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Actionable SaaS metrics Thomas Fuchs Freckle Time Tracking

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The goal is to sustainably make more money (duh).

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Spend Less More customers Charge more easy (do it!) hard likely not worth it

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What metrics to track?

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Money per day

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Conversion rate The percentage of newly created accounts that become paying customers

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whoa, raw data sucks

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now, that’s better* *more later

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paid at least twice in 100 day time span signed up on January 26 Conversion rate

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Churn rate The percentage of paying accounts that are cancelled* within a time period (usually monthly). *includes abandoned (credit card not updated)

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Lifetime Customer Value All the monies the average paying customer will end up giving to you.

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Lifetime Customer Value daily billed $ amount per paying customer × average length of paid subscription

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Lifetime Customer Value Money = average daily $ amount billed in the last 30 days

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Lifetime Customer Value Accounts = current number of active paying accounts

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Lifetime Customer Value Churn = 30-day churn rate (in %) for the last 30 days

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Money ÷ Accounts × 30 days × 100 ÷ Churn rate Lifetime Customer Value

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(Money ÷ Accounts × 30) × (100 ÷ Churn) Example: Money = $1,000, Accounts = 500, Churn = 5% (1000 ÷ 500 × 30) × (100 ÷ 5) = 1000 ÷ 15000 × 20 = $1,200

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The goal is to sustainably make more money…

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(Money ÷ Accounts × 30) × (100 ÷ Churn) 1. charge more 3. lower churn 2. increase conversions

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(Money ÷ Accounts × 30) × (100 ÷ Churn) easy hard hard

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Trends > Absolute numbers It doesn’t matter if your churn rate is 2% or 15%, it only matters that it goes down over time (does it?)

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Raw data (billed amount per day, last 3Y)

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30-day moving average (billed amount per day, last 3Y)

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Days where billing didn’t work or where we had to catch up with lots of payments

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These spikes are not meaningful and distract from the overall trend.

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Locally weighted scatterplot smoothing (LOESS) (billed amount per day, last 3Y)

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“LOESS is a non-parametric regression method that combines multiple regression models in a k-nearest- neighbor-based meta-model.” (Gesundheit!) TL;DR: LOESS gives you meaningful trends https://github.com/jasondavies/science.js/

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Real world example: raising our prices

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We increased prices between 60% and 250% Lifetime customer value up conversion rate down

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To increase conversion rate, we introduced a lifecycle email sequence (after some tweaks it worked beautifully!)

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Segmentation Find places to improve by plan, by number of users, by number of support emails, by amount of data, by referrer, etc.

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[email protected] @thomasfuchs