How to Calculate Customer Lifetime Value for your Shopify Store

How to Calculate Customer Lifetime Value for your Shopify Store

Customer Lifetime Value (LTV) is one of the most important but often hard to calculate metrics for Shopify stores. A lot of store owners struggle to optimize their Shopify stores without fully grasping the concept of LTV. The understanding and calculation of LTV are pivotal in shaping marketing strategies and ensuring long-term profitability.

What is Customer Lifetime Value?

Customer Lifetime Value (LTV) defines the total revenue a customer brings to your store throughout your entire relationship with them. Instead of just focusing on individual orders, It takes into account the customer’s frequency of purchases, average order value, and the duration of the customer's relationship with your brand.

So it is the total money a user spends in their lifetime on your store. This is critical to know as you spend most of your marketing money in acquiring the customer. So even if the AOV (Average Order Value) is less but the LTV is much higher, you can still go ahead and spend more than AOV to get a new customer. 

The metric becomes even more important once you  decide to scale your store and starting increasing up your marketing activities to acquire new customers.

How to Calculate Customer Lifetime Value

Now that we know how important LTV is as a metric, let’s dive in to understand how to calculate it. (If it gets too complicated for you, don’t worry, we have a free tool for you that makes it super easy to calculate this with just a csv of your store’s data.)

While the basic formula for LTV calculation is quite simple, its defining some of these metrics that can get tricky -

LTV = Average Order Value (AOV) × Purchase Frequency in a Year (F) × Customer Lifespan in Yrs (L)

Average Order Value (AOV) is quite literally the average value of orders in a given period of time. This data should preferably be for more than 12 months so that it accounts for all seasonal changes

Purchase Frequency in a Year (F) is the number of orders a customer places on average over a year. 

Customer Lifespan (L) is the duration in years for which a user on average stays active on your shopify store.

While the above formula looks simple, the challenge is in calculating the last 2 metrics - Purchase Frequency and Customer Lifespan. This can be because of the following reasons -  

  • If the store is new the number of users who have made their first purchase more than 12 months ago would be less which might skew the data.
  • There might be certain changes in the store, which only a new set of users would have seen and would not be that prominent in the data set. 

Also, calculating this across different segments of users can be time consuming and hence we built a simple google sheet for you to calculate your customer LTV with just one click. Just create a copy of this sheet in your google drive, upload your order data from shopify and the sheet will calculate your LTV. 

LTV Calculation by Lumino

You can just take the above sheet to calculate your LTV and skip the details below. You can straight move to the section that talks about how to use LTV in your marketing decisions.

Or if you want to understand how we overcame the above challenges in our approach, well then read on…

To overcome these issues and come out with a close to accurate LTV (LTV calculation is not really a science) we used some statistical concepts like relationship between different user buckets and extrapolation. Here by different user buckets we mean users being part of different cohorts based on their first purchase. Let’s dive deeper into cohorts and how LTV is calculated - 

If the store is not subscription based, considering the total time duration of 3 years (36 months) should be good enough. 

So taking 3ys as the time duration the whole user set will be divided into 3 buckets - 

  1. 36 months or more
  2. 18 months or more
  3. 9 months or more

The difference between the first order of the user and today’s time will tell us in which bucket the user falls in - 

So Bucket 1 will have all the orders of users which are between the first date of purchase till 36 months from that date. All the orders after 36 months will be rejected as the time period is taken as 36 months. For example if there is a user who has order history as -

User Name First Order Date Within 9 months after 1st order Within 18 months after 1st order Within 36 months after 1st order Total Orders in Lifetime
User A 21 Aug 2020 2 3 (1 more between 21st May 2021 and 21st Feb 2022) 4 (1 more order between 21st Feb 2022 and 21st Aug 2023) 4 (No order after 18 months)

So this user will fall in ‘36 months and more’ bucket with number of orders as 4

The same user will also fall in ‘18 months and more’ bucket with number of orders as 3

The same user will also fall in ‘9 months and more’ bucket with number of orders as 2

Similarly all the users will have some value for each bucket if applicable (For eg someone who have ordered for the first time on 21st Apr 2023, the user will only belong to ‘9 months and more’ bucket and not to other buckets as the time between first order data - 21st Apr 2023 and today is less than 18 months)

Once this exercise is completed on all user data points, the average money spent for each bucket is calculated. The good thing in this set is that all users irrespective of their age on store are considered.

In the above case where there is only 1 user and say AOV of 10$ the following data will be computed - 

9 months - $20

18 months - $30

36 months - $40

Once this is calculated the ratio of the buckets are identified - 

9 month : 18 month : 36 months = 2:3:4

These ratios will be used in extrapolation of data when the user falls in a lower time duration bucket only.

For eg - if there is some user who is only 9 months old (Diff between user first order and today) has spent 50$ in the first 9 months, then the user is expected to spend 100$ in 36 months. 
Once all the users are extrapolated to 36 months the total spent by these users are calculated and LTV is derived.

You can use this sheet to model your LTV calculations by just adding order data from your shopify store - 

LTV Calculation by Lumino

How to Use Customer Lifetime Value in Your Marketing Decisions

Now that we have calculated our LTV, let’s understand how this data can be used to profitably run and scale your business. You can use LTV to shape your marketing and product development strategies. Here's how you can leverage LTV:

  1. Segmentation and Personalization: Segment your customers based on their LTV and create targeted marketing campaigns tailored to their specific needs and preferences. 
  2. Customer Retention Initiatives: Invest in customer retention strategies to increase the lifespan and value of your existing customer base. You can offer loyalty programs, personalized recommendations, and exclusive discounts to different segment of customers based on their LTV
  3. Investment Allocation: Allocate marketing resources and budgets based on the LTV of different customer segments. Maximizing marketing ROI by focusing on high-value customers and reducing spends on  acquisition channels that bring in lower-value customers.
  4. Product Development and Upselling: Develop products or services that cater to the needs of your high LTV customers, and implement upselling and cross-selling strategies to increase the average order value for each customer.

In conclusion, understanding and utilizing Customer Lifetime Value is an indispensable aspect of running a successful Shopify store. By harnessing the power of this metric, you can make data-driven decisions, improve customer relationships, and drive sustainable growth in the competitive e-commerce landscape.