The analysis demonstrates that player retention in mobile games can be accurately modeled with a power‑law curve, expressed either as \(r(n)=a\,b^n\) or \(r(n)=a\,n^{b}\). By fitting the curve to a limited set of cohort data—typically day‑1, day‑3, and day‑7 retention—analysts can derive the parameters \(a\) (initial retention level) and \(b\) (decay exponent). Excel’s LINEST function on logged values provides a straightforward method for estimating these coefficients, yielding representative curves such as \(r(n)=0.396\,n^{-0.472}\).
Once fitted, the curve serves multiple forecasting purposes. It predicts daily active users for any future day through a recurrence relation that incorporates the retention rate and cumulative DAU. It also enables calculation of player duration by summing \(r(n)\) over time, which feeds directly into lifetime value (LTV) estimates. For instance, with an average revenue per daily active user of \$1.00, the model projects LTV90 around 7.65 and LTV180 near 10.17, illustrating how early retention translates into long‑term monetization.
The methodology extends to benchmarking against industry standards. By comparing a studio’s day‑1, day‑7, day‑30, and day‑90 retention figures to genre‑specific percentiles provided by GameAnalytics, developers can identify performance gaps and prioritize optimization efforts. The approach is applicable across mobile game segments worldwide and covers the entire post‑install period, offering a scalable tool for forecasting, monetization planning, and competitive analysis.
