Game Analysis

Game Event Performance Evaluation

Quantitatively evaluate the true incremental impact of events like launches, limited-time events, recharge rebates, lapsed-player win-back, and crossover collaborations, distinguishing organic growth from event-driven growth.

IndustriesGames

Agent · Skills Live

skill · loadedindustry+100

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Metric overview

live

DAU

48.2k

+6.4%

Payers

3.1k

+2.1%

ARPU

$0.72

−1.3%

Analysis readyevidence attached

Game Event Performance Evaluation is an event-operations capability in ThinkingAI's industry Skill library. It helps game operations teams quantitatively evaluate an event's true impact after it ends across user growth, activity, retention, monetization, ROI, and more, distinguishing organic growth from event-driven incremental growth, so overall organic fluctuation is not mistaken for event results. This is ThinkingAI's decade of industry experience, packaged into ready-to-use Skills.

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The Lunar New Year limited-time event is over. GMV is up 45% in the reports, and my boss wants me to confirm how much of that 45% actually came from the event: don't credit organic growth to the event. While you're at it, check the quality of the new users we bought.

Set the baseline: take the 7 days before the event as the organic period, calibrated against the same-genre market

Organic growth over the same period: GMV +18%, DAU +11%

Pull the event-period core metrics (GMV / DAU / penetration / ARPPU) and compare each against the baseline

New vs existing split: compare event-period new users against organic new users on retention and payment with the same definitions

Event new users' next-day retention 28%, only two-thirds of organic new users'

Rebuild the event participation funnel (impression → participation → threshold met → redemption → payment)

The break is at the 3-day cumulative threshold: only 22% reach it

Generate an incremental-attribution conclusion and structural recommendations for the next event

The review is done. Of the +45% in the reports, only 27 points are true event lift; the remaining 18 points are organic Lunar New Year growth. The total hits the mark, but the structure of the growth is a problem: the money mostly comes from existing users, and the new users you bought take their rewards and leave.

1. True lift after removing organic growth

Metric	Baseline period	Event period	Apparent change	True lift
Daily avg GMV	$385K	$559K	+45%	+27% (organic +18%)
Daily avg DAU	200K	236K	+18%	+7% (organic +11%)
ARPPU (daily / paying user)	$47	$56.4	+20%	Mostly event-driven
Payment penetration	4.1%	4.2%	+0.1pp	Essentially flat

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Breaking down the growth engine: ARPPU +20% while penetration barely moved: the GMV lift comes mainly from existing users spending more per head, not from a wider paying base. For reporting up, use the 'true lift +27%' framing; the 45% will fall apart the next time there's no Lunar New Year tailwind.

2. New vs existing split: the quality of the growth

Metric	Event-period new users	Organic new-user benchmark	Gap
Next-day retention	28%	42%	−14pp
7-day retention	12%	23%	−11pp
First-week payment conversion	3.2%	6.8%	−3.6pp

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3. Event participation funnel: where the break is

Lunar New Year event participation funnel (all impressed users)

Event page impression

100%

Joined event tasks

64%

Met 3-day cumulative threshold

22%

Redeemed core reward

19%

In-event payment conversion

The break is very concentrated: 64% of users joined the tasks, but only 22% met the '3-day cumulative check-in' threshold, and event new users' next-day retention is only 28%, so most new users simply don't survive to day 3. The threshold design is mismatched with the new-user lifecycle.

Recommendation

Three takeaways: (1) Switch reporting and evaluation to the true-lift +27% framing; the baseline method is now fixed and reusable. (2) For the next event, change the 3-day cumulative threshold to 'claim on day 1 + escalating rewards': there's precedent where a similar change lifted threshold completion from 22% to 78%, shifting rewards from volume-chasing to habit-building. (3) Put the 12% of 7-day-retained new users from this event into their own cohort for follow-up engagement, so the traffic you paid for doesn't churn a second time.

The incremental-evaluation baseline is saved as a template and can be applied directly to the next event review.

On your data

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The problem

Game operations teams make two classic mistakes in event reviews. First, counting organic growth as event results: over 60% of teams directly credit the GMV or DAU growth during an event to the event, when 30-50% of it may be overall organic growth. Second, single-dimension evaluation: looking only at GMV and not retention, or only at DAU and not payment penetration, leading to the conclusion that an event succeeded while its long-term metrics worsened.

What it does

Incremental thinking: automatically establishes a comparison baseline between the event period and the baseline period, strips out organic growth, and reconstructs the event's true incremental contribution

Six-dimension evaluation framework: user growth, activity, retention rate, monetization performance, ROI, and long-term impact, comprehensive rather than one-sided

Evaluation model matched to event type: launches, limited-time events, recharge rebates, lapsed-player win-back, and crossover collaborations each have dedicated evaluation logic

When to use it

Performance review and incremental attribution after a limited-time event ends

First-week data evaluation and retention-trend forecasting for a launch event

ROI calculation and payment-penetration evaluation for a recharge-rebate event

Return-rate and re-churn-rate evaluation for a lapsed-player win-back event

Brand-exposure conversion and new-user quality evaluation for a crossover collaboration event

In the field

Case

A tower-defense game · Lunar New Year limited-time event review

Operations reported GMV grew 45% during the event. After establishing a baseline, the Skill found 18% organic growth over the same period, making the true event increment 27%; new users from the event had 28% day-1 retention, below the 42% of organic new users, with only 12% day-7 retention; on the monetization side, ARPPU rose 20% but penetration rose only 3%. Based on this, the team adjusted subsequent events to reduce reliance on short-term rewards and add long-term habit-building mechanics.

FAQ

How long does an event evaluation take?

Data collection usually takes 1-2 hours and analysis about 30 minutes; a complex event may need half a day for a complete evaluation.

Can I evaluate before the event ends?

You can do a mid-event evaluation of core metric trends, but final incremental attribution requires comparing against baseline-period data after the event ends.

How is the baseline period determined?

The Skill recommends a baseline by event type: the prior 7-day organic period for limited-time events, historical launch data for the same genre for launch events, and the 30 days before win-back for win-back events.