Word of Mouth is critical, but notoriously hard to measure and therefore hard to influence. @ybhaijee (Former VP Growth @ Eaze) wrote an excellent post on @reforge about The Word of Mouth Coefficient with some analysis: reforge.com/blog/word-of-m…
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When @ybhaijee and @tomaspueyo worked at Zynga together, they wanted a metric for Word of Mouth that was:
1. Based on Active Users
2. Stable enough to be used in forecasting
3. Could be influenced with product/marketing initiatives
The result was the Word of Mouth Coefficient
1. Based on Active Users
2. Stable enough to be used in forecasting
3. Could be influenced with product/marketing initiatives
The result was the Word of Mouth Coefficient
WOM Coefficient: Says that for every X active user, you will bet Y new organic users in that time period.
One key 🔑 is that rather than basing the metric on new users (like K-Factor) they based it on active users. Retention is at the foundation of every growth loop...
One key 🔑 is that rather than basing the metric on new users (like K-Factor) they based it on active users. Retention is at the foundation of every growth loop...
They looked at data across companies. Found the relationship that active users predicted new organic users to be true with r squared of .958...
Here is some interesting data from a mobile gaming company. Shows that the WOM Coefficient was stable across functions in DAU around .08...
More interesting is that it is stable (but different) across different types of games. This means it can be used in forecasting as well as how a portfolio of games fits together.
Here is data for an EdTech unicorn (real data but anonymized). A few things:
1. Seasonality - The metric is stable but within seasons. Makes sense for EdTech. If we were to look at this YoY it would show a similar pattern.
2. It also shows the impact of Covid-19 headwinds.
1. Seasonality - The metric is stable but within seasons. Makes sense for EdTech. If we were to look at this YoY it would show a similar pattern.
2. It also shows the impact of Covid-19 headwinds.
Here is another one for a mobile music app. The most interesting thing here is that the WOM coefficient declines with increases in paid acquisition. A lot of teams model a viral factor consistently as paid acquisition scales. This is wrong.
The key to all of this is once you track it, can you find inputs that can be influenced with product and marketing? @ybhaijee is going to break down how to do that in a follow-up post. Thanks to @philgcarter, @tomaspueyo, and @2michaeltaylor for the additional insights.
