The Freemium Formula That Has Misled Many

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Elena Verna (Reforge Partner, Ex SurveyMonkey, Miro, Netlify, MongoDB) has spent her career in freemium. I loved this post from her because it challenges some widespread thinking on freemium models.

The Old Way Of Thinking About Freemium

"Many companies still only consider freemium models if they think they will pay for themselves. They take an approach boiling down a freemium model's value down to four factors:

  1. The cost of marketing and selling to a user in a paid model

  2. The cost of serving a free user

  3. The cost of acquiring a free user

  4. How effective you are at converting users from free to paid

The problem is that this approach falsely assumes that only the direct monetization value from a freemium model should be considered.

How To Actually Think About The Value of Freemium

In addition to the above considerations, you need to account for the indirect growth value that freemium can provide. Elena says there are 6 things:

  1. Higher Willingness to Pay - Potential buyers consciously or unconsciously evaluate if Purchase Price < Potential Value * Probability of Realizing Potential Value. Freemium can increase the probability of realizing potential value.

  2. Increased Network Effects - Freemium can contribute to increase network effects such as the Data Network Effect Spotify has around discovery. NFX leads to increased acquisition, retention, and monetization.

  3. Stronger User Habits - Free products allow users to develop a habit before they pay, creating not only more opportunities to convert but most importantly ensuring that customer retains.

  4. Growth Loop Acceleration - Freemium can both enable product-led growth loops and accelerate existing ones where free trials and other models cannot.

  5. Lower CAC - One of the more overlooked methods of indirect monetization is getting users into your product before they do in competitors and become more expensive to acquire.

  6. Faster Product Insights - A larger, free user base can accelerate experimentation cycles and lead to more/better user insights for product decisions.

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