Lifetime Value Report Walkthrough

This guide will explain how the Lifetime Value report is aggregated, how to read the report, how to toggle report settings and filters to suit your needs, and how to derive actionable insights from the data.

What is cohort analysis and why is it valuable?

Cohort analysis is a decades-old method of studying a customer base by first separating it into groups, and then tracking how each group (or “cohort”) engages with a business over time.

The Lifetime Value Report (or “LTV report”) applies this method by grouping your customers into cohorts based on when they placed their first order with your store. For each cohort, it calculates how much an average customer is initially worth to your business, and then it continues to update this figure as repeat customers make additional purchases over time. Ultimately, this allows you to estimate the lifetime value of your past and present customers.

The report also offers various parameters, filters, and other settings that you can manipulate to better understand how and why different subsets of customers are worth more to your business than others, and which will help you to confidently make important 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?

As we’ll see, these are just a few of the many questions that when used effectively, the LTV report can help you answer.

Let’s get started.

How to read the LTV report

After signing into your Lifetimely account, click on the “Lifetime Value” tab at the top of your screen. The app will automatically pull your data and generate a report.

By default, the report calculates accumulated sales per customer over the preceding six months, with customers 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 start by breaking down the components of the report for just one cohort.

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The first cell of every row identifies the cohort that you’re looking at. In this case, the cohort consists of customers who made their first order in May 2020.
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The next three columns provide the following information:
  • New customers - the total number of customers in the cohort. The reason they are “new” is because they made their first order in May 2020.
  • 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 example above, we see that the May 2020 cohort consists of 2534 new customers, that they were acquired at an estimated cost of $17 per customer, and that 36% of customers in the cohort made a second order within six months of their first order.

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The next cell (First Order) is our starting point for estimating the value of an average customer in the cohort.

We ran a report with the default settings, so the metric we’re seeing is 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 May 2020 cohort spent an average of $104 on their first orders. Another way of saying this is that an average customer from this cohort was initially worth $104 in sales to your business.

👉 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!
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The remaining cells in the row show us how the estimated value of a customer in the cohort has increased over time, fueled by members of the cohort that have returned to your store to make additional orders.

The value in the first cell of this section (0 months since the first order) is total first order cohort sales + total additional cohort sales in the same month / total customers in the cohort. The next cell to the right gives you the same calculation plus any additional cohort sales one month after first orders. This pattern continues to the end of the row, where you finally see all cohort sales in the six months after first orders (including first orders) / total customers in the cohort.

Let’s look at the numbers from our sample report again:

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

In column “0” we see a value of $121. This means that after placing their first order sometime in May, several customers from this cohort returned to the store before the end of the month to make additional orders. Together, they generated enough additional sales to raise the average value of a customer in this cohort by $13.

As we continue to the right, we see this figure rise month-to-month. By 3 months after first orders, customers from this cohort had an average value of $211 in sales. (Just to drive the point home, this is calculated by totaling all May sales [including first order sales], June sales, July sales, and August sales, and then dividing by the total customers in the cohort.)

This report is showing us the past six months of customer data, so the final cell in this row takes us to up to the present day. The $237 figure here shows us that a customer who made his/her first order in May 2020 has on average been worth $237 in sales to your business. That’s more than double the first order value! And since 64% of customers in this cohort never made a second order (which we know because the R-% is 36%), this suggests that the customers who have returned have been making large and/or frequent orders.

👉 Use case: Imagine this sample report came from your store. What would you do with the above information? While it’s hard to reach any firm conclusions by looking at just one cohort, the data here can suggest which reports to run, which filters to apply, and which cohorts to compare in order to learn valuable information about your customers.

For example, we know that the 36% of repurchasing customers from our May 2020 cohort generated high sales totals in the six months after their first orders. Do these customers have something in common? Are most of them coming from the same marketing channel? The same country? Did they sign up for a loyalty program? Because these customers are so valuable, you would want to run new reports with various filters to try to build a profile of your best customers.

Another example - we know that average customer value in our May 2020 cohort increased every month, but started to flatten after month 4. Is this standard behavior for your customers? Did earlier cohorts experience this same plateau? In other cohorts, is the average customer value at month 6 an approximate lifetime value, or did this number continue to increase through the year? To answer these questions, you could run new reports with adjusted time periods to see how earlier cohorts behaved over a larger time frame. Then if you saw significant differences in average customer value, you could apply filters in the same way described above to find your more valuable customers.

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.

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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 despite significant increases in customer acquisition costs. Both of these trends would certainly raise some flags and instigate further analysis!
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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 May 2020 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.

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 six 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.

Let’s walk through what each of these metrics is calculating and the value provided by each type of report.

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

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.

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.

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).

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To activate the CAC payback tool, simply click the CAC payback switch to the "on" position.
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As soon as you activate the tool, you will see a bright green marker somewhere 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.
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You can hover over any of these green markers to see the actual CAC payback figure. In this example, the CAC payback time of the October 2020 was 3.8 months. Since the average CAC of the October 2020 cohort was $45, this means that it took 3.8 months of cohort sales to raise the average customer gross margin to $45. Past this point, any additional sales by this cohort should generate a net profit.
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If you don't see a green marker anywhere along a cohort's timeline (such as in the Nov 2020 and Dec 2020 cohorts above), then you have not yet broken even on your CAC.
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If the green marker is at the very beginning of your timeline (such as in the Jan 2021 cohort above), then you 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.

To apply a filter to your report:

  1. Click the filters button (highlighted above)
  2. Decide which filter(s) you want to apply to your report
  3. Click the text box of your selected filter. This will display a drop-down menu of every available measure that you can filter by.
  4. Select one or more options
  5. Click the blue button marked “Apply filters” in the bottom right of the panel.

The following filters are available:

Marketing channels/sources

If you’ve connected your store’s Google Analytics account to Lifetimely, you have the option to filter your report results to include only the customers who were directed to your store by any of the following channels:

  • Affiliates
  • CPC Google
  • Direct
  • Email
  • Facebook Paid
  • Instagram
  • 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: For more detailed information on how these channels relate to Google Analytics channels, refer to this article: Google Analytics integration and the marketing channel filters

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.

Tags

These filters let you take advantage of the feature in Shopify that allows you to tag customer data. You have the option to filter the report to only include OR only exclude customers with a given tag. Again, clicking on the empty text box will pull up a list of available tags to select from.

👉 Use case: Perhaps you run a business where you offer wholesale prices to a select few customers. Because these customers are known outliers, it might make more sense to exclude them from your reports.

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
👉 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.
    💡 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!

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