Lifetime Value Analysis

Lifetime Value Analysis breaks down customer revenue by how customers answered a specific survey question. Each answer option becomes its own row, so you can see — for any audience segment captured in a survey — how much they're worth on day one, how much they return for, and how their cumulative spend grows month by month.

Getting started

1. Open Lifetime Value Analysis from the navigation.
2. Pick a date range. The range is always in whole months — the start and end months are both fully included. The first month is the acquisition month (see below).
3. Pick a survey and a question from the configuration sidebar on the right.
4. Choose a metric to display in the monthly columns: LTV, Total revenue, Orders count, or Average order value.

If the sidebar is closed, click the adjust icon in the top-right to reopen it.

How the cohort is defined

For each answer option, the cohort is fixed to the first calendar month of the date range. A customer is included in that option's cohort only if both:

1. They answered the question with that option during the first month, and
2. They placed at least one Shopify order during the first month.

Customers who responded or were acquired in any later month are excluded entirely — even if their orders fall within the date range. This is what gives the LTV curve a stable denominator: the cohort size doesn't change as months are added.

A customer who chose multiple options (multi-select) is counted in each option's cohort independently.

Reading the table

Each row is one answer option. Range-type questions show one row per integer between the question's minimum and maximum (e.g. 1 through 5).

Summary columns

Column	What it means
Response	The answer option (or Other if the question allows free-form responses).
LTV	Total revenue from the cohort across the date range, divided by the number of unique cohort customers.
Total customers	Unique customers in the cohort.
Acquisition	Total revenue from the cohort during the first month only.
Repeat	Total revenue from the cohort across every month after the first month. Acquisition + Repeat equals total revenue (not LTV).

Monthly columns

The monthly columns (Jan 2025, Feb 2025, …) are cumulative through the end of that month, computed from the cohort defined above. The value in each cell depends on the selected metric:

Metric	Cell shows
LTV	Cumulative revenue ÷ unique cohort customers.
Total revenue	Cumulative revenue (sum of order totals).
Orders count	Cumulative order count.
Average order value	Cumulative revenue ÷ cumulative order count.

Because the cohort is fixed to the first month, LTV, Total revenue, and Orders count are monotonically non-decreasing left to right. Average order value can move in either direction as repeat orders shift the per-order average.

Heatmap shading

Monthly cells are color-shaded across the full visible range — the lowest non-zero value in the table anchors the lightest shade, and the highest value anchors the darkest. This makes variation visible even when all values are large. Cells with no data are left blank.

What's included and what isn't

• Only customers identified by a numeric Shopify customer ID are counted. Responses without a matched customer are ignored.
• The Other row appears only if the question is configured to accept "Other" responses.
• Options with no matching cohort still appear as a row with zero values — useful for confirming the option is being seen.
• Orders are bucketed by the account's configured time zone.

Tips

• Compare Acquisition vs Repeat side-by-side to see which audience segments come back. High Acquisition but low Repeat suggests one-time buyers; a healthy Repeat number indicates loyalty.
• Use LTV when comparing options with very different cohort sizes — it normalizes by customer count.
• Use Average order value to compare per-purchase value across options, independent of how often customers come back.
• Widen the date range to see longer LTV curves. Shorten it to focus on a more recent cohort.