Lifetime Value (LTV) Cohort Report Overview

Discover how much your customers are really worth over time - and where to focus your marketing spend.

The Lifetime Value (LTV) Report groups customers by when they first ordered and shows how their value grows month by month. With this view, you can:

See which cohorts deliver the highest repeat revenue
Measure how quickly you recover acquisition costs
Make smarter decisions on where to invest and scale

What’s New

We’ve introduced several updates to make the LTV Report easier to use and more insightful:

Ask an AI button – Get instant answers about your data without running extra filters or reports.
Tooltips on cohort titles – Hover to see quick explanations and definitions.
Fresh visuals – Updated colors and chart legends for clearer comparisons.
Predictive vs. Historical averages – Easily distinguish past performance from AI-modeled forecasts.

Reading & Comparing the Report

Each row in the report represents a cohort - customers grouped by the month, quarter, or year they made their first purchase.

Cohort summary (left columns):

New Customers – How many first-time buyers entered that month?
CAC (Customer Acquisition Cost) – Your average spend to acquire each customer.
R-% (Repeat Rate) – The percentage who placed at least one more order.

Value over time (right columns):

First Order (0 months) – Average spend on their first order.
Accumulated Value Growth – How customer value increases as they repurchase.
Final Column (e.g., Month 6) – Total per-customer value at the end of your selected timeframe.

Tip: Watch how these numbers grow over months and compare across cohorts. Are newer customers spending more? Repurchasing less?

The spaghetti chart view gives a visual way to track and compare LTV growth across cohorts. Hover over any point to see exact values for that month.

Customizing the Report

You have complete control over how the data is displayed - from timeframes to metrics and filters.

Time Period & Timeframe

Choose the range you want to analyze (up to 4 years).
Group cohorts by week, month, quarter, or year for the level of detail you need.
Use shorter timeframes for recent campaigns, longer ones for steady long-term trends.

Metrics

Default metric: Accumulated Sales per Customer
Other options: Gross Margin, Net Sales, Orders, Contribution Margin, and more.
Toggle between currency and percentages with a single click.

Filters

Focus on exactly what matters by filtering cohorts by:

Marketing channel (e.g., Facebook, affiliates)
Products or collections purchased
Geography (country or state)
Tags or discount code usage
Sales channel (e.g., in-store vs. online)

Save commonly used filter combinations as Saved Cohorts for faster future reporting.

CAC Payback Tool

Understanding when you recover your acquisition costs is critical.

When a cohort’s gross margin surpasses CAC, you’ve broken even - marked by a green indicator on the timeline.
No green marker? That cohort isn’t profitable yet.
Marker at the start? You were profitable from day one.

Average LTV vs Historical Average LTV

The difference in the LTV average depends on whether the calculation is cohort-based or customer-based.

LTV Average Row (Cohort-based): This method calculates the average for an entire group (or cohort) of customers based on their sign-up month. It includes all purchases made by these customers within a specific time frame, like 3 months, even if a purchase falls slightly outside the exact 3-month window from an individual customer's first order. This gives you a broad, month-by-month view of the cohort's performance.

Historical LTV Averages Section (Customer-based): This calculation is more precise. It looks at each individual customer and measures their LTV based on a strict time period, such as exactly 3 months from the date of their very first order. Because it's so specific, the result can sometimes be slightly lower than the cohort-based average, as it excludes any purchases that fall even one day outside that exact time frame.

Weighted vs. True Average

The second difference is how the final average is determined.

LTV Average in the Table (Weighted Average): This method takes into account the size of each cohort. Larger cohorts (with more customers) have a greater impact on the final average shown in the table. This is useful for understanding the overall trend, as it gives more weight to the groups that represent a larger portion of your customer base.

Historical LTV Average at the Bottom (True Average): This is a simple, straightforward average. It adds up the total LTV for all customers and divides that sum by the total number of customers. It provides a simple, unweighted number that shows the average LTV across the entire customer base, without giving more importance to larger groups.

Tips & Key Metrics

Best Practices

Compare predictive vs. historical LTV to spot where future value may outpace past trends.
Look beyond averages — repeat rate (R%) shows how many customers come back, while accumulated value shows how much they spend.
Shorter cohorts help you test new marketing strategies quickly; longer ones reveal long-term behavior.
Monitor CAC vs. value to keep acquisition costs sustainable.

Key Metrics (Quick Reference)

CAC – Marketing spend per new customer
R-% (Repeat Rate) – % of customers placing at least one more order
Accumulated Sales per Customer – Total sales ÷ total customers in cohort (over time)
Predictive LTV – AI-estimated forecast of future cohort value

Using the Ask an AI Feature

New and highly recommended: Let AI analyze your cohorts for you.

Click the Ask an AI button in the LTV report header.
Choose from suggested questions (or type your own).
Get instant, data-informed answers - copy, give feedback, or regenerate if needed.
Clear the chat anytime to start fresh.

This is perfect for spotting trends or asking “why” questions without manually slicing data.

You’re All Set!

With the LTV Report, you can:

See how customer value evolves over time
Pinpoint high-performing segments and campaigns
Understand when your acquisition costs are repaid
Use AI-powered insights for faster, smarter analysis

Need more help? Reach out via in-app chat or visit our Help Center.