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Mage Q&A: How can I use AI to predict customer lifetime value (LTV)?
AI can provide solutions to predict customer lifetime value (LTV) which can help increase your bottom line by improving customer retention, brand loyalty, and growth efforts.
- What is customer lifetime value (LTV)?
- How do you calculate customer LTV?
- Why does LTV matter?
- How can AI help with predicting LTV?
What is customer lifetime value (LTV)?
Customer lifetime value (LTV) is an estimate of total revenue a customer will generate for a business throughout their lifespan. This can help determine economic decisions for a company including marketing budget, resources, and forecasting. It’s a valuable metric as retention costs less than new customer acquisition and because increasing the value of existing customers is a great way to drive growth and increase profitability.
How do you calculate customer LTV?
To calculate customer LTV, you need to calculate the average dollar amount of purchase multiplied by the number of customer purchases per year and the average length of the customer relationship in years to determine customer LTV.
Simply put, CLV = LTV × Profit Margin.
Why does LTV matter?
Customer LTV is one of the most important metrics for businesses, particularly in marketing and e-commerce. It provides great insights on the company’s long-term financial viability. Here’s how customer LTV can impact your business.
- Impact to bottom line — Customer LTV has a direct impact on your profitability. Optimizing customer LTV means receiving consistent and quality (repeat) orders from customers which allows for higher profit margins than trying to acquire new customers due to customer acquisition costs, increasing your overall return on investment (ROI).
- Improved customer targeting — With the right customer information, you can allocate and maximize your marketing budget to reach the ideal target group while spending less than competitors.
- Overall business growth — Greater margin means more money to reinvest toward business growth such as expanding your service or product, locations, markets, etc. It is recommended that businesses monitor and optimize customer LTV if they are looking for continuous growth.
By understanding the customer experience and measuring feedback at all key touchpoints, you can start to understand the key drivers of LTV. It’s a great metric to use when you have an ongoing relationship with customers (e.g. paid media subscriptions or phone contracts). It’s also useful for identifying early signs of attrition (e.g. drop in spend after first year and drop in subscription activity).
How can AI help with predicting LTV?
AI gives us the power to track, analyze, and understand important customer metrics, including digital marketing channels that drive customers to your brand. This is vital in creating strong relationships that ultimately lead to higher LTV.
Machine learning can be used to predict the number of purchases and the average purchase value in order to calculate the LTV. This can contribute to the delivery of relevant content that’s more likely to create strong relationships and convert customers. With metrics and insights, you can now prioritize your investment in each of your customers. If certain campaigns aren’t attracting or re-engaging customers, AI can let you know when it’s time to shift your efforts, saving you time and money.
Customer LTV is a measure of the average customer’s revenue generated over their entire relationship with a company. Thankfully, there are AI tools that will do the tough tasks such as calculating and modeling for businesses and provide a solution for improving your customer retention and overall profitability. With the ability to process and analyze vast amounts of data over a customer’s lifetime, AI can help marketers make metric-driven decisions to tailor their strategies and encourage customers to convert faster, purchase more, improve loyalty, and eventually become a product promoter.
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