Summary

If your LTV Cohort customer counts don't match the New Customer counts reported in Shopify, it’s typically due to differences in how each platform defines and filters new customers. While Shopify includes every first-time customer regardless of order value, Triple Whale’s Cohort dashboard applies more strict inclusion criteria—such as excluding $0 orders and filtering by global settings—resulting in fewer new customers reported in some cases.

Why This Happens

Several key differences explain why your new customer counts may be lower in Triple Whale's LTV Cohorts view compared to Shopify:

• $0 Orders Are Excluded
  Customers whose first purchase total was $0 are excluded from LTV Cohorts, but included in Shopify reports.

• Incomplete Data History
  If your store's historical order or customer data wasn't fully imported, some returning customers may appear as new in Triple Whale but not in Shopify.

• Global Filters in Triple Whale
  Filters applied in the Triple Whale app—such as by channel, campaign, product, or date range—can narrow the visible customer set, reducing the number shown in the Cohorts dashboard. Shopify does not apply these filters by default.

• Customer ID Requirements
  Cohort reporting may exclude customers with missing or invalid IDs, which are sometimes present in older or imported order data.

• Differences in Cohort Logic
  LTV Cohorts group customers based on the month of their first valid tracked purchase (Month 0). Shopify’s “new customer” metrics may use different criteria based on store-side order timestamps and cookies.

Example Scenario

A brand sees the following discrepancy:

• Shopify shows 2,000 new customers in July.
• Triple Whale LTV Cohorts shows 1,725 customers in the July cohort.

Breakdown:

• 125 customers placed $0 orders and are excluded by Triple Whale.
• 100 customers had their first order before historical data was imported.
• 50 customers were filtered out due to active product or channel filters in Triple Whale.

After filters are cleared and data is reimported, the count adjusts and aligns more closely.

How This Affects Reporting

• LTV May Be Skewed
  If some valid new customers are excluded from the cohort, the average LTV may appear artificially higher or lower depending on which types of customers were filtered out.

• Misaligned Cohort Sizes
  Shopify’s “new customer” reports may show a larger cohort size, which can lead to confusion when comparing metrics unless you account for platform differences in filters and inclusion criteria.

• Unexpected Differences Across Reports
  Comparing new customer counts across platforms without understanding these differences may lead to incorrect assumptions about growth or performance.

How to Interpret the Data Correctly

• Use LTV Cohorts for Trend Analysis
  The Cohorts dashboard is designed to analyze revenue and retention over time, not to serve as a raw count of new customers. Use it to monitor lifetime value trends, not to match Shopify totals exactly.

• Check for Exclusions
  Be aware that $0 orders and customers without valid IDs are excluded. These differences alone often explain much of the variance from Shopify.

• Review Global Filters
  Make sure no filters (e.g., campaign, channel, product) are hiding new customers from view. Clear all filters to see the total unfiltered cohort.

• Request a Data Reimport if Needed
  If you suspect historical order or customer data is missing, contact support to request a full data reimport, which can correct customer classification and cohort grouping.

• Compare Trends, Not Totals
  Instead of trying to match Shopify’s new customer counts exactly, focus on month-over-month LTV growth, retention, and behavior within Triple Whale.

By understanding how Triple Whale defines and filters new customers in the LTV Cohorts dashboard, you can confidently use it to analyze customer value and retention trends—even if the totals differ slightly from Shopify.