ENSURING CLEAN DATA FOR LOOKALIKE AUDIENCES

Ensuring Clean Data for Lookalike Audiences

Ensuring Clean Data for Lookalike Audiences

Blog Article

Key Takeaways

  • Remove duplicates, incomplete, and low-value records

  • Standardize email and phone formats automatically

  • Validate data quality before seeding audiences

  • Automate monthly cleanses for ongoing accuracy

Your lookalikes are only as good as your seed list. Dirty or duplicate data skews Facebook’s matching algorithm—wasting budget on bad leads.

  1. Normalize & Validate
    Use QuickAds.ai’s Data Cleaner (via CSV upload) to standardize phone numbers (E.164), lowercase emails, and strip out invalid entries. The tool flags malformed records—so you can remove or correct them before audience creation.

  2. Deduplicate Automatically
    Duplicate customer entries inflate lookalike pools. QuickAds.ai merges records with matching identifiers and retains the highest-value profile—ensuring a precise seed list.

  3. Score & Filter
    Assign values based on recency, frequency, and monetary metrics (RFM scoring). Exclude low-value leads (e.g., one-time minimal buyers) so Facebook’s algorithm learns from your best customers only.

  4. Automate Monthly Cleanses
    Schedule recurring data cleansing tasks in QuickAds.ai—ensuring your seed lists remain pristine as new customers flow in. Clean data drives tighter lookalikes, lower CPAs, and higher ROAS.

Clean, validated seed data is the foundation of predictive lookalikes—ensure yours is impeccable to unlock peak audience performance.

Report this page