3 Pitfalls When Calculating LTV

Introduction

To start, I’m Cameron Hozouri, CEO and Co-founder of ByteBrew, the all-in-one platform to analyze, operate and grow your apps and games without barriers. From the start ByteBrew was developed to make the lives of game developers easier, and for us as previous game devs, one of the biggest challenges was how to accurately measure LTV.

In games and apps, this metric forms the foundation for decision-making across every stage of an app’s lifecycle – from evaluating high-performing user acquisition channels and revenue-driving user segments, to balancing core gameplay and refining monetization strategies.

However, traditional approaches to LTV calculation mishandle several key issues that our all-new LTV Reports product specifically addresses.

#1 - ARPDAU / ARPU Problem

Across case studies and documentation, the prevailing methodology for computing user lifetime assumes either a constant ARPDAU or ARPU variable in their formulas.

Utilizing these simplified metrics ignore fundamental aspects of user behavior – revenue is not static, nor uniform, either user-to-user or day-to-day. In live mobile monetizing environments players behave dynamically with a high degree of variability in how long they play, what they engage with, and how they monetize. By flattening user behavior to one monolithic spending rate across an observed date range, the method fails to accurately represent the revenue generated by users – leading studios to make misinformed decisions around user acquisition, and game design.

#2 - Regression Modeling

To produce models that mimic the decaying nature of retention patterns in mobile games, the most conventional lifetime value formulas rely on fitted power regression modeling to output LTV values. While power regression is most suited for mirroring early user decay, it falls short in accurately representing the long tail user behavior in engagement and monetization that define mobile app lifecycles. By design, the gradual decay of power functions introduces an upward bias in LTV calculations—one that compounds over time, becoming increasingly pronounced in longer retention windows. This produces potentially inflated LTV projections in the tail end of the user lifecycle, where real monetization activity has often tapered off or stopped entirely. As a result of its function, the model continues to attribute value to users who are no longer generating revenue, painting a distorted view of long-term performance.

#3 - Aggregated Data

Equally vital to the methods used to produce LTV is the fidelity, structure, and granularity of the underlying data. Even the most complex models break down when built on data streamed from multiple separate platforms. In these environments, data is often merged into static, aggregated metrics—reintroducing the core issues that produce inaccurate projections. When data is no longer directly tied at the user level, drilling down into cohort-specific LTV behavior fails. Attempting to identify which users monetize after completing a specific in-app event, or how LTV varies by acquisition source, simply can’t be diagnosed. Without data that’s generated, ingested, and stored at the event level within a unified architecture, you lose both the operability and user-level context required to measure real LTV.

ByteBrew LTV Reports

At ByteBrew we released our all-new LTV Reports to provide our community grounded clarity into the true value of their users by calculating real LTV. As a unified platform that operates your engagement, monetization and marketing data, ByteBrew has the unique capability of both generating and ingesting real-time revenue for in-app ad revenue, in-app purchases and custom events directly from apps on demand - enabling true LTV measurement capable of being drilled down to the individual player level.

To illustrate, the below diagram depicts a representation of how ByteBrew ingests revenue data from every player’s lifetime in its time series format to deliver our studios with the highest level of granularity in real lifetime value.

ByteBrew LTV does not utilize regression models or static monetization metrics to approximate values, ByteBrew LTV leverages our proprietary compute engines and data architecture to generate the actual lifetime value of each player in real time—capturing every dollar spent from their very first interaction. Leveraging our architecture’s data interoperability, the platform unlocks answers to previously inaccessible questions – such as: What is the LTV of users who complete the tutorial on Day 0 acquired through a specific campaign?

Learn more about our LTV Reports Announcement