Measuring What Matters: KPIs That Actually Predict Program Success
- Yaniv Corem
- 18 hours ago
- 8 min read
Last quarter, a program director sent me her stakeholder report.
It looked impressive. Glossy design, lots of charts, big numbers.
"87 applications received. 15 founders accepted. 12 completed the program. Demo day had 150 attendees. Founders raised $3.2M in follow-on funding."
I asked her: "How many of those founders are still operating six months later?"
Silence.
"How many actually validated their business models during the program?"
Another pause.
"Do you know which parts of your program actually drove those funding outcomes, or are you just guessing?"
She admitted: "We track what's easy to measure. Not necessarily what matters."
And that's the problem.
Most accelerators are drowning in data but starving for insight. They measure activity (how many workshops, how many mentors, how many applications) instead of impact (did founders actually improve? did the program make a difference?).
They track outputs (funding raised, demo day attendance) instead of outcomes (sustainable businesses, founder capability development, long-term survival).
And then they wonder why their program looks successful on paper but doesn't feel like it's actually working.
The Vanity Metrics Trap (Again)
I know I've hit this topic before in "The Program Goals Trap," but it's worth repeating because I see programs make the same mistakes over and over.
Here's what vanity metrics look like in practice:
"We had 200 applications!" Cool. But how many were actually a good fit? If 80% were terrible fits, your sourcing strategy is broken—you're just generating noise.
"Demo day had 300 attendees!" Great optics. But how many were actual investors with check-writing authority? How many meetings did founders get? How many deals closed?
If demo day is a networking event masquerading as investor pipeline, you're measuring the wrong thing.
"Founders raised $5M in total funding!" Sounds impressive. But:
What percentage of the cohort raised? (If it's 2 out of 15, that's a different story)
At what valuation? (Raising at a terrible valuation isn't success)
From whom? (Friends and family? Venture debt? Actual VCs?)
Are they still operating 12 months later? (Raising money doesn't mean you'll survive)
See, vanity metrics look good in reports. They're easy to track and impressive to funders.
But they don't tell you whether your program is actually working.
What Actually Predicts Success
Want to know the difference between programs that create real outcomes and programs that create the illusion of success?
It comes down to what they measure—and when.
The best programs track leading indicators (things you can act on during the program) and outcome-based metrics (things that prove long-term impact).
Here's the framework I recommend:
Leading Indicators (During the Program)
These are the metrics that tell you whether founders are actually progressing, not just showing up.
Founder capability growth
Can founders do things at the end of the program that they couldn't do at the beginning?
Examples:
Can they run effective customer discovery interviews?
Can they scope an MVP and prioritize features ruthlessly?
Can they analyze unit economics and make data-driven decisions?
Can they tell a compelling story about their business?
How to measure:
Pre/post capability assessments (ask founders to rate themselves, or better yet, have them demonstrate skills)
Milestone completion (e.g., "completed 10 customer interviews and synthesized insights")
Facilitator observations (did they apply what they learned?)
Why it matters: If founders aren't developing new capabilities, your curriculum isn't working—no matter how impressive your speakers are.
Business model validation progress
Are founders testing their assumptions and gathering evidence that their business will work?
Examples:
Number of customer interviews completed
Evidence of product-market fit signals (retention, NPS, willingness to pay)
Results from go-to-market experiments (which acquisition channels work?)
Financial model clarity (do they understand their unit economics?)
How to measure:
Weekly progress updates (What did you test this week? What did you learn?)
Milestone tracking (e.g., "achieved 10% week-over-week growth in active users")
Founder-reported validation data (customers acquired, revenue generated, retention rates)
Why it matters: Founders who validate their models during the program are far more likely to succeed post-program than founders who just refine their pitch.
Mentor-founder relationship quality
Are mentors actually delivering value, or are they just showing up?
Examples:
Founders rate mentorship as "very helpful" (not just "attended")
Founders report applying mentor feedback
Mentors report meaningful progress in founder conversations
How to measure:
Post-session feedback surveys (on a 1-10 scale, how useful was this mentor session?)
Quarterly founder surveys (Who are your top 3 most helpful mentors? Why?)
Mentor engagement tracking (Are mentors showing up and staying engaged?)
Why it matters: Mentorship is one of the biggest value drivers in accelerators—but only if it's high-quality. If mentors aren't helping, you need to know so you can fix it.
Founder engagement and satisfaction
Are founders showing up, participating, and finding value—or are they checked out?
Examples:
Attendance rates (are founders skipping sessions?)
Participation quality (are they asking questions, sharing progress, engaging in peer learning?)
Real-time satisfaction (are they telling you what's working and what's not?)
How to measure:
Attendance tracking (flag founders with <80% attendance early)
Engagement scoring (facilitators rate participation on a 1-5 scale)
Pulse surveys (weekly or bi-weekly "How's it going?" check-ins)
Why it matters: Founder disengagement is an early warning sign. If you catch it in Week 3, you can intervene. If you don't notice until Week 10, it's too late.
Lagging Indicators (Post-Program)
These are the metrics that prove your program created lasting impact.
Follow-on funding raised (with context)
Yes, funding matters. But not in isolation.
What you actually need to know:
What percentage of cohort raised?
How much did they raise?
From whom? (Angel? Seed VC? Corporate partner?)
At what stage? (Pre-seed? Seed? Series A?)
How long after the program? (Immediately? 6 months? 12 months?)
Why context matters: A cohort where 80% raised $100K each from friends and family is very different from a cohort where 30% raised $1M each from institutional VCs.
Track the details, not just the headline number.
Revenue traction and path to profitability
Funding is great. But revenue is better.
What you need to know:
What percentage of cohort has revenue?
What's their monthly/annual recurring revenue (MRR/ARR)?
What's their growth rate?
Are they profitable, or do they have a clear path to profitability?
Why it matters: Founders who build sustainable businesses (revenue, customers, clear path to profitability) are more valuable than founders who raise money and burn it in 6 months.
12-month and 24-month survival rates
This is the metric most programs ignore—and it's the one that matters most.
What percentage of your cohort is still operating (not shut down, not pivoted out of existence) 12 and 24 months post-program?
Industry benchmark: ~70% survival at 12 months is solid. ~50% at 24 months is realistic.
If your survival rates are significantly below this, your program isn't creating lasting impact—regardless of how much funding founders raised.
Founder capability retention
Are founders still applying what they learned, or did it all fade after demo day?
Examples:
Do they still run customer discovery when testing new ideas?
Do they track the right metrics?
Do they make data-driven decisions about product and go-to-market?
How to measure:
Quarterly check-ins with alumni (What are you working on? Are you still using [framework/tool] from the program?)
Case studies and testimonials (How did the program change how you operate?)
Why it matters: If founders forget everything you taught them, your program was glorified summer camp—not founder development.
Alumni engagement
Are alumni still connected to your program, or did they disappear after graduation?
Examples:
Percentage of alumni who respond to outreach
Percentage who attend alumni events
Percentage who give back (mentor current cohorts, make intros, provide feedback)
Why it matters: Engaged alumni are a signal that your program created real value. Disengaged alumni suggest the opposite.
Systems Metrics (Meta-Level)
These are the metrics that tell you whether your program itself is sustainable.
1. Cost per successful founder
Don't just measure cost per cohort. Measure cost per successful founder.
Example:
Program cost: $500K
Cohort size: 15 founders
Founders who "succeeded" (raised funding or hit revenue milestones): 5
Cost per successful founder: $100K
This tells you whether your program is financially sustainable. If your cost per successful founder is climbing, you've got a problem.
2. Mentor retention and satisfaction
Are your mentors sticking around, or do they burn out after one cohort?
Track:
Mentor return rate (what percentage mentor multiple cohorts?)
Mentor satisfaction scores
Mentor-reported value (Do they feel like they're making an impact?)
Why it matters: If mentors burn out, you lose institutional knowledge and have to constantly recruit/onboard new mentors (expensive, time-consuming).
3. Program team efficiency
How much time is your team spending on admin work vs. high-value founder support?
Track:
Hours spent on application processing, data entry, scheduling
Hours spent on 1:1 founder coaching, mentorship facilitation, strategic support
Why it matters: If your team is spending 40% of their time on admin, you need better systems. That time should be spent helping founders.
4. Stakeholder alignment
Are your funders, founders, and team on the same page about what success looks like?
Track:
Funder satisfaction with program reports and outcomes
Founder satisfaction with program value
Team satisfaction and retention
Why it matters: Misalignment creates churn. If funders want X, founders need Y, and your team is delivering Z, something's broken.
How to Actually Implement This
Alright, so you're convinced you need better metrics. Now what?
Here's the step-by-step:
Step 1: Audit your current metrics
Pull your last stakeholder report. List every metric you're currently tracking.
For each metric, ask:
Is this an output or an outcome?
Is this a leading indicator (predictive) or lagging indicator (retrospective)?
Does this metric actually tell me if founders are succeeding?
Be brutal. Circle the vanity metrics and cross them out.
Step 2: Pick 3-5 metrics per stage
Don't try to track everything. Pick the most important metrics for each stage:
Selection: Applicant quality, founder-program fit
Development: Capability growth, validation progress, engagement
Graduation: Funding raised (with context), revenue traction
Post-Program: 12-month survival, founder capability retention
That's ~10-15 metrics total. More than that, and you'll drown in data.
Step 3: Build collection systems
Decide how you'll actually collect this data:
Weekly founder check-ins (via Airtable, Notion, or a custom form)
Post-session surveys (quick 2-3 question pulse checks)
Quarterly alumni surveys (check in on survival, revenue, funding)
Annual deep dives (full impact assessment)
Pro tip: Make data collection as frictionless as possible. If founders have to fill out a 20-question survey every week, they won't do it.
Step 4: Set targets
For each metric, define what "good" looks like.
Examples:
Founder engagement: 85%+ attendance, 4+ average participation score
Capability growth: 70%+ report significant improvement in [skill]
12-month survival: 70%+ still operating
Mentor satisfaction: 80%+ would mentor again
Targets give you a baseline to measure against.
Step 5: Review and iterate
After every cohort, review your metrics:
Which metrics moved in the right direction?
Which didn't?
What do we need to change?
Measurement isn't "set it and forget it." It's a continuous improvement loop.
Common Mistakes to Avoid
Before you go rebuild your metrics dashboard, watch out for these traps:
Mistake 1: Tracking too many metrics
More metrics ≠ better insight. Pick the 10-15 that actually matter and track those religiously.
Mistake 2: Only measuring at the end
If you're only assessing impact at demo day, you've lost 90% of your ability to intervene and improve founder outcomes.
Mistake 3: Ignoring leading indicators
Lagging indicators (funding, survival) tell you what happened. Leading indicators (engagement, validation progress) tell you why and give you a chance to fix problems in real-time.
Mistake 4: Not disaggregating data
"Founders raised $5M" is useless. Break it down: How much per founder? What percentage raised? From whom? At what stage?
Mistake 5: Treating all outcomes equally
A founder who raised $2M and failed in 6 months is not the same as a founder who bootstrapped to $500K ARR and is still growing.
Weight outcomes by long-term sustainability, not just short-term optics.
The Bottom Line
If you're measuring what's easy instead of what matters, you're flying blind.
Vanity metrics make you look good in reports. But they don't tell you whether your program is actually creating impact.
The programs that win—the ones that create real founder outcomes and build sustainable models—are the ones that measure the right things.
They track leading indicators during the program so they can course-correct in real-time.
They track outcome-based metrics post-program so they can prove long-term impact.
And they're honest about what's working and what's not, even when the data is uncomfortable.
Start measuring what matters. Not what's easy.
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Need help building a better metrics system? I've created a KPI Selection Framework that walks you through choosing the right metrics for each stage of your program and building collection systems that actually work. Download it here.
You might also find the Leading Indicator Tracker useful—it's a simple dashboard template for tracking founder progress in real-time. Grab it here.
This post is part of a series on program design and operations for accelerators, incubators, and startup studios. If you found this useful, you might also like: "The Program Goals Trap" and "Beyond Demo Day: Setting Post-Program Success Metrics."
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