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User Data and E-commerce

Data Quality Monitoring

Probr automatically analyzes the quality of data flowing through your sGTM container. This page explains the quality metrics and how to interpret them.

User Data Quality (Enhanced Conversions)

Why It Matters

Enhanced Conversions for Google Ads, Meta CAPI, and other platforms depend on the presence of first-party user data:

  • Email -> user matching for attribution
  • Phone -> complementary signal for matching
  • Address (first name, last name, city, country) -> improves match rate

Low presence rate = low match rate = fewer attributed conversions = underestimated ROAS.

What Probr Checks

For each event, Probr checks the presence (non-empty) of the following fields:

Field Path in sGTM event Impact
Email user_data.email_address Critical — primary signal for matching
Phone user_data.phone_number Important — improves match rate by ~15%
First name user_data.address.first_name Useful — address matching
Last name user_data.address.last_name Useful — address matching
City user_data.address.city Complementary
Country user_data.address.country Complementary
Metric Target Critical if below
Email presence rate >70% on conversions <40%
Phone presence rate >30% <10%
Address presence rate >50% <20%

These targets apply to conversion events (purchase, generate_lead, sign_up). It's normal for page_view events to have a lower rate.

How to Improve Rates

If your rates are low:

  1. Check your client-side dataLayer: is user_data being pushed to the dataLayer before the conversion event?
  2. Check the sGTM client: does the client (GA4, custom) properly transmit user_data in the event data?
  3. Forms: ensure your checkout/login forms collect this data and make it available in the dataLayer
  4. Consent: if you use a CMP, verify that marketing consent is granted before sending user data

E-commerce Data Quality

Why It Matters

Incomplete e-commerce data causes:

  • Loss of revenue tracking in GA4 if value or currency is missing
  • Impossible deduplication if transaction_id is missing (purchases counted twice)
  • Broken item reports if items is missing (no product performance)

What Probr Checks

On purchase, begin_checkout, add_to_cart, and add_payment_info events:

Field Path Impact if absent
value value Revenue not tracked
currency currency Revenue in unknown currency
transaction_id transaction_id Risk of duplicates
items items No product data
Metric Target
value present on purchase 100%
currency present on purchase 100%
transaction_id present on purchase 100%
items present on purchase >95%

On add_to_cart and begin_checkout, a 100% rate for items is expected. For value, it may vary depending on your implementation.

Quality Dashboard

In the Probr dashboard, the Data Quality tab displays:

  1. Overall score: weighted completeness percentage
  2. Trends: presence rate evolution over 7/30 days
  3. Alerts: notification if a rate drops below the critical threshold
  4. Detail by event: breakdown by event type (purchase vs page_view vs add_to_cart)

Interpreting the Overall Score

Score Interpretation
90-100% Excellent — your implementation is solid
70-89% Good — some improvements possible
50-69% Warning — data is regularly missing
<50% Critical — implementation issue to fix
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