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updatedAt: 2025-03-02T12:30:12.000Z
agentTools:
  projectIndex: https://triplewhale.readme.io/llms.txt
---

# Why Does My Lifetime Value (LTV) Metric Keep Changing Over Time?

# Summary

Lifetime Value (LTV) represents the total revenue you can expect from each customer over their relationship with your business. Because LTV calculations factor in **all** orders made by these customers—sometimes within a specific window, such as 60 or 90 days—this metric naturally evolves as more purchases occur. Changes in time range selection, additional orders from existing customers, and different product-specific calculations further explain why the number you see for LTV isn’t static.

# Why This Happens

1. **Rolling Cohorts and Additional Purchases**
   * LTV is typically calculated by summing all order revenue from a group of customers and dividing by the number of unique customers in that group.
   * If these same customers make **subsequent purchases**, the total order value grows—thus **raising** (or occasionally lowering) the average LTV over time.

2. **Time Range Selection (60-Day or 90-Day LTV)**
   * In some reports, the LTV focuses on orders placed within 60 or 90 days of a customer’s initial purchase.
   * If a customer makes multiple purchases within that window, **all** those purchases are added to their total, which can cause the LTV to increase as the data updates.

3. **Excluding $0 Orders and Customers Without IDs**
   * Many LTV tools (including Triple Whale’s 60/90 LTV pages) **exclude** orders with a $0 total or orders without valid customer information.
   * If previously untracked orders become valid (e.g., corrections to the order record), LTV may shift because the underlying set of included orders changes.

4. **Product-Specific Filtering**
   * In certain pages, LTV is calculated only for customers who purchased a specific product in their first order.
   * Any **subsequent** purchases (with or without that product) are still added to their lifetime revenue, so LTV can keep rising as these customers place new orders.

5. **Different Definitions in Different Reports**
   * Some pages measure LTV using a “lifetime” approach with no cutoff window, while others focus strictly on a 60- or 90-day period after a first purchase.
   * These variations can make LTV appear to “change,” but it often reflects the same data applied to different time spans or product filters.

# Example Scenario

1. **Day 1**: A new customer buys Product X for $50 (COGS excluded for simplicity).
2. **Day 15**: The same customer returns and makes a second purchase of $30.
   * **60-Day LTV**: Now includes both orders ($80 total) for this one customer, raising the per-customer LTV to $80.
   * If the tool only had the first purchase accounted for at $50, it updates once the second purchase is recognized, causing an apparent “change” in LTV.

# How This Affects Reporting

* **Fluctuating Averages**: If a large cohort of customers makes follow-up purchases, the average LTV can rise significantly over time.
* **Differences Across Tools**: Various dashboards (e.g., the 60/90 LTV page vs. Product Analytics) have unique filters and definitions, so the same customer data can produce different LTV results.
* **Ongoing Recalculation**: Every new order from an existing customer re-enters the formula. This can boost (or occasionally lower) the average if the subsequent orders have lower or higher values than the original purchase.

# How to Interpret the Data Correctly

1. **Identify the Time Window**
   * Confirm whether you’re looking at a **60-day, 90-day, or unlimited** timeframe. Understand that customers who purchase again during that period increase the total.

2. **Check Excluded Orders**
   * Remember that $0 orders or orders without valid customer data may be omitted. Corrections to those records can change your LTV down the line.

3. **Evaluate Product-Specific Views**
   * If the LTV is filtered for a certain product, only the first purchase of that product defines the customer pool. Any subsequent purchases—of any product—will raise their LTV.

4. **Watch for Delayed Updates**
   * If a purchase wasn’t fully captured or has been updated in the system (e.g., an order gets corrected), the LTV might jump once the platform recognizes the change.

5. **Monitor Trends, Not Just a Single Number**
   * LTV is dynamic by nature. Observe how it shifts over weeks or months to gain a more reliable view of customer profitability rather than focusing on a single snapshot in time.

By keeping in mind that LTV is regularly updated with each new purchase—whether the focus is on a 60/90-day period or a product-specific view—brands can better understand why this key metric continues to evolve rather than remaining fixed.