Not all customers are created equal. Some shoppers may discover your brand by happenstance and make a single purchase out of curiosity or convenience; others will seek you out and loyally return to your store or website without ever thinking of switching brands. Businesses of all sizes need to make complex decisions about their marketing and the types of customers they should attract, and these choices become even more challenging when it’s not clear which customers are just one-time buyers and which are die-hards for your brand.
Customer lifetime value (CLV) can serve as a barometer for your potential customers to reveal which of them will go on to become brand loyalists so that you can focus your marketing efforts on shoppers like them. There are myriad ways to analyze and apply CLV data, but brands can get started by considering three major principles:
Organize Your Data
What makes a great customer? The answer lies in the data. Integrating your customer data is the first step toward developing a CLV model and building stronger relationships with your most loyal supporters. Start by bringing together the disparate knowledge bases that may keep your data separated so that you can derive insights from the full range of information available, and be sure that your data traces the entire customer journey so that nothing’s left out. Then, once you’ve synchronized your customer data, you can begin to craft a CLV model.
Design Your Model
There are countless directions where you could take your CLV model, but realistically, your available resources will shape its dynamics. A standard CLV model would analyze variables such as average revenue per customer, average return frequency, and the variety and scope of goods or services customers look to you for, but brands with access to data scientists or other experts can develop more sophisticated frameworks.
Put Your Insights to Work
Even the most cutting-edge analysis of robust data sets won’t revolutionize your business or your brand—for that, you’ll need to operationalize your data. For example, if your CLV model revolves around which of your customers are “below average,” “average,” or “above average” patrons, you can put this data to work by identifying the potential customers who most closely resemble the “above average” group and increasing your spend on them. This will allow you to more efficiently allocate limited resources and unlock higher ROI by focusing on customers who mean the most to you.