LiveOps Analysis

Channel Quality Analysis

Compare retention, payment, and ROI performance across channels side by side to precisely identify good and bad channels, giving budget allocation a data basis.

IndustriesGamesE-commerce

Agent · Skills Live

skill · loadedindustry+100

Run this Skill on my data

Segmentation

by cluster

Analysis readyevidence attached

Channel Quality Analysis is a channel-analysis capability in ThinkingAI's industry Skill library. In multi-channel campaigns, it helps game and e-commerce operations teams compare core metrics like CPI, retention rate, payment rate, LTV, and ROI across channels side by side, identify good channels and diagnose the root cause of bad ones, giving budget-allocation optimization data support. This is ThinkingAI's decade of industry experience, packaged into ready-to-use Skills.

Live walkthrough

AgentSave as AgentExport session

channel-quality-analysis

Compare the quality of 5 UA channels over the last 30 days. Right now budget is allocated by CPI, and channel A is cheapest so it takes 40%, but something feels off: ROI just won't lift.

Align definitions: unify the time window (last 30 days), unify the LTV model, exclude organic volume

Call ae-analysis to pull six metrics by channel: CPI / next-day retention / payment rate / 7-day LTV / ROI / activity

Cross-compare and rank by quality

Fraud-signal detection: cross four signals: day-1 retention, payment rate, behavioral-event richness, new-user vs retention divergence

Channel A hits all four signals at once

Generate budget reallocation recommendations

Your instinct is right. Channel A is the lowest-quality channel of the lot, and its lowest CPI is its only merit, yet it's holding 40% of your budget.

1. Six-metric comparison across five channels

Channel	CPI	Next-day retention	Payment rate	7-day LTV	ROI	Spend share
Channel A	$3.2	15%	0.3%	$5	0.67	40%
Channel B	$8.4	42%	2.1%	$28	3.5	20%
Channel C	$5.5	31%	1.2%	$14	2.1	18%
Channel D	$12.1	38%	1.8%	$25	1.9	12%
Channel E	$6.8	24%	0.8%	$9	1.1	10%

← Scroll to see more

CPI vs 7-day LTV by channel　Unit: $

CPI ($)7-day LTV ($)

Ranking by CPI versus by ROI produces almost exactly opposite results. Channel B's CPI is 2.6× channel A's, but its LTV is 5.6× as high. Cheap volume isn't saving money, it's spending money on users who won't pay.

2. Channel A's fraud suspicion

Next-day retention 15% against a base average of 28%+, clearly anomalous

Payment rate 0.3%, near zero

Monotone behavior: 80% of channel A's new users triggered only the login event, no combat, no progression

New-user volume is stable but the 7-day retention curve diverges in shape from other channels

All four signals hit at once, so channel A's traffic very likely contains fraud, with fake traffic estimated at 20–30% of its new users.

3. Budget reallocation recommendation

Channel	Current share	Recommended share	Rationale
Channel A	40%	10%	ROI<1 and continuously loss-making; keep a small amount for control observation
Channel B	20%	45%	ROI 3.5 and volume not yet saturated; prioritize scaling
Channel C	18%	20%	Stable quality, modest increase
Channel D	12%	15%	High LTV but expensive CPI; scale to test marginal ROI
Channel E	10%	10%	Hold and observe, ROI borderline

← Scroll to see more

Conclusion

Under this plan, overall ROI is projected to rise from the current 1.4 to above 2.3. Two reminders: (1) channel B's CPI may rise after scaling, so watch marginal ROI weekly; (2) simultaneously open a settlement reconciliation and traffic-quality dispute with channel A.

The six-metric comparison report is saved to the dashboard and set to refresh automatically every Monday.

On your data

That was a simulated run

Leave your work email and we will run a live walkthrough on your real business data.

The problem

The most common bind for acquisition teams is "spending money without knowing if it was spent well." More than 60% of teams allocate budget by CPI as the only ranking metric, sending an average of 35% of budget to bad channels with ROI below 1. Low CPI doesn't mean a good channel; low-price channels often have worse retention, payment rate, and LTV. Fraudulent traffic from bot channels can reach 20% to 30% of total new users, continuously burning budget without delivering real user value.

What it does

Three-principle assessment: comparability, representativeness, and sustainability, avoiding single-day and wrong-definition misjudgments

Full-metric side-by-side comparison: quantify channel quality across six dimensions, CPI, retention rate, payment rate, LTV, ROI, and activity

Bad-channel root-cause diagnosis: distinguish three root causes, poor acquisition quality, skewed user behavior, and technical issues

When to use it

Side-by-side quality comparison and ranking after a multi-channel campaign

Root-cause diagnosis when a channel's ROI drops abnormally

Monthly or quarterly channel budget-allocation decisions

Quality trial assessment before onboarding a new channel

Identifying bot channels and alerting on fraudulent traffic

In the field

Case

A game company · reallocating budget across 5 channels

The team ranked by CPI and gave 40% of budget to channel A. After comparing six-dimension metrics across 5 channels, the Skill found channel A had a CPI of just $3 but next-day retention of 15%, 7-day LTV of $5, and ROI of 0.67, while channel B had a CPI of $8 but next-day retention of 42%, 7-day LTV of $28, and ROI of 3.5. The team cut channel A's budget from 40% to 10% and raised channel B's from 20% to 45%, reaching an actual ROI of 2.6x the next month.

FAQ

How much data does channel comparison need?

We recommend at least 7 days for a preliminary comparison, 14 days to assess the retention trend, and 30+ days to compute LTV and ROI. A new channel's trial can start with 3 days of day-one and next-day retention.

How do you identify a bot channel?

Judge from a combination of signals: abnormally low day-one retention, a payment rate of 0, single-type behavioral events, and new-user volume diverging from the retention trend.