Our team has created a comprehensive e-commerce customer lifetime value model that can be used by smaller e-commerce companies without the capabilities and resources to use more complex models. In many cases, people are looking to calculate the customer’s lifetime value to ensure your paid ads are profitable over time. So, we have included in the model a method for calculating how much you should be spending to acquire a customer from paid ads.
Current Issues with Other Customer Lifetime Value Models
Currently, more basic customer lifetime value models are making a few statistical errors, which we have corrected in our model.
No Factoring of Present Value
Our entire financial system hinges on the fact that a dollar earned today is better than a dollar earned some time in the future. Many current models do not calculate the present value of the lifetime value and if they do, are not taking into account the lifespans of customers and how some customers who spend more but over a longer period may be worth less than customers who spend less over a shorter period.
No Analysis Based on Product/Marketing Updates
Customer behavior changes influenced by changes to product or marketing must be included in the model. Product changes can allow the products to sell better upfront but could lower lifetime value over time. The inverse is also true. At the end of the day, factors beyond control continually update the LTV, so our model factors in changes happening over time to dynamically update customer lifetime value.
What Our Model is Designed to Do
Our model is designed to provide a more comprehensive customer LTV analysis than the more simplistic models while retaining the ability for small businesses to be able to crunch the numbers with the limited data that most small businesses have. We differentiate our analysis in two main ways.
Factor in the Present Value of the Customer
Our model takes into account that a customer who spends slightly less money in a shorter period will be worth more than a customer worth more over a longer period. We discount the orders from the customer by the current risk-free interest rate from the US Treasury, but you can use any rate you’d like.
Gives More Weight to Recent Customers
If a customer is actively placing orders and has not yet reached the end of the customer’s lifetime, they should be weighted more in the calculation because they are a recent occurrence. New products, marketing, and a plethora of other factors affect lifetime value, so weighing the most recent data more allows the model to dynamically update with the newest product and marketing changes happening in the business.
Feeding the Model
The data inputs of the model are as follows. We recommend no less than 100 customer records to ensure statistical significance. Excel can input this data automatically depending on your data storage provider.
Number Input
#1 Customer ID
#2 Customer Original Purchase Data
#3 Date of Last Purchase
#4 Average Revenue (or Gross Profit) per Order
#5 Total Number of Orders
#6 Current Customer Acquisition Cost
#7 Current Treasury Yield (Discount Rate)
#8 Lambda Value*
*Lambda is a measure of how much weight we wish to apply to newer data versus older data.
Once the model has access to that data, the following information is calculated automatically.
Calculations
Now that the basic data has been put in, we can calculate the following metrics:
Metric Formula
Years of Customer Relationship Original Purchase Date - Last Purchase Date
Orders per Year Total Orders / Years of Customer Relationship
Simple LTV Average Revenue per Order * Years of Customer Relationship * Orders per Year
Years Since Last Purchase (Today - Date of Last Purchase) / 365
Present Value LTV PV(Treasury Rate, Years of Customer Relationship, -Average Revenue per Order * Orders per Year, 1)**
Linear Decay Recency Factor Years Since Last Purchase / Max(Years Since Last Purchase)
Linear Recency Adjusted LTV PV LTV * Linear Decay Recency Factor
Exponential Decay Recency Factor EXP(-Lambda* * Years Since Last Purchase)
Exponential Decay Adjusted LTV PV LTV * Exponential Decay Recency Factor
LTV:CAC Ratio PV LTV / Current CAC
**1 is used to signal payments are made at the beginning of the period because the customer makes their purchase on the first day of the period.
Outputs
The model outputs averages for each of the metrics, giving both weighted and unweighted data to both how far in the past the customer is and the total length of time the person was a customer(present value calculations).
| Output (Averages) | Implication |
|---|---|
| Customer Original Purchase Date | Tells you when you were acquiring the most new customers |
| Date of Last Purchase | Displays whether customers are continually reordering often |
| Average Revenue per Order | Amount customers typically spend per order |
| Total Orders | Average amount of orders per customer |
| Years of Customer Relationship | Length of time customers periodically order |
| Orders per Year | Average number of orders per year |
| Simple LTV | Unadjusted average amount people typically spend |
| Years Since Last Purchase | Amount of time since most customers in the dataset have purchased |
| Present Value LTV | Present value of the customer average discounted at the risk-free interest rate |
| Linear Recency Adjusted LTV | LTV adjusted for present value and the recency of the order declining linearly |
| Exponential Recency Adjusted LTV | LTV adjusted for present value and the recency of the order declining exponentially by the Lambda value |
| LTV:CAC Ratio | Present Value LTV compared to the current CAC (minimum 3:1 is recommended) |
| Recommended CAC Maximum | Based on LTV, the highest amount recommended to spend acquiring a customer |
Customer Acquisition Cost by Industry
In this section, see CAC KPIs broken down by industry.
Industry Average CAC
Fashion and Apparel $55.42
Health and Wellness $46.67
Beauty and Personal Care $73.33
Home Decor $35.42
Electronics and Gadgets $13.50
Sports Equipment and Gym Equipment $51.50
Pet Products and Pet Care $120.42
Subscription Services $100.00
Online Courses and Educational Content $66.67
Jewelry $12.50
Recommerce $86.67
CBD Products $140.00
Kitchenware $34.38
Earth-Friendly Products $73.33
Home Office Equipment $55.00
Interpreting the Results & Action Steps
Check your metrics for the following information to find out what action steps to take based on the data:
- If recency-adjusted LTV > PV LTV, LTV is trending in the wrong direction
- If recency-adjusted LTV < PV LTV, LTV is trending in the right direction
- If the LTV:CAC ratio is less than 3.0, CAC must be lowered or LTV must be raised
- If your current CAC is greater than the recommended CAC maximum, the CAC must be lowered
If you would like a copy of the spreadsheet or would like to discuss implementing the model into your business, reach out on our Contact Us page.