used smart defaults for all configuration
- Showed pre-built dashboards relevant to their industry
- Generated first real insights within 5-10 minutes
Users who went through Quick Start could see real value from their real data on day 1.
Then we encouraged them to customize and expand: "You're seeing basic insights. Want to add more data sources and customize your dashboards to go deeper?"
By that point, they were invested. They'd seen value. They trusted the product worked. They were willing to spend time on proper setup.
Progressive Disclosure of Complexity
The old onboarding tried to teach users everything upfront. The new onboarding revealed complexity progressively, only when users needed it.
Week 1 goal: Get first insights from one data source Week 2 goal: Add second data source and explore pre-built templates Week 3 goal: Customize dashboards and build first custom report Week 4 goal: Set up automated reporting and team sharing
We didn't hide the advanced features—we just didn't force users to learn them before they saw basic value.
This violated conventional wisdom about onboarding. Every product expert says: "Teach users the full product early so they understand its capabilities."
That advice is wrong for complex products.
Users don't care about capabilities until they've experienced basic value. Teaching them everything upfront just delays the moment they see why this product matters.
The Results: Time-to-Value Collapsed
We rolled out the new onboarding flow to 50% of signups and compared them to the control group over 8 weeks.
New flow vs. old flow:
Time to first insight:
- Old: 14 days average
- New: 2.8 days average
- Improvement: 80% reduction
Trial-to-paid conversion:
- Old: 11% conversion rate
- New: 24% conversion rate
- Improvement: 13 percentage points (more than doubled conversion)
90-day retention:
- Old: 64% of paid users still active after 90 days
- New: 78% still active after 90 days
- Improvement: 14 percentage points
Support tickets during trial:
- Old: 2.3 tickets per trial user on average
- New: 0.9 tickets per trial user
- Improvement: 60% reduction
Same product. Same features. Same pricing. Just a different path to value.
The business impact: At our trial volume (500 signups/month), the conversion improvement was worth ~$800K in additional ARR annually.
What I Learned About Time-to-Value
This project taught me several uncomfortable truths about product onboarding:
Comprehensive Onboarding Kills Activation
The instinct when building onboarding is to be thorough. Show users everything. Teach them all the features. Make sure they understand the full product.
This instinct is wrong.
Comprehensive onboarding delays value. Users who spend their first week "learning the product" never get to the part where the product solves their problem.
They get exhausted, distracted, or discouraged before they experience the core value proposition.
Better approach: Deliver minimum viable value as fast as possible, then progressively teach advanced capabilities.
Sample Data Isn't Cheating
Some product people think showing sample data during onboarding is dishonest or gimmicky.
It's neither.
Sample data lets users see what "good" looks like before they invest time in setup. It sets expectations. It proves the product can generate the insights they care about.
Users know it's sample data. They're not confused. They're motivated because they can visualize what their real data will look like once they finish setup.
The First Value Moment Doesn't Need to Be Perfect
Our product's full value required connecting multiple data sources, customizing dashboards, and configuring complex settings.
But users didn't need the full value in week 1. They needed enough value to believe the product could work.
A simple insight from one data source was enough to prove: "This product can help me."
Once users believed that, they invested time in proper setup to unlock more value.
You don't need perfect value on day 1. You need proof-of-value.
Time-to-Value Is More Important Than Feature Breadth
Before this project, the product team prioritized shipping new features. More features = more value = more conversions.
That logic is backwards for early-stage users.
Users don't convert because you have lots of features. They convert because they experienced value during their trial.
Shipping features that users discover in month 3 doesn't help conversion. Shipping features that get users to value in day 1 transforms conversion.
After this project, we reprioritized the roadmap around time-to-value, not feature count.
How to Reduce Your Time-to-Value
Here's the process I'd use if I had to do this again at a different company:
Measure Current Time-to-Value
Define what "first value moment" means for your product. For us, it was "generated first meaningful insight from their data."
Then measure: How long does it take users to get there from signup?
Pull data on recent signups and track:
- Time from signup to first value moment
- What percentage reach it in: <1 day, 1-3 days, 3-7 days, 7-14 days, 14+ days
- Correlation between time-to-value and conversion rate
You'll likely find: Users who get value fast convert at much higher rates than users who take a long time.
Identify the Barriers to Fast Value
Interview users who took a long time to reach first value (or never got there). Ask:
"Walk me through your first few days with the product. Where did you get stuck? What took longer than expected?"
Watch session recordings of new users. Look for:
- Where do they spend the most time?
- Where do they get confused?
- What steps take days when they should take minutes?
For us, the barriers were: complex data source connections, overwhelming configuration options, and waiting for enough data to accumulate.
Design the Minimum Path to Value
Ask: What's the absolute minimum a user needs to do to see proof that this product can solve their problem?
Strip away everything non-essential:
- Can you skip configuration and use smart defaults?
- Can you start with one data source instead of requiring all of them?
- Can you show pre-built examples instead of requiring customization?
- Can you use sample data to show value before real data is ready?
The goal: Get users to a "wow, this could work" moment in under 30 minutes, even if the setup is incomplete.
Build Quick Start Path Alongside Custom Setup
Don't remove the comprehensive setup flow. Some users want full control.
Instead, offer two paths:
Quick Start: Fastest path to value, using defaults and templates Custom Setup: Full control, takes longer but gives exact configuration they want
Let users choose. Most will choose Quick Start. Those who choose Custom Setup are usually power users who know exactly what they want.
Use Progressive Disclosure
Don't hide advanced features, but don't force users to learn them upfront.
Week 1: Basic value from easiest setup Week 2: Introduce one additional capability Week 3: Show customization options Week 4: Reveal advanced features
By week 4, users have experienced value multiple times and are invested enough to learn complex features.
The Uncomfortable Truth About Trials
Most SaaS products have trial periods that are too short for their time-to-value.
Common mistake:
- 14-day trial
- 10-day average time-to-value
- Users churn right as they're about to see value
Two solutions:
Option 1: Extend trial length to match time-to-value + buffer
- If time-to-value is 10 days, make trial 21 days
- Gives users time to experience value before deciding
Option 2: Reduce time-to-value to fit trial length (what we did)
- If trial is 14 days, reduce time-to-value to <3 days
- Users experience value early and have 11 days to explore further
We chose option 2 because it had higher conversion impact. Users who saw value in the first 3 days were more likely to convert than users who saw value in days 10-14, even with a longer trial.
Early value creates momentum. Late value creates doubt.
What Changed After We Fixed Time-to-Value
Reducing time-to-value didn't just improve conversion—it changed how we thought about product development.
Product roadmap: We now evaluate every feature by asking: "Does this help users get to value faster or does this add value for users who are already activated?"
Both are important, but we prioritize fast-time-to-value features over depth features until we hit 40%+ trial conversion.
Customer success: CS used to focus on helping users through comprehensive setup. Now they focus on helping users get one quick win, then expanding from there.
Marketing: Trial messaging changed from "Start your 14-day trial" to "See your first insights in under 10 minutes." Conversion on trial signups increased because expectations were clearer.
Sales: Sales started using time-to-value as a competitive differentiator. "Our competitors take 2-3 weeks to get set up. You'll see value this afternoon."
Having fast time-to-value became a core part of our go-to-market strategy.
Time-to-Value Is Your Most Important Activation Metric
If I could only track one metric for product adoption, it would be time-to-value.
Not activation rate. Not feature usage. Not onboarding completion.
Time-to-value predicts everything else:
- Users who get value fast convert at higher rates
- Users who get value fast retain at higher rates
- Users who get value fast expand faster
- Users who get value fast refer more
Every day you delay value is a day users might churn, get distracted, or lose confidence in your product.
If your time-to-value is longer than half your trial period, you have a problem.
Fix it by removing barriers, using defaults and templates, showing sample data, and designing a quick-start path that gets users to proof-of-value as fast as possible.
Your conversion rate will thank you.