The LTV and Subscriptions report has four pages: Overview, Cohort Analysis, Repeat Purchases, and Break-Even ACoS. Together they answer how your brand acquires, retains, and monetizes customers over time, and how aggressively you can afford to advertise given the long-term value of a new customer.
Watch the video above or read an article below to learn what each page is built for.
Overview Page
This is the most data-rich page in the report. It breaks your total sales down into new-to-brand sales, repeat sales, and Subscribe and Save sales, and compares all of them to the prior period.
The Core Metric to Watch
New-to-brand sales are the leading indicator for your brand's future health. Total sales can look stable even when new-to-brand sales are declining, because repeat and Subscribe and Save orders keep the number propped up in the short term. But if new-to-brand sales fall consistently, your subscription count and repeat revenue will start declining a few months later once cancellations outpace new signups. The KPIs at the top of the Overview page make this split visible at a glance.
A note on new-to-brand data accuracy: New-to-brand orders are identified by customer email. This data is only available for FBA orders, and only once an order transitions from pending to shipped status. As a result, new-to-brand figures for the most recent week and the week before it may be incomplete. We recommend avoiding decisions based on last week's new-to-brand numbers specifically. Give the data at least two weeks to fully settle before acting on it.
Charts on the Overview Page
Sales chart (total, new to brand, repeat): Weekly view showing the orange total sales line alongside the new-to-brand and repeat breakdowns. Extend the time range to last 8 or 16 weeks to see when a trend started.
New to brand sales only: Shows new-to-brand as a percentage of total sales week by week. Useful for establishing your brand's normal range. For some brands it's 30%, for others 70%, depending on product category and repeat purchase behavior. A declining trend here is a flag worth investigating.
Sales detailed by order type: Breaks sales into four buckets: new-to-brand Subscribe and Save, regular new-to-brand, repeat Subscribe and Save, and regular repeat. Most sellers are surprised to find that regular repeat (customers who reorder manually without a subscription) typically drives more repeat revenue than Subscribe and Save repeat orders.
Subscribe and Save vs regular sales: Shows the S&S share of total sales over time. Watch this in context: if S&S share spikes upward, it often means new-to-brand sales dropped, not that S&S improved.
Subscribe and Save new-to-brand orders vs price and coupon: Shows how many new Subscribe and Save customers you acquire each week, plotted against your sale price (purple) and coupon percentage (green). This is the chart to use when testing which price and coupon combination acquires more subscribers. You can see whether a lower price with the same coupon outperforms a higher price with a bigger coupon, or vice versa.
Subscribe and Save real vs fake: A subscription is marked "fake" if no second purchase is recorded for that customer. This chart shows what percentage of your new S&S sign-ups are genuine retained subscribers versus one-time buyers who opted in for the discount and didn't reorder. Note: this metric needs time to settle. Avoid reading it for the most recent few weeks, as the second purchase simply hasn't had time to occur yet. Two to three months of data gives a reliable picture.
Product-Level Data and Export
Below the charts, the same KPIs are shown by parent ASIN with period-over-period comparison. Clicking a parent filters all charts on the page to that product. You can also filter to a specific child ASIN.
To export, hover your mouse over the top right corner of the table until the three dots icon appears, then click it and select Export Data. Choose Summarized Data and export as Excel. The output is structured as a database table, well-suited for pivot tables.
Cohort Analysis Page
Cohort Analysis groups customers by the month of their first purchase and tracks how those same customers behave over time. The goal is to understand how your LTV actually develops: how many units a customer buys, how much revenue and profit they generate, and how long it takes to recoup your acquisition cost.
How to Read the Table
Each row is a monthly cohort of new customers. Month 0 is the same month as their first purchase. Month 1 is the next calendar month. Month 2 is the month after that, and so on. The columns show cumulative performance, so by month 7 you can see the total LTV that cohort has generated since their first order.
The available cohort views are:
LTV in units: How many total units the average customer in that cohort has purchased, including their first order and all repeat orders.
Revenue per customer: Same logic using revenue instead of units. Shows how much total revenue one acquired customer generates over time.
Profit per customer: How much profit one customer generates month by month, including advertising spend in the acquisition cost. This is where you'll see whether a cohort started in negative profit (spend exceeded first-order margin) and when it turned positive. If a cohort reached profitability by month 3, that informs how aggressively you can advertise for new customers.
Profit margin: The same view expressed as a margin percentage rather than a dollar amount.
Retention (count): How many customers from the original cohort placed an order in each subsequent month.
Retention (percentage): The cumulative percentage of the cohort that has made at least one repeat purchase by each month.
All customer revenue: Total revenue generated by the cohort each month, both new-to-brand and repeat, shown as both a monthly figure and a cumulative total.
Filtering Cohorts
You can filter by product (parent or child) and by order type (Subscribe and Save vs non-Subscribe and Save). Comparing the S&S and non-S&S cohorts side by side shows how much difference subscription behavior makes to LTV for a specific product, and whether it's worth investing more to push customers toward subscribing.
Why Cohorts Matter for Testing
Cohort Analysis is the right tool for measuring the long-term result of pricing and promotion tests. A week where you run a 40% coupon may look worse on short-term ACoS but produce a more loyal cohort with higher 6-month LTV. The cohort table is how you verify that, not by looking at the week's revenue, but by tracking what those customers did in the months that followed.
Repeat Purchases Page
This page shows how quickly your customers make a second purchase, broken down into time windows: 0-7 days, 8-15 days, 16-30 days, 30-60 days, 60-90 days, 90-120 days, and beyond. The percentage column shows what share of total repeat orders fall within each window, building toward 100%.
The main use case is timing retargeting campaigns and Brand Tailored Promotions. If 40% of your repeat orders happen in the 30-60 day window, that's when a retargeting push or a win-back promotion is most likely to land. Running it at 14 days (before most customers are ready to reorder) wastes budget. Running it at 90 days (after most have already reordered or moved on) is too late.
Once you've adjusted your retargeting timing based on this data, use Cohort Analysis to track whether retention rates improve in subsequent cohorts.
Break-Even ACoS Page
This page calculates how high your ACoS can be before you lose money, not just on the first order, but accounting for the repeat revenue that customer will generate over 1, 3, 6, and 12 months.
For each parent or child ASIN, you'll see your average sale price and average profit per unit for the last 30 days, followed by the break-even ACoS at each LTV horizon. If your product's break-even ACoS at 6 months is 126%, that means you can run campaigns above your first-order margin and still recover the investment within 6 months through repeat purchases.
The page also shows a payback period based on your actual current ACoS: how many months it will take to recoup your ad spend given your LTV curve. A payback period of 1 month means you're well within your first-order margin. A payback period of 6 means you're relying on repeat revenue to break even.
How to Use It
Break-Even ACoS is most useful as a ceiling for how aggressive to get on specific keywords, particularly ones where your market share isn't yet maximized. If your payback period is comfortably short, there's room to increase bids. If it's long, review where spend is going before pushing further.
Two important caveats: first, exclude branded search terms when reading your ACoS against these thresholds, as branded terms inflate efficiency and will make the number look healthier than it is for non-branded keywords. Second, Amazon advertising often functions as an organic rank support tool as much as a direct revenue driver, so a break-even calculation based purely on attributed sales may understate the value of the spend.