May 10, 2023 · 20m · top-founders
Using data to find product-market fit
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
Mode co-founder Benn Stancil outlines how early-stage SaaS startups can leverage behavioral data and customized retention metrics to identify authentic user value and avoid the costly trap of premature scaling.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Nathan holds 4.3% of the talking time here. How this is scored →
Nathan as informed peer 0.0 Guest teaching 0.0 Guest disagreement 0.0 Nathan pushing back 0.0
speaking balance: gold is Nathan, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 18:30 Benn calls traditional PMF curves a lie
Benn directly refutes conventional startup dogma, stating that static binary PMF diagrams are misleading and that fit must be repeatedly rediscovered.
Hardest push from Nathan ▶ 0:00 Latka asserts strict content quality standardsLatka sets a rigorous bar for conference presentations, emphasizing that only concrete data and hard growth metrics are accepted.
Biggest teaching moment ▶ 14:35 Benn demonstrates the flaw in aggregate retention metricsBenn systematically shows how identical headline retention rates can completely obscure whether an app has real sticky PMF or sporadic engagement.
Nathan holds their own ▶ 0:25 Latka highlights proprietary SaaS database toolsLatka demonstrates his domain authority by presenting GetLatka's searchable founder database across revenue, CAC, and valuation metrics.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Nathan as informed peer | Guest teaching | Guest disagreement | Nathan pushing back | Why |
|---|---|---|---|---|---|---|
| Nathan Latka Introduces SaasOpen and Founder Data Resources | 0 | 0 | 0 | 0 | Nathan introduces the conference recording from SaaS Open, followed by Benn Stancil taking the stage for a solo keynote presentation on finding PMF using data. | |
| Uncovering User Traction: The Knot Case Study | 0 | 0 | 0 | 0 | Benn presents a case study of The Knot discovering unanticipated viral user behavior around their wedding countdown feature using Mode data. | |
| Following Desire Paths in Behavioral Product Data | 0 | 0 | 0 | 0 | Benn describes how software desire paths emerge in data, citing Burbn's pivot to Instagram and the need to reduce discovery time before runway depletes. | |
| The Dangers of Premature Scaling and False PMF | 0 | 0 | 0 | 0 | Benn breaks down the risks of scaling before genuine PMF and uses hypothetical cohort retention visualizations comparing Greenhouse and Front. | |
| Establishing Product-Specific Product-Market Fit Metrics | 0 | 0 | 0 | 0 | Benn outlines customized metrics tailored to specific business types, such as DAU/MAU for daily tools, NPS for competitive products, and willingness to pay. | |
| Product-Market Fit as a Continuous Lifecycle | 0 | 0 | 0 | 0 | Benn concludes the presentation by dismantling the myth of static PMF, framing it as an ongoing cycle across customer segments and new market expansion. |
Statements from this episode (9)
Stancil: Lean into observed user behavior rather than forcing intended workflows
“They really, like, leaned into the way that people were using it, rather than trying to force them to do, do the things they originally wanted them to do.”
Stancil: Finding product-market fit requires tracking features that distract users
“And so when you're, like, looking for product market fit, this is actually probably the best thing you can do, is pay attention to, like, what are your girls in the red dress, ah, that everybody's actually distracted by when you're trying to get them to build …”
Stancil: Mode prematurely scaled ad spend before achieving product-market fit
“And basically this was us finding, thinking we'd found a product market fit, and we hadn't. That we thought we had found something that worked, we thought it was time to scale, to spend more money on ads, and all that kind of stuff, and it turns out kind of ob…”
Stancil: Daily retention curves obscure vital differences in product stickiness
“The problem here is, if you just look at daily retention rates for these two things, they will look exactly the same. That the way this math works out is these two things can actually show the exact same retention rates on like a day-by-day basis. And really w…”
Stancil: Daily software needs a 50% DAU/MAU ratio for PMF
“One, if you are a product that needs to be used every day, something like what front is makes sense. Things like DAUs over MAUs, which effectively measures how many days in a month people are using your product. It's really important. A high number there matte…”
Stancil: B2B SaaS stickiness is measured by 4-day weekly active users
“Similarly, if you're, like, a SaaS business that's selling B to B, obviously, in that case, it usually follows much more of, like, a work week kind of pattern. And so if people are using your product more than four days a week, like, how many percentage of you…”
Stancil: High NPS is a strong PMF signal in crowded markets
“If you are a product that has a lot of competition then something like a high NPS is a pretty good sign of product market fit, because people have a lot of choices. You need to know that people, like, actually like your product. It's a thing that they want to …”
Stancil: 'Vitamin' products must measure user disappointment upon removal
“If your product is a vitamin and not a painkiller, so if your product is something that's kind of a nice to have, you want to actually see that people will be upset if they take it away. So this is actually a metric that I think Superhuman used, the email clie…”
Stancil: Startups only achieve product-market fit on a per-segment basis
“This is not something where it's like you are pre and post product market fit forever. You are pre and post product market fit for particular types of buyers, for particular products, for particular segments, and all those sorts of things.”