![](\"https:\/\/measuringu.com\/images\/referfriend.jpg\")
Customer loyalty is often a better predictor of future company growth than customer satisfaction alone.<\/p>\nCustomer loyalty is often a better predictor of future company growth than customer satisfaction alone.<\/p>\n
While customer satisfaction is an important measure of customers’ expectation, customers can be satisfied but not loyal.<\/p>\n
To measure customer loyalty, you need to use a mix of behavioral and attitudinal metrics. Here’s a synopsis of this mix of metrics as described in the chapter on measuring loyalty in my upcoming book, Customer Analytics for Dummies<\/a>.<\/p>\n The degree to which customers with many options choose to stay with one company or product line indicates loyalty. This is easily measured if you have access to customer transaction data. Simply compute the percentage of customers that have made repeated purchases over a time-frame.<\/p>\n Repurchase rates differ by product and industry. Here are some examples:<\/p>\n Whereas the repurchase rate is a behavioral metric, the intended repurchase rate is an attitudinal metric<\/a>. It is a measure of what customers say they will do at various touchpoints<\/a>, for example, buy the product or service again. Two examples from a recent survey on smartphones<\/a> found the following:<\/p>\n Asking customers if they are likely to recommend a product or service to a friend or colleague has been made famous (or notorious<\/a>) by the Net Promoter Score (NPS)<\/a>, a popular way of measuring customer loyalty through understanding word-of-mouth marketing. The measurement is based on a single question: “How likely are you to recommend {product or service} to a friend or colleague?” The likelihood-to-recommend question was found to be the best or second-best predictor<\/a> of repeat purchases or referrals in 11 out of 14 industries.<\/p>\n Some examples of Net Promoter Scores:<\/p>\n Where possible, you can sometimes determine the customers who recommended your product or service to a friend or colleague.\u00a0 Those “refer a friend” promotions that companies like DirecTV and Uber use can be associated to the customers that are helping grow your customer base.\u00a0 This provides the ability to understand who is recommending the most customers, why they are recommending, and provides another opportunity to reward your most loyal customers.<\/p>\n People’s predictions of their own behavior are notoriously unreliable<\/a>. Their recollections of past behavior are a bit more reliable. So, when you aren’t able to determine if customers have recommended, ask them\u2014ask them whether they have recommended and to how many friends or colleagues. As with all attitudinal measures, this also opens the door to asking WHY customers will or will not recommend.<\/p>\n For products and services\u2014like rental cars, restaurants, flights, and clothing\u2014that are repeatedly purchased from a single company in a competition-rich environment, the number of transactions becomes a good measure of loyalty. While metrics derived from purchase data are preferred, when they aren’t available, you can ask customers approximately how often they’ve purchased in the last year.<\/p>\n The amount spent over any extended period indicates loyalty. The amount of revenue per customer per period points directly to the likely and potential lifetime value of a customer<\/a>. Nordstrom, for example, lets its customers know each month how much they’ve spent, and rewards them with credits toward future purchases. In a competitive industry like retail clothing, consumers have many options; a high amount spent per year at a given retailer might indicate a high share of a consumer’s clothing budget, and consequently provide some gauge of loyalty.<\/p>\nRepurchase Rate<\/h2>\n
\n
Intended Repurchase Rate<\/h2>\n
\n
Likelihood to Recommend<\/h2>\n
\n
Recommendation Rate<\/h2>\n
Recalled Recommendation Rate<\/h2>\n
Number of Purchases per Year<\/h2>\n
Revenue per Customer<\/h2>\n