O2O Data Quality Assurance Process for Online Movie Seat Selection

1. Background

Online movie seat selection is a typical O2O service where users purchase tickets online and watch movies offline, but it demands higher real‑time data and service responsiveness than traditional O2O businesses, requiring up‑to‑date schedule data and instant seat‑locking, payment, and ticketing.

The consumption chain is often separated by time and location, leading to data anomalies such as changes or inconsistencies that can affect user experience, generate complaints, or trigger refunds.

Therefore, technical methods are needed to proactively detect these consumption‑chain data anomalies to ensure data correctness, consistency, and timely user notifications.

2. O2O Data Quality Assurance Process

The process consists of three main parts:

Basic data preparation: Analyze user order data and competitor schedule data to obtain baseline monitoring data.

Real‑time data analysis: Compare data from different partners and competitors to identify schedule errors or inconsistencies, drive data fixes and configuration upgrades, and assess order‑schedule mismatches to trigger timely customer service actions.

Issue distribution mechanism: Route identified problems, reasons, and severity levels to appropriate personnel (R&D, PM, BD, operations, sales, customer service, etc.).

3. Data Quality Assurance Evaluation Standards

Two dimensions are used to evaluate the effectiveness of data quality work:

Data‑related complaint ratio: number of data‑related complaints divided by total product complaints.

Data Quality Score (Q): Accuracy × 30% + Recall × 30% + Closed‑loop rate × 40%, measuring the capability and maturity of data quality measures, including timely and accurate issue detection and resolution.

Formulas:

Accuracy = Recalled issues / Total alerts

Recall = Recalled issues / (Recalled issues + Complaint issues)

Closed‑loop rate = Resolved issues / Recalled issues

4. Concrete Case Study

The case "Order schedule vs. real‑time schedule inconsistency" illustrates the workflow:

Root cause : After a user places an order, a partner updates the schedule, causing a mismatch between the ordered schedule and the cinema's actual showing.

Initial detection plan : Compare order schedule with real‑time schedule over a time window and raise an alarm on mismatches.

Challenge : Some schedule changes are only updates from partners without actual changes in the cinema's program, leading to many false positives.

Improved solution : Incorporate competitor schedule data to verify whether a schedule truly changed, improving accuracy.

Issue handling : After confirming the problem, send an email to customer service, who contacts the cinema for final confirmation and then notifies the user to mitigate loss.

Images illustrating the process flow are included in the original document.