Summary
Lifetime value is projected revenue per customer during her/his lifetime for your company (website).
It’s been called the most important metric for eCommerce and SaaS companies, and with good reason. It’s also one of the more underappreciated, perhaps because many brands struggle to properly define and calculate it. With this article, we are going to reveal this mystery by giving some definitions, examples, data sources, insights, how to calculate this metric, and more useful information about CLTV that business owners and managers should know.
Business overview
By measuring CLTV in relation to customer acquisition costs (CAC), companies can measure how long it takes to recoup the investment required to earn a new customer — such as the cost of sales and marketing.
At a glance, CLTV tells you how much a customer is worth to your brand and gives you insight into their overall value. Based on that information a better understanding and estimation of how much should be invested in customer acquisition and retention can make the process more transparent and clear for sales and marketing teams.
The sooner you start to track CLTV, the better information you will have about which of your marketing campaigns drive customer loyalty and increase overall revenue.
Technical overview
Step 1 – Grab your sales data
It depends on your business, but usually, when we need to calculate it for an e-commerce business we need some main data from your deals or purchases with the following characteristics:
- Customer ID
- Orders date
- Order revenue
Step 2 – Calculate derived characteristics on customer level
There is a need to split active and not active users and then from the active segment to compute the following characteristics:
- “Months since first-order” – this represents the number of months since a customer made his first purchase up to now
- Average “Number of orders per month” per customer – This characteristic divides the total number of orders for a particular customer per number of months since the first purchase
- Average “Amount per Order” per customer. This is the average order amount for a particular customer
Step 3 – Calculate how many months a single customer stays with the company
There are different approaches to compute it, for instance, it can be an average of months since the first order for all active customers.
Step 4 – Calculate average monthly revenue per customer (LTV Score)
The last step is to derive several characteristics:
- Average Revenue per month(LTV Score) = Number of orders per month X Order Amount per month
- Then we split “LTV Score” to 5 or 10 equal BINS it is called TILE technique
- Now we know “LTV Level” (BIN) for each customer and its importance based on the bins it stays.
Step 5 – Calculate overall LTV or LTV for each segment
Once you compute LTV score you can compute:
- Overall LTV if you average LTV score for all customers
- LTV for each score level if you average them based on the level they are
Example
In this example, we provide data from an online store and it covers all the calculations we spoke about.
Step 3 and 5 can be computed separately.
Table1
In Table 1 you could see every customer has a score level and it is from 1 to 5. Such an approach could help you precisely calculate the expected revenue for every customer you gain at a specific price.
Advantages and disadvantages
Advantages:
- Work on retention by increasing the number of purchases
If you’re looking at customer lifetime value as a way to justify spending more on the acquisition, you’re probably focusing on the wrong side of the equation. In actuality, the best way is to invest in retention by increasing the purchase frequency of your heavy spenders by recommending new products relevant to them using various channels.
For instance: For every 1% of shoppers who return to your eCommerce site after their first visit, your revenue increases by approximately 10%. So if you retain 10% more of your existing customers, your revenue effectively doubles.
- Reduce your churn rate
A very common technique to reduce churn rate is by identifying and focusing on customers with minimal or moderate customer lifetime value and turning them into loyal customers.
For example, if there is a decrease by 3-5 % of your churn rate your profitability can increase by anywhere from 25 to 95%.
- Incentivize repeat customers and nurture one-time customers
In eCommerce, the probability of selling to an existing customer is around 60-70%. Yet the probability of selling to a new shopper ranges from 5-20%. In addition, returning customers spend an average of 67% more than first-time customers. Knowing to focus on repeat customers—as well as getting one-time customers to make another purchase—clears the way for centering your marketing strategy on these segments in order to drive CLTV.
- Customer Loyalty
We all know about the Pareto Principle, which hypothesizes that 80% of your revenue comes from 20% of your customers. Not surprisingly, focusing on your most loyal customers makes a lot of sense for increasing your customer lifetime value.
Their overall spend. And then you calculate your brand’s CLTV, it’s easier to see and segment your highest-value customers, giving you a chance to target them with special campaigns intended to increase their loyalty—and why wouldn’t you, when just a 5% increase in customer loyalty can increase your average profit per customer by 25-95% ?
Challenges:
- It Can Be Hard to Measure
If you don’t have quality tracking systems in place, calculating CLV can be difficult. An enterprise resource planning (ERP) or customer relationship management (CRM) system can make this information easily available on an automated dashboard that tracks KPIs.
- High-Level Results May Be Misleading
Looking at a business’s total CLV can be a helpful data point, but it can also cover up problems in certain customer segments. Breaking down the data by customer size, location and other segments may provide more useful data.
- Complex calculation
Differences in products, costs, purchase frequencies, and purchase volumes can make customer lifetime value calculations complex. However, with the right tools, you can find customer lifetime value in just a few clicks.
Conclusions
In this series of articles, we explain some of the most applicable models for every online business, with business and technical details, which can be used by everyone. Meanwhile, if you want to automate your business and access our expertise in solving your problems, please contact us. Also check Catwing (AI platform for eCommerce and SaaS) a tailored solution for each customer with fast deployment, numerous models, and AI automation.
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