User AnalysisSingle-User Behavior Analysis
See a single user's full lifecycle in one click: profile, behavior sequence, economy flow, progress, and churn attribution, freeing operations and support from piecing users together across systems.
User path
step to stepSingle-User Behavior Tracking is a user-insight capability in ThinkingAI's industry Skill library. It helps operations and support teams quickly get a single user's complete data profile when handling complaints or churn interventions, covering everything from registration info to behavior sequence, economy flow, game progress, and churn cause. This is ThinkingAI's decade of industry experience, packaged into ready-to-use Skills.
Live walkthrough
#account_id, pull the profileorder_create / pay_callback / item_grantitem_grantDone, and it took under a minute from user ID to conclusion. The player is telling the truth: the money really was paid, and it's stuck at the item-grant step: not a payment-channel problem, and not a false report. An abnormal window in the grant service at 14:30–14:35 swallowed two of his orders.
1. Key behavior sequence for the day (14:00–16:00)
| Time | Event | Detail |
|---|---|---|
| 14:12 | pay_callback → item_grant | $648 payment success, 6,480 diamonds delivered |
| 14:21 | pay_callback → item_grant | $648 payment success, delivered normally |
| 14:32 | pay_callback (no item_grant) | Payment success, grant event missing (break point) |
| 14:34 | pay_callback (no item_grant) | Payment success, grant event missing (break point) |
| 14:47 | pay_callback → item_grant | $648 payment success, delivery back to normal |
| 15:02 | Support ticket created | Player reports "top-up never arrived" |
2. Timeline comparison and attribution
Attribution chain: the grant service had a 5-minute abnormal window at 14:30–14:35 (with matching error records in the service log) → orders that completed payment within the window had normal callbacks but had their grant requests swallowed → this player's two orders at 14:32 and 14:34 fell exactly into it → pay_callback present, item_grant absent → on the player's side it shows up as "paid but never arrived." The shortfall of $1,296 corresponds to two orders and can be re-granted directly by order number. More important is the blast radius: the same-window scan found 23 players and 31 orders affected, of which 19 haven't filed a ticket yet, so I'd suggest a batch re-grant plus proactive compensation to head off the remaining tickets before they happen.
On your data
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The problem
When handling complaints or churn interventions, the biggest headache for operations and support is piecing a user together across systems: registration info in the registration table, behavior sequence in the event table, top-up records in the payment table, and game progress in the progress table, switching among five systems and averaging over 15 minutes per user query. A more common pain point is difficult churn attribution: operations receives 20+ high-value churn alerts a month on average but cannot connect signals like declining login frequency, stopped payment, and lower core-gameplay participation into a timeline.
What it does
When to use it
Support quickly understanding the full picture of a VIP user during a complaint
Operations doing attribution analysis on a churned high-value user
Design reviewing a specific user's behavior path to validate gameplay design
Monitoring alerts on activity-status changes of high-value users
Batch-comparing behavior differences between churned and normal users
In the field
FAQ
Can it only query a single user?
The core capability is deep single-user insight, but it supports batch comparison, viewing multiple user profiles and key metrics at once for churned-versus-normal user comparison.
Do I need to know the user ID to query?
You need to provide the #account_id or #user_id in AE. With only partial info, the Skill can help locate the user by searching user properties.
What time range does the data cover?
It queries the past 30 days of behavior data by default, while economy-flow and progress data cover the user's full lifecycle, and you can specify a custom time range as needed.
Related Skills
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