Data Management & ETL

Normalize fragmented data, convert it into customer profiles, and feed segmentation and automation.

Many teams struggle with “we have data but cannot use it.” In Pika, ETL is not just an import screen; it keeps customer profiles continuously updated and transforms transactional data into actionable signals. This makes segment rules and personalization more real.

What does it provide?

  • Normalization: map data from different sources into a common schema.
  • Customer profile updates: contact/customer merge logic.
  • Loyalty/score updates: map purchase and behavior data into scoring.
  • Event generation: trigger automation with critical events like purchases.

How it works (summary):

  1. Data arrives from source systems (POS/e-commerce/CRM).
  2. Validate and normalize.
  3. Update profile (fields, tags, scores).
  4. Update segment memberships.
  5. Publish event; journey/campaign runs.

KPI: import success rate, duplicate ratio, normalization error rate, ingestion-to-trigger time.