Lifetime Value Report Walkthrough

This guide explains how to interpret the Lifetime Value (LTV) report, adjust its settings and filters, and extract actionable insights!

What is cohort analysis and why is it valuable?
How to read the LTV report
- Analyzing data for a single cohort
- Analyzing data across multiple cohorts
How to customize your LTV report

What is cohort analysis and why is it valuable?

Cohort analysis involves grouping customers and tracking each group's engagement with your business over time.

The LTV report groups customers by the month of their first order and calculates their average initial value. This figure updates as repeat purchases occur, helping you estimate customer lifetime value.

The LTV report offers various filters and settings to help you understand the value of different customer subsets, allowing you to confidently make informed decisions such as:

How much can I afford to spend to acquire new customers?
Which of my marketing initiatives should I abandon, and which should I scale up?
Which product categories should I expand to maximize long-term profits?

How to read the LTV report

Log into your Lifetimely account, from the left-side menu, click the “Lifetime Value" tab and "Cohorts".

By default, the report shows accumulated sales per customer over the past six months, grouped by the month of their first order. (Don’t worry if this is confusing - we’ll explain what all this means in a moment.)

Here’s an example from one of our test stores:

Analyzing data for a single cohort

Let's examine the components of a single cohort in the report.

The first cell of each row identifies the cohort, in this case customers who placed their first order in June 2024.

The next three columns display:

New customers - the total number of customers in the cohort. The reason they are “New” is because they made their first order in June 2024.
CAC (Customer Acquisition Cost) - an estimate of the average cost of acquiring each customer in the cohort, measured by total marketing spend divided by the number of new customers. This isn’t a perfect measure, but it should be accurate enough to draw general conclusions.
R-% - the percentage of customers in the cohort who made at least one additional order within the selected time period of the report

In the above example, the June 2024 cohort includes 578 new customers, acquired at an estimated cost of $21 each, with 38% making a second purchase within six months.

The "First Order" cell is our starting point for estimating the value of an average customer in the cohort. Using the default settings (Metric = Accumulated sales per customer). Since we’re only interested in the value of customers’ first orders in this cell, the calculation is total first order cohort sales / total customers in the cohort.

In our example, we see that customers in our June 2024 cohort spent an average of $81 on their first orders, meaning each was initially worth $81 in sales.

👉 Without cohort analysis, this figure would perhaps be our best estimate of average customer value. Pay attention to how much this number changes as the report accounts for sales data from customers who repurchase over the following months. The difference might be so drastic that you’ll wonder how anyone could make educated business decisions without this analysis!

The remaining cells in the row track how the value of a customer in the cohort grows over time, as members make repeat purchases.

The value in the first cell of this section (0 months since the first order) is:
- (total first-order cohort sales + total additional sales in the same month) ÷ total customers in the cohort
The next cell adds additional sales from the second month, and so on
The final cell (in this example, Month 6) shows all cohort sales in the six months after first orders, the calculation:
- total cohort sales (including first orders) ÷ total customers in the cohort.

💡 Tip: Hovering your mouse over any cell in this section will pull up an explanation of the value you’re looking at.

Let's look at the numbers!

As we move across the row, the figure continues to rise month-to-month. By 3 months after their first order, customers from this cohort had an average value of $124 in sales. This is calculated by summing all sales from June (including first orders), July, August, and September, and dividing that total by the number of customers in the cohort.

The report provides six months of data. The final cell in the June 2024 row shows an average value of $124, meaning that customers who made their first purchase in June 2024 generated, on average, $124 in sales over this period. This represents a 53% increase from their initial value of $81, demonstrating the value of returning customers.

With a repeat rate (R-%) of 38% for this cohort, it’s clear that most customers (62%) did not make a second order. However, the customers who did return placed substantial or frequent orders, significantly increasing the cohort's overall average value.

In column "0," we see a value of $83 for the June 2024 cohort. This indicates that after placing their first order, several customers returned to make additional purchases within the same month. These additional sales raised the average value of a customer in this cohort from their initial first-order value of $81 to $83.

👉 Use case 1: Imagine this sample report came from your store. What would you do with the above information? While it’s hard to reach firm conclusions from just one cohort, the data here can guide you on which reports to run, which filters to apply, and which cohorts to compare to uncover valuable customer insights. For example, we see that 38% of repurchasing customers in the June 2024 cohort generated significant sales totals in the six months after their first orders. What do these customers have in common? Are they coming from the same marketing channel, country, or promotion? Did they engage with a loyalty program? Since these customers are so valuable, you might run additional reports with filters to build a profile of your top-performing customers and identify similar opportunities.

👉 Use case 2: The average customer value in the June 2024 cohort steadily increased from $83 to $124 by month 3, but plateaued after that. Is this typical behavior for your customers? Did earlier cohorts experience similar trends? Does the average customer value at month 6 represent their approximate lifetime value, or does it continue to grow beyond this time frame? To answer these questions, you could run reports with adjusted timeframes to analyse earlier cohorts over a longer period. If you noticed significant differences in customer value trends, you could apply filters (e.g., by marketing channel, product, or region) to identify which segments are driving higher customer value and focus on targeting them.

Analyzing data across multiple cohorts

Studying data across multiple cohorts will give you a more complete picture of how your customer base is evolving over the lifetime of your business.

One of the best ways to compare cohorts is to compare values within a single column of the report. Let’s zoom back out on our sample report to see how.

1: Comparing important figures such as CAC and R-% across cohorts can help you quickly identify trends in your business. In the example above, we see that repurchasing percentages are steadily dropping, this trend would certainly raise some flags and instigate further analysis!
2: Comparing values in this section of the report shows you how customer behavior is changing over time. Using the report above, we see that despite equivalent first order values across all cohorts, customers in the June 2024 cohort generated much higher repeat sales over subsequent months than later cohorts, with a noticeable downward trend for each group of new customers.

The value of comparing metrics across cohorts is that it helps you quickly identify changes in customer behavior. You need to know how your customer base is changing in order to start asking the right questions (and running the right reports) to determine why it’s changing, which will then help you to make more profitable business decisions.

👉 Use case: Let’s say that over the past six months, your R-% is holding steady across all cohorts, but you see a significant increase in average customer value for newer cohorts. This would suggest that even though new customers are repurchasing at the same rate as older customers, they’re spending more money on average when they revisit your store. You know what is going on, which should help you think of theories for why this is happening (for example, maybe the driving factor is a new product you recently launched, or a marketing campaign you scaled up). Now you can run new reports with adjusted parameters and filters to test these theories.

Spaghetti graphs

To see your metrics plotted on a line graph, you can toggle your LTV report to a line graph (or "spaghetti chart") view using the button highlighted above. The advantages of a spaghetti chart is that it:

Helps you visualize the trajectories of each cohort's LTVs
Makes it easier to compare LTVs at similar points in time relative to first orders.

💡 Tip: Hovering over different points of the chart will give you exact LTV figures for a given cohort as it changes over time.

How to customize your LTV report

The top banner of the LTV report includes a number of settings you can customize to dig deeper into your customer data. Let’s take a closer look at what you can learn by adjusting each of these settings.

Selecting timeframes for your cohorts

Time period

The default LTV report displays customer data from the previous six months. If you’d prefer to see a different time range, click on the box marked “Time period”. You’ll see options to reach as far back as four years, or you can define your own custom date range.

When does it make sense to look at reports with shorter vs longer time periods?

Shorter time periods may make more sense for you if your customer base is dynamic, meaning that new customer cohorts may behave very differently in comparison to previous cohorts.

Reports with longer time periods, however, will be better-suited for businesses where customer behavior has held relatively steady over time.

Timeframe

In the next box to the right, you can choose to separate your customers into cohorts based on the week, month, quarter, or year in which they made their first purchase.

When does it make sense to split customers into smaller cohorts (grouped by week or month of first order) vs larger cohorts (grouped by quarter or year)?

As with time periods, shorter timeframes (which result in smaller cohorts) may make more sense if you have a dynamic customer base and/or if you’re trying to quickly draw conclusions about a new group of customers.

👉Use case: Let’s say your business is in the CPG industry, and you want to acquire more customers by launching a program where new customers get their first product for just the cost of shipping. This program will only be successful if the average lifetime value of these new customers makes up for the loss you take on their first orders. If new customers don’t return to your store, you risk taking a major hit. In this case, you would want to run reports over short time periods with cohorts separated into weeks. This would help you quickly estimate the CAC payback time of your new customer base in order to analyze the viability of the program.

If your customer base has behaved in a relatively stable manner over multiple quarters or years, then selecting longer timeframes can give you a reliable estimate of the lifetime value of your customers.

Choosing which metrics to analyze

The LTV report gives you the option to display thirteen types of metrics in your report! The report will keep the same format regardless of the metric you select, but different metrics will help you reach different insights and conclusions about your data.

By default, your report will display data as accumulated sales per customer. To change this metric, simply select a new one from the Metrics drop-down menu (see below), and a new report will instantly generate.

⚠️ Note: All sales-related metrics include taxes and account for both refunds and canceled orders.

⚠️ Note: The Net Sales metric in Lifetimely is calculated differently from the Net Sales in Shopify. In Shopify, Net Sales = Gross Sales - Discounts - Refunds and it doesn't take into account the Shipping Revenue. Whereas in Lifetimely, Net Sales = Product Revenue + Shipping Charged - Discounts - Refunds - Taxes

💡 Tip: For some of these metrics, it may help to see values represented as a percentage instead of an absolute number. To switch the format to percentages, just click on the % icon in the box marked “Format”.

Accumulated sales per customer

This metric calculates the total sales generated by an average customer from a given cohort over time. This and accumulated gross margin per customer are the two most useful metrics for giving you an estimate of the lifetime value of your customers, and therefore will be your go-to reports for calculating average customer values and monitoring how these values change over time.

Accumulated net sales per customer

Total net sales (after taxes, discount codes, and refunds have been deducted) per customer in that cohort so far. Not just across returning customers, but across all the customers in that cohort.

Accumulated gross margin per customer

The difference between this and accumulated sales per customer is that this metric calculates your gross margin by subtracting all product-related costs (marketing costs are not removed from this value). This helps you estimate the average profit of your customers over time.

Assuming that your product costs are accurately captured in Lifetimely, this is our recommended metric for determining the lifetime value of your customers and for understanding how much you can afford to spend to acquire new customers.

👉 Use case: Let’s say you sell supplements. Because supplements are a consumable product where lifetime values of customers are much higher than first order values, you know you don’t have to be profitable on a first order basis. Let’s say that reports on your existing customers indicate that your business is profitable as long as you make back your CAC in 6 months. Now imagine that you maintain the same CAC while doubling your marketing budget to expand into a new marketing channel. Do these new customer cohorts behave in the same way as previous ones? Will you continue to make back your CAC in 6 months? Generating reports with this accumulated gross margin per customer metric will help you analyze whether this new marketing initiative is profitable.

Accumulated contribution margin per customer

Total sales - discount codes - refunds - taxes - COGS - marketing spend per customer in that cohort.

Average order value

This metric tracks each cohort's average order value in each year/quarter/month/week following initial orders. It's calculated by taking total sales (after discount codes and before refunds) and dividing by the total number of orders for each time period.

Customers

The customers metric sets aside sales numbers to show you how many customers from each cohort are making additional orders in your store over time. This helps you analyze when your customers are most likely to make an additional purchase and see whether new customers are repurchasing at a faster or slower rate compared to older customers.

💡 Tip: It may be easier to draw certain conclusions about your customers when you see this metric expressed as a percentage instead of an absolute number.

👉 Use case: Let’s say that last year you ran a Black Friday sale and you’re unsure of whether to run a similar sale this year. To help with your decision, you can use the customers metric to run a report on your November cohort from last year to see how many Black Friday customers repurchased from your store, and how long after Black Friday they waited to make a new order.

Cohort transactions

To get even more granular than the Customers metric, you can run a report that gives you discreet transactions per cohort over time. These last two metrics (cohort sales and cohort transactions) may be especially helpful in building financial models.

Cohort sales

This metric shows you how the total sales volume for each cohort increases over time, allowing you to compare net sales across cohorts and see how much each cohort is contributing to total sales. Total discreet sales per cohort during week/month/year.

Cohort net sales

Total net sales (after taxes, discount codes, and refunds have been deducted) per cohort during week/month/year.

Accumulated orders per customer

Like the customers metric, accumulated orders per customer also sets aside sales numbers to show you how many orders are placed by a cohort over time. This helps you analyze the frequency at which new customers order additional products compared to previous cohorts.

Customers who purchased X times

This metric is a bit different from the others - instead of giving you a measure of lifetime value, it breaks down how often different customers within each cohort are placing orders. When using this metric, keep in mind that timeframe is removed as a factor, so comparing cohort values within each column isn't as much of an apples-to-apples comparison as with other metrics. E.g. How many new customers from Jan-2024 cohort have made 3 or more orders?

Accumulated sales per customer / CAC

Accumulated sales divided by CAC. The ratio measures the relationship between the lifetime value of a customer and the cost of acquiring that customer

Accumulated gross margin per customer / CAC

Accumulated gross margin divided by CAC. Once > 1.0, your cohort has become profitable and CAC payback has happened. The ratio measures the relationship between the lifetime value of a customer and the cost of acquiring that customer

Using the CAC Payback tool

For many businesses, one of the most important insights to be gained from the LTV report is an estimate of how long it takes to break even on new customers. This is known as “CAC payback time” - the point at which the accumulated gross margin of an average customer finally outweighs the average cost of acquiring a customer. (Reminder: your gross margin is sales after refunds and discounts, minus shipping costs, handling costs, transaction costs, and custom costs). In other words, this is the time it takes for an average customer to become profitable.

The CAC payback tool helps you visualize where this breakeven point is for each of your cohorts.

⚠️ Note: The CAC payback tool will only give you reliable figures if your costs are complete and accurate. If you haven't finished entering your cost data, you can do so in the Cost Data & Integrations section of the app (or refer to our guide to Getting Started).

1: To activate the CAC payback tool, simply click the CAC payback switch to the "on" position.
2: As soon as you activate the tool, you will see a bright green marker along the timeline of all of your cohorts that have reached breakeven, indicating the exact point where accumulated gross margin per customer equals CAC for the cohort.
3: You can hover over any of these green markers to see the actual CAC payback figure.
4: If you don't see a green marker anywhere along a cohort's timeline, then you have not yet broken even on your CAC.
5: If the green marker is at the very beginning of your timeline (such as in the Jan 2024 and Aug 2024 cohort above), then you have already broke even on your CAC on your cohort's first orders.

Applying filters for more advanced analysis

On the far right of the settings bar of the LTV report is a button that will pull up a long list of filters you can apply to your report. Applying these filters is the most effective way to extract actionable insights from your customer data.

The following filters are available:

Marketing channels/sources

If your store's data includes tracking information from various marketing and referral channels, you have the option to filter your report results to include only the customers directed to your store by specific channels:

Affiliates
CPC Google
Direct
Email
Facebook Paid
Instagram
Klaviyo list, segment or campaign
Organic Search
Other Social Advertising (i.e. non-FB or IG, such as TikTok and Pinterest)
Referral
Social (i.e. all social media sources)

💡Tip: To drill down even further, you can also filter by specific sources of customer traffic (instead of by general categories). Clicking on either “Source/Medium” text box will pull up all available sources. Whether you filter by marketing channel or by source, you have the option to go by either the first touch or last touch of your customers. The first touch is the channel the customer used when first accessing your store, even if he/she didn’t convert to a paying customer on the first visit. The last touch channel is how the customer accessed your store during the session in which he/she placed an order for the first time.

👉 Use case: Let’s say that over the past year, you’ve launched an affiliate program and you’ve started running ads on both Facebook and Instagram. Your marketing efforts seem to be paying off: profits are high, and when you run your LTV reports, you see that average customer value in newer cohorts is much higher than in earlier cohorts. Now you want to reinvest those profits into acquiring more customers. Which campaign should you double down on? Running reports that filter for these marketing channels would help you analyze which marketing initiative is bringing in your highest-value customers, which in turn will help you decide how best to allocate your marketing dollars.

Products

With these filters, you can run reports to:

Only include customers who purchased a certain product with their first order
Only include customers who purchased from a certain collection with their first order
Only include customers who purchased a certain product type with their first order

👉 Use case: Let’s say you sell two types of products. One type has a higher average gross margin, but the other type has a higher repurchase rate. Comparing a report of average customer value for each product type can help you identify which product type is more profitable over the long-term.

Sales channels

The sales channel filters let you account for the different ways you might sell your product, such as in-store vs. online. You can choose to only include OR only exclude customers from a given source.

Other

Filters in the “Other” category allow you to run reports to:

Only include customers from specific Countries, or a certain State

👉 Use case: A country you’ve been selling to issues a new tariff that increases your shipping costs. Analyzing average customer value for residents of this country can help you decide whether to continue selling to the country or whether you need to adjust your prices.

Only include customers who applied a discount code to their first order

👉 Use case: Imagine that six months ago, you issued 500 coupon codes for one of your products. Though you sold at a high volume, you didn’t make any profit on these sales. To decide if this campaign was worth it, you can run a report to see if customers who took advantage of this deal were non-returning deal-seekers or customers who returned to your store and repurchase other products at full price.

Surveys

Connect Upsell to use post-purchase survey responses as a filter and segment customers by Questions or Responses.

💡 Tip: Keep an eye out for news about new filters. We’re constantly adding more! And if you have an idea for a new filter, please let us know!

Saving cohorts

If there are combinations of frequently used filters that you plan on applying again, you can save time by saving your customized cohorts with the "Save cohort" button at the bottom of the Filters window.

To save a cohort:

Select a combination of one or more filters.
Click the "Save cohort" button at the bottom of the Filters window.
Assign a name to your new cohort. This will add it to the "Saved cohorts" drop-down menu at the top of the Filters window

To access a saved cohort:

Click on the "Saved Cohorts" drop-down menu at the top of the Filters window
Select your saved cohort and click the blue "Apply filters" button. This will automatically apply the correct combination of filters associated with the cohort to your LTV report.