Monetization Analysis

Game Revenue Forecast

Run a dynamic revenue forecast model on real-time AE data (DAU/DNU/ARPU/retention), supporting forward prediction, reverse solving, and dual-lever optimization, moving game operations from setting targets by gut feel to computing the path with data.

IndustriesGames

Agent · Skills Live

skill · loadedindustry+100

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The Game Revenue Forecast Skill is a revenue prediction and path planning capability in ThinkingAI's industry Skill library. It helps game operations teams run a dynamic revenue forecast model on real-time AE data (DAU/DNU/ARPU/retention rate), supporting forward prediction (revenue trend over the next X months), reverse solving (the DNU/ARPU needed to hit a target revenue), and dual-lever optimization (the best mix of acquisition and operations), moving from setting targets by gut feel to a precise planning mode that computes the path with data. This is ThinkingAI's decade of industry experience, packaged into ready-to-use Skills.

Live walkthrough

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The boss set a Q4 monthly-revenue target of $9M, and our SLG is now at $6.89M monthly and still sliding. Work out whether we can actually hit it as things stand, and if not, by how much we fall short and how to make it up.

Three-path data collection: prefer reusing existing dashboards

Reused the "Core Overview" dashboard; DAU/DNU/ARPU/retention all available

Extract model parameters

DAU 62,000, DNU 1,350, ARPU $3.7, daily churn rate 2.4%

Forward forecast: extrapolate the status quo to October

October monthly revenue about $6.27M, a $2.73M gap to target

Reverse solve: the DNU / ARPU combinations needed for the $9M target

Dual-drive optimization: search across the three levers of UA × operations × retention

Model's done. First, the number the boss wants: coasting into Q4 as things stand, monthly revenue slides to $6.27M, $2.73M short of the $9M target (a 30% gap). But split across three levers pulled together, the target is within reach.

1. Forward forecast: where the status quo slides to

Monthly revenue actual and forecast (holding current parameters)　Unit: $10K

Actual monthly revenueForecast (status quo)

Model parameters come from the last 7 days: DAU 62,000, DNU 1,350, ARPU $3.7, daily churn rate 2.4%. Holding this set, DAU converges toward a steady state of 1,350 ÷ 2.4% ≈ 56,250, and monthly revenue flattens after settling at $6.27M in October. It won't crash, but there's a fixed, unbridgeable gap to $9M.

2. Reverse solve: what combination $9M requires

Path	DNU	ARPU	Daily churn	Assessment
A. Pure UA	1,350 → 1,950 (+44%)	$3.7	2.4%	Budget +44%, CPI likely rises during the ramp
B. Pure operations	1,350	$3.7 → $5.33 (+44%)	2.4%	Monetization depth alone, unrealistic
C. Dual-drive (recommended)	1,350 → 1,600 (+19%)	$3.7 → $3.94 (+6.5%)	2.4% → 2.1%	Each of the three levers carries part, highest feasibility

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Path C check: steady-state DAU = 1,600 ÷ 2.1% ≈ 76,190, × ARPU $3.94 ≈ $300K/day, i.e. $9M monthly, hitting the target right on the line.

3. Execution breakdown of the recommended path

UA side: DNU 1,350 → 1,600; at the current CPI of $33, daily budget goes $45K → $53K, watching that marginal CPI stays under $36

Operations side: ARPU +6.5%, carried by the Q4 season pass and leaderboard-push bundle schedule, without touching base pricing

Retention side: daily churn 2.4% → 2.1%, corresponding to about +3pp on new-user 7-day retention, via a dedicated onboarding-optimization effort

Conclusion

The target is reachable, but none of the three levers can be missing: UA alone needs DNU +44%, operations alone needs ARPU +44%, and betting on any single one will crash. Set monthly checkpoints: if any of DNU, ARPU, or churn achieves less than 80% of plan in a month, trigger the contingency and recompute the path.

The forecast model and parameter snapshot are saved, with automatic recomputation on the 1st of each month and a pushed variance report.

On your data

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The problem

Revenue forecasting is a core input to operations planning, yet more than 80% of teams still set targets by gut feel. They don't know the current retention curve's decay parameters, don't know the actual ARPU trend, and don't know the relationship between acquisition cost and new-user retention, so experience-based forecasts are off by an average of more than 40%. Reverse solving is an even larger blind spot: questions like "to hit 500K daily revenue, how many new users or how high an ARPU do I need" have no calculation tool behind them and can only be trial-and-error guesses.

What it does

Three-path data collection: prefer existing Dashboard data, then Builder-generated queries, and finally a manual Ad-hoc fallback. No guessed parameters

Forward prediction: enter current DAU/DNU/ARPU/churn rate to predict the revenue trend over the next 180 days

Reverse solving: enter a target revenue to back-calculate the DNU and ARPU combination path required

Dual-lever optimization: adjust acquisition (DNU) and operations (ARPU/retention) parameters together to find the optimal path to the target

Automatic environment setup: Python dependencies (numpy/scipy) are detected and installed automatically so the user never sees an error

When to use it

You need to forecast the revenue trend over the next 3 to 6 months for budget planning

You need to reverse-calculate the DNU and ARPU required to hit a target revenue

You need to assess the optimal combination of acquisition spend and operations optimization

You need to predict the future DAU trend from retention rate

You need to estimate user LTV to inform campaign decisions

You need to do campaign budget planning and an acquisition plan

In the field

Case

An SLG game · next-quarter revenue target planning

The team estimated by experience "300K daily revenue, 50K/day acquisition budget." With the Game Revenue Forecast Skill, the system collected the past 7 days of DAU/DNU/ARPU/retention data and extracted DAU 8000, DNU 1500, ARPU 3.75, and a daily churn rate of 2.1%. Forward prediction showed 180-day revenue holding steady with a slight decline; reverse solving gave several target paths, and the team chose a dual-lever plan of DNU 1800 plus ARPU 3.5, adjusting the acquisition budget from 50K to 36K.

FAQ

What is the difference between forward prediction and reverse solving?

Forward prediction is "given current parameters, predict the future result"; reverse solving is "given a target result, back out the parameters required." The former suits trend forecasting, the latter suits target planning.

Where does the data come from?

All data comes from ae-cli queries against the AE analytics platform. DAU/DNU/revenue/retention rate are all real-time data, with no guessing and no manual entry.

How does this differ from the LTV Curve Fitting and Segmented Calculation Skill?

Revenue Forecast focuses on total revenue trends and target-path planning, while LTV Curve Fitting and Segmented Calculation focuses on single-user lifetime-value estimation. They complement each other.

Precise LTV through curve fitting

LT Prediction (User Lifetime Prediction)

Estimate average user lifetime in days

LTV Payback Period Prediction

Predict whether spend will pay back

LTV Analysis and Monitoring

Diagnose the root cause of LTV drops