3 ms·

The falls out of the fundamental equation for SaaS and other subscription business models: LTV = prospects * (conversion rate to paying) * (average price point

The falls out of the fundamental equation for SaaS and other subscription business models: LTV = prospects * (conversion rate to paying) * (average price point) / churn This makes a 5% increase in prospects, conversion rate, or price cause a 5% increase to LTV (and, eventually, to the enterprise value of the company). A 5% decrease in churn (measured against one's current churn rate, e.g., 5% -> 4.75%) has a slightly more than 5% impact to LTV / enterprise value. So many decisions about running a SaaS company fall directly out of this equation. Competent SaaS operators memorize it or, for less mathematically oriented operators, can at least summarize the relationships implied.

dfee 9y ago
If you're measuring the LTV of your pipeline, then yes. The challenge is measuring duration (retention length) -- especially early on when campaigns are typically run single threaded and have an outsized impact on LTV of individual cohorts. I.e. My company is 18 months old - what's my LTV? One approach is factoring in the period-churn-rate (if measuring MRR then considering monthly churn). But again, modeling isn't always super defensible.
- brockf 9y ago
  Survival modeling is exactly what's needed for these situations. It allows you to (a) consider censored data (i.e., active customers who you know stay for at least X months) and, (b) use flexible survival distributions beyond the standard exponential distribution assumed in the typical monthly churn rate calculations. Source: Run a data science company and we work on a lot of customer lifecycle modeling projects with companies much younger than yours.
  - dfee 9y ago
    I've done a bit of survival modeling, but my purpose was to understand retention across cohorts with certain attributes (typically, sign-up date, though occasionally campaign). I'm interesting in how you've used this to model churn. Is there a blog post or resource you recommend to learn more about this?
clairity 9y ago
spoken like a true startup founder. =) there is a lot to think about in a startup but the most important part is tracking how attractive your offering (not just the product, but also messaging and other value adds) is to your target market. i.e., nearly every startup is primarily a marketing problem, not a technology problem.

dfee 9y ago

If you're measuring the LTV of your pipeline, then yes. The challenge is measuring duration (retention length) -- especially early on when campaigns are typically run single threaded and have an outsized impact on LTV of individual cohorts. I.e. My company is 18 months old - what's my LTV? One approach is factoring in the period-churn-rate (if measuring MRR then considering monthly churn). But again, modeling isn't always super defensible.

brockf 9y ago
Survival modeling is exactly what's needed for these situations. It allows you to (a) consider censored data (i.e., active customers who you know stay for at least X months) and, (b) use flexible survival distributions beyond the standard exponential distribution assumed in the typical monthly churn rate calculations. Source: Run a data science company and we work on a lot of customer lifecycle modeling projects with companies much younger than yours.
- dfee 9y ago
  I've done a bit of survival modeling, but my purpose was to understand retention across cohorts with certain attributes (typically, sign-up date, though occasionally campaign). I'm interesting in how you've used this to model churn. Is there a blog post or resource you recommend to learn more about this?

clairity 9y ago

spoken like a true startup founder. =) there is a lot to think about in a startup but the most important part is tracking how attractive your offering (not just the product, but also messaging and other value adds) is to your target market. i.e., nearly every startup is primarily a marketing problem, not a technology problem.