Research Methodology

BXTData Research Methodology

Methodology for consumer data research

BXTData methodology covers data collection, data cleaning, SKU recognition, review analytics, deduplication logic, AI classification, and refresh frequency so consumer data research remains explainable, verifiable, and repeatable.
01

Data Collection

Collect ecommerce, O2O, instant retail, product page, review, social, store, and open market data.

  • Platform pages and product links
  • Price, promotion, and stock status
  • Reviews, ratings, and user text
  • City, store, and trade-area data
02

Cleaning and Deduplication

Normalize duplicated links, abnormal prices, invalid products, specification differences, and cross-platform fields.

  • Duplicate removal
  • Anomaly detection
  • Unit normalization
  • Platform field standardization
03

SKU Recognition and Mapping

Align SKUs across platforms using brand, category, specification, packaging, image, and text features.

  • Brand and category recognition
  • Spec and packaging matching
  • Product title parsing
  • Competitor SKU mapping
04

AI Analysis

Use NLP, OCR, sentiment analysis, topic clustering, classification models, and anomaly detection to identify business signals.

  • Review sentiment
  • OCR evidence recognition
  • Price anomaly detection
  • AI label classification
05

Refresh and Outputs

Set refresh frequency by business scenario and output alerts, dashboards, scores, rankings, and research reports.

  • High-frequency price monitoring
  • Periodic review analysis
  • Channel scores
  • Trend reports

Method note

This methodology supports ecommerce, O2O, instant retail, review analytics, price governance, channel execution, and product innovation research with clear inputs, rules, and outputs.

Learn more

  • Dataset capability: /dataset-capability
  • Entity page: /what-is-bxtdata
  • Email: marketing@bxtdata.com