Measure customer lifetime value (LTV/CLV) using Parabola

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Measure customer lifetime value (LTV/CLV) using Parabola

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What is customer lifetime value?

Customer Lifetime Value, often shortened to CLV or LTV, is the predicted amount of money that a customer will spend for the length of their relationship with your business. Acquiring new customers can be an expensive process. Understanding your customers’ LTV helps you make sure you are spending appropriately. LTV can also give some insight into your customers’ spending patterns. You can see if higher spending customers are making small frequent purchases or if they are spending large amounts on one or two items throughout their relationship with your business.

Why is customer lifetime value important?

Understanding your customers is incredibly important when planning for and maintaining your business. When considering how much to spend to acquire new customers, your current customers’ average LTV is an important metric to keep in mind. It is often easier to maintain a relationship with a current customer than it is to acquire a new one.

Calculating customer lifetime value allows you to:

  • 💳 Understand what high spending customers will buy: You can determine which products your higher spending customers are most likely to buy and promote or stock them as needed.
  • 🖼️ Adjust how you advertise to bring in new customers: When combined with customer acquisition cost (also knows as CAC) reports, customer lifetime value can give you a fuller picture of the amount of money you are spending to bring in new customers vs how much money your customers are spending with you.
  • 📈 Identify areas of your business that can be improved: If you’re having trouble retaining your customers, use customer lifetime value to determine who is more likely to be longterm vs short term. If you are seeing customers leave after a short amount of time, potentially adjust how you are doing promotions or adjust outreach to improve your relationship.

What makes calculating customer lifetime value difficult?

With so many moving parts, calculating customer lifetime value can come with challenges. Here are just a few:

🕰️ Calculating LTV is time consuming

Calculating customer lifetime value is a lengthy process and your team may need to try several different formulas before you find the right fit for your business and customers. Your customer and order data lives across different applications and is in different formats which adds another level of complexity when you begin to combine that information. Any little adjustment to how you format your data can throw everything off. On top of that, your team will have to constantly maintain internal documentation that walks through the manual process of doing this. This time would be much better spent working to improve your customers’ LTV.

👩🏽‍💻 You may need to combine the data with other reports

While customer lifetime value is an important piece of information on its own, it is often combined with other data and reports when making decisions about the business. You may often see LTV combined with customer acquisition cost reports to see how much money is being spent to acquire customers vs how much money customers then spend. These reports all take time to put together as you combine and format data into a single source. Your team’s day can very quickly turn into generating reports that will then need to be combined with other reports, etc. If you make any changes to one of the reports you are combining with, you have to start over again.

🤔 Your results may need to be segmented for specific customer groups

Every customer is different which means when you are analyzing your customer lifetime values, it is often better to do this in select groups instead of just one big table that show all of your customers. Filtering down your information to pull select groups of customers takes time. Changing up how you’re filtering those results creates even more work which means you can’t quickly pull in your results.

How can Parabola help?

Calculating customer lifetime value can feel like an overwhelming process as you get started. Parabola allows you to:

  • Automate the process of calculating customer lifetime value, allowing you to spend more time actioning off of the data than creating it.
  • Easily combine multiple reports to get a full picture of your customers and how much they are spending vs another value like customer acquisition cost.
  • Make quick changes and filter for customer groups that you are looking to focus on.

We’re going to walk through an example of an LTV report that you can build out in Parabola using our Shopify customer lifetime value report recipe. Use the recipe to get started on your own report and easily customize it as you follow along with the sections below!

1. Pull in your order and customer data

In the screenshot below, you can see how a team can use Parabola to pull in the last year of their order and customer data. In this example, the team is using Shopify so they easily connect to the Pull from Shopify steps and then match the orders to their customers using a Combine tables step:

Customer and order data is pulled in using Pull from Shopify steps and then matched up.

2. Calculate LTV based on recency, frequency, monetary value, and lifespan

The cards listed below are calculating 4 different areas:

  1. How recently a customer has made a purchase: We use the Compare dates step to find when orders were last placed and then give a score based on how recently the order was placed using the Insert if/else column step.
  2. How frequently a customer makes a purchase: Similar to the Recency card, we calculate how frequently a customer makes a purchase and assign them a score.
  3. How much money a customer has spent: We calculate how much a customer has spent within a timeframe and assign them a score based on that amount.
  4. How long a customer will have a relationship with your business: Compare your current customer’s time with your business to the average length of time that a customer typically buys from a business before becoming inactive.
Steps are used to find how recently a customer has placed an order and the customer is then given a score that will later be used to calculate LTV.
Customers’ buying frequency is calculated and they are assigned a score based on how frequently they shop with the business.
The amount of money that a customer spends totaled and then they are assigned a score based on this amount.
The amount of time since a customer first bought something from the business is found and they are assigned a score to be used in later calculations.

3. Combine your calculations and break customers out into groups

Easily combine the calculations that you did in the previous cards into one table that you can work from. Next, break out your customers into different groups based on the numbers they were assigned in the above calculations. Use an Insert math column step to determine which customers will go in which groups based on their scores.

The data from the previous steps is combined using Combine tables steps. Customers are broken out into groups based on the previous calculations.

4. Find the average order value and purchase frequency by group and format the report

The Sum by group steps will allow you to easily generate the total amount spent and how frequently a customer placed an order and group it by the selected groups. From there, you can format your report to look exactly how you’d like using steps like Select columns and Format numbers. Finally, email the report to yourself and your teammates using the Email a CSV attachment step!

The average order value and purchase frequency for each group is calculated and the report is formatted before it sent as a CSV via email.

You can start building for free by signing up for Parabola. Email us at help@parabola.io and we can help you get started!

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Emory Stainbrook
Last updated:
July 27, 2022

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Measure customer lifetime value (LTV/CLV) using Parabola

Emory Stainbrook
Last updated:
July 27, 2022

What is customer lifetime value?

Customer Lifetime Value, often shortened to CLV or LTV, is the predicted amount of money that a customer will spend for the length of their relationship with your business. Acquiring new customers can be an expensive process. Understanding your customers’ LTV helps you make sure you are spending appropriately. LTV can also give some insight into your customers’ spending patterns. You can see if higher spending customers are making small frequent purchases or if they are spending large amounts on one or two items throughout their relationship with your business.

Why is customer lifetime value important?

Understanding your customers is incredibly important when planning for and maintaining your business. When considering how much to spend to acquire new customers, your current customers’ average LTV is an important metric to keep in mind. It is often easier to maintain a relationship with a current customer than it is to acquire a new one.

Calculating customer lifetime value allows you to:

  • 💳 Understand what high spending customers will buy: You can determine which products your higher spending customers are most likely to buy and promote or stock them as needed.
  • 🖼️ Adjust how you advertise to bring in new customers: When combined with customer acquisition cost (also knows as CAC) reports, customer lifetime value can give you a fuller picture of the amount of money you are spending to bring in new customers vs how much money your customers are spending with you.
  • 📈 Identify areas of your business that can be improved: If you’re having trouble retaining your customers, use customer lifetime value to determine who is more likely to be longterm vs short term. If you are seeing customers leave after a short amount of time, potentially adjust how you are doing promotions or adjust outreach to improve your relationship.

What makes calculating customer lifetime value difficult?

With so many moving parts, calculating customer lifetime value can come with challenges. Here are just a few:

🕰️ Calculating LTV is time consuming

Calculating customer lifetime value is a lengthy process and your team may need to try several different formulas before you find the right fit for your business and customers. Your customer and order data lives across different applications and is in different formats which adds another level of complexity when you begin to combine that information. Any little adjustment to how you format your data can throw everything off. On top of that, your team will have to constantly maintain internal documentation that walks through the manual process of doing this. This time would be much better spent working to improve your customers’ LTV.

👩🏽‍💻 You may need to combine the data with other reports

While customer lifetime value is an important piece of information on its own, it is often combined with other data and reports when making decisions about the business. You may often see LTV combined with customer acquisition cost reports to see how much money is being spent to acquire customers vs how much money customers then spend. These reports all take time to put together as you combine and format data into a single source. Your team’s day can very quickly turn into generating reports that will then need to be combined with other reports, etc. If you make any changes to one of the reports you are combining with, you have to start over again.

🤔 Your results may need to be segmented for specific customer groups

Every customer is different which means when you are analyzing your customer lifetime values, it is often better to do this in select groups instead of just one big table that show all of your customers. Filtering down your information to pull select groups of customers takes time. Changing up how you’re filtering those results creates even more work which means you can’t quickly pull in your results.

How can Parabola help?

Calculating customer lifetime value can feel like an overwhelming process as you get started. Parabola allows you to:

  • Automate the process of calculating customer lifetime value, allowing you to spend more time actioning off of the data than creating it.
  • Easily combine multiple reports to get a full picture of your customers and how much they are spending vs another value like customer acquisition cost.
  • Make quick changes and filter for customer groups that you are looking to focus on.

We’re going to walk through an example of an LTV report that you can build out in Parabola using our Shopify customer lifetime value report recipe. Use the recipe to get started on your own report and easily customize it as you follow along with the sections below!

1. Pull in your order and customer data

In the screenshot below, you can see how a team can use Parabola to pull in the last year of their order and customer data. In this example, the team is using Shopify so they easily connect to the Pull from Shopify steps and then match the orders to their customers using a Combine tables step:

Customer and order data is pulled in using Pull from Shopify steps and then matched up.

2. Calculate LTV based on recency, frequency, monetary value, and lifespan

The cards listed below are calculating 4 different areas:

  1. How recently a customer has made a purchase: We use the Compare dates step to find when orders were last placed and then give a score based on how recently the order was placed using the Insert if/else column step.
  2. How frequently a customer makes a purchase: Similar to the Recency card, we calculate how frequently a customer makes a purchase and assign them a score.
  3. How much money a customer has spent: We calculate how much a customer has spent within a timeframe and assign them a score based on that amount.
  4. How long a customer will have a relationship with your business: Compare your current customer’s time with your business to the average length of time that a customer typically buys from a business before becoming inactive.
Steps are used to find how recently a customer has placed an order and the customer is then given a score that will later be used to calculate LTV.
Customers’ buying frequency is calculated and they are assigned a score based on how frequently they shop with the business.
The amount of money that a customer spends totaled and then they are assigned a score based on this amount.
The amount of time since a customer first bought something from the business is found and they are assigned a score to be used in later calculations.

3. Combine your calculations and break customers out into groups

Easily combine the calculations that you did in the previous cards into one table that you can work from. Next, break out your customers into different groups based on the numbers they were assigned in the above calculations. Use an Insert math column step to determine which customers will go in which groups based on their scores.

The data from the previous steps is combined using Combine tables steps. Customers are broken out into groups based on the previous calculations.

4. Find the average order value and purchase frequency by group and format the report

The Sum by group steps will allow you to easily generate the total amount spent and how frequently a customer placed an order and group it by the selected groups. From there, you can format your report to look exactly how you’d like using steps like Select columns and Format numbers. Finally, email the report to yourself and your teammates using the Email a CSV attachment step!

The average order value and purchase frequency for each group is calculated and the report is formatted before it sent as a CSV via email.

You can start building for free by signing up for Parabola. Email us at help@parabola.io and we can help you get started!

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