Freemium Economics: Leveraging Analytics and User Segmentation to Drive Revenue (The Savvy Manager's Guides)
Eric Benjamin Seufert
Freemium Economics presents a practical, instructive approach to successfully implementing the freemium model into your software products by building analytics into product design from the earliest stages of development.
Your freemium product generates vast volumes of data, but using that data to maximize conversion, boost retention, and deliver revenue can be challenging if you don't fully understand the impact that small changes can have on revenue. In this book, author Eric Seufert provides clear guidelines for using data and analytics through all stages of development to optimize your implementation of the freemium model. Freemium Economics de-mystifies the freemium model through an exploration of its core, data-oriented tenets, so that you can apply it methodically rather than hoping that conversion and revenue will naturally follow product launch.
By reading Freemium Economics, you will:
- Learn how to apply data science and big data principles in freemium product design and development to maximize conversion, boost retention, and deliver revenue
- Gain a broad introduction to the conceptual economic pillars of freemium and a complete understanding of the unique approaches needed to acquire users and convert them from free to paying customers
- Get practical tips and analytical guidance to successfully implement the freemium model
- Understand the metrics and infrastructure required to measure the success of a freemium product and improve it post-launch
- Includes a detailed explanation of the lifetime customer value (LCV) calculation and step-by-step instructions for implementing key performance indicators in a simple, universally-accessible tool like Excel
the ad experience for the user. Because the search engine in the paid search model assumes the responsibilities of the DSP, the SSP, and the ad exchange from the ad exchange model, some operational efficiencies that emerge as frictions between moving parts are eliminated. For instance, the search engine is incentivized to offer advertisers robust analytics and targeting mechanisms in the search engine’s suite of advertising tools to optimize the advertiser’s yield and encourage continued use.
Facebook. Spotify’s partnership with Facebook, which came mere months after the service launched in the United States, contributed to an intense surge in growth that year. From September 15th, 2011 (a few days before the Facebook partnership was launched) through December 2012, Spotify’s user base doubled, from 10 million to 20 million users. Even more impressive was Spotify’s rate of conversion; on December 6th, 2012, Spotify announced that 5 million of its 20 million active users, or 25
projected return on investment and estimated time required to implement. Methodologies differ on how the product backlog should be constructed, but the team should estimate the development length of each backlog item and take only those that can feasibly be accomplished within the predetermined iteration cycle, with the understanding that some very large features will be implemented across multiple cycles through multiple component backlog items. Product teams are incentivized to keep iteration
social products with active communities and in products launched on platforms for which user reviews affect discoverability. The quality of a user’s device can also serve as an indicator for disposable income and thus the propensity (and ability) to spend money in a freemium product, although it is a weaker signal than some behavioral indicators. Demographic data can become more useful when it is available at a deeper level, such as when a user has connected to a product through a social
has been useful primarily for establishing a baseline understanding of how much money can be spent, on a per-person basis, to acquire new users. This aspect of LTV certainly holds true in the freemium model. It is a useful benchmark for setting the marketing budget, which determines how much money can be spent in user acquisition. But the dynamics of freemium product development, and the presupposition of at least a minimally effective analytics infrastructure upon launch of a freemium product,