The WOM Coefficient

Yousuf Bhaijee (Former VP Growth @ Eaze) and Tomas Pueyo (VP Growth at Course Hero) wrote an excellent piece on defining a metric to measure and monitor word of mouth called The Word of Mouth Coefficient. Here is my quick synthesis and some thoughts.

Core Problem: WOM is Critical But Hard to Measure and Therefore Hard To Influence

Why define and use this metric? Yousef and Tomas lay out a good argument that:

1. Word of Mouth is becoming more critical as an acquisition channel as the core 3 (Facebook, Google, Amazon) become more saturated, competitive, and less open.

2. Historically, word of mouth has been hard to measure. Most teams use some version of NPS, attribution surveys, or social listening tools to get a sense.

3. Because it is hard to measure, teams tend to ignore word of mouth, lean into more measurable channels, which leads to being less capital efficient.

The Word of Mouth Coefficient

The WOM Coefficient is pretty simple. It is the number of new organic users divided by active users (returning users + non-organic new users). If your denominator is WAU and the WOM Coefficient is .2, it tells you every 5 WAU's generates 1 new user per week via word of mouth. Their goal was three things. Create a metric that was:

1. Tied To Active Users - It was tied to active users. The premise is that active users is the predictor of word of mouth, not new users. This make sense, as retention is at the core of every growth loop.

2. Stable - The metric was stable, and therefore we could use it to forecast.

3. Able To Be Influenced - We were able to find the inputs and influence it with product and marketing initiatives.

The Data + The Key

Yousuf put together some data across three different product categories, gaming, EdTech, and a music mobile app and found in all three cases that the above was true. Active users did predict word of mouth and the metric was stable enough to use in forecasting. Here is the example showing the WOM Coefficient from three different gaming genres:

But as with any output metric, the key is can you find inputs that you can influence to improve the output. I'm interested in the next post in the series to see cases where Yousuf and team were able to do that.

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