Measurement and Meaning of Customer Loyalty

Buffer

The use of customer loyalty survey data to help manage customer relationships has received much technological innovation over the past decade. Web-based surveys provide an easy vehicle for customers to provide feedback. For example, individual customer concerns are addressed through the use of automated prompts (typically in the form of emails) to Account team members who are responsible for quickly resolving specific causes of customer loyalty. Additionally, organization-wide customer loyalty issues are identified through automated analyses (e.g., driver analysis) which highlight common causes of customer loyalty/disloyalty. Furthermore, customer survey results are accessible 24×7 by all employees through Web-based reporting tools. Finally, companies even link customer survey data to their CRM systems to enhance day-to-day account management with both attitudinal data and operational data. It is clear that efforts in the field of customer loyalty have simplified the process of data collection, analysis, reporting, and integration with existing business systems.

While the quality of the customer loyalty survey process has seen a great deal of improvement in business settings, the quality of the measurement and meaning of customer loyalty has not kept pace. Our latest research on customer loyalty, however, tries to narrow this gap. Customer Loyalty 2.0 represents this advancement in the measurement and meaning of customer loyalty. The purpose of the discussion is to demonstrate the limitation of current customer loyalty measures, highlight our latest research findings, introduce the idea that customer loyalty measured through surveys is best conceptualized as a multidimensional entity. That is, customer loyalty is not a single entity. More on that idea later.

Customer Relationship Management and Customer Loyalty

While many objective measures of customer loyalty exist (e.g., defection rate, number of referrals), customer surveys remain a frequently used way to assess customer loyalty. There are a few reasons for the popularity of customer survey use in customer experience management. First, customer surveys allow companies to quickly and easily gauge levels of customer loyalty. Companies may not have easy access to objective customer loyalty data or may simply not even gather such data. Second, results from customer surveys can be more easily used to change organizational business process. Customer surveys commonly include questions about customer loyalty as well as the customer experience (e.g., product, service, support). Used jointly, both business attribute items and loyalty indices can be used (e.g., driver analysis, segmentation analysis) to identify reasons why customers are loyal or disloyal. Finally, objective measures of customer loyalty provide a backwards look into customer loyalty levels (e.g., defection rates, repurchase rates). Customer surveys, however, allow companies to examine customer loyalty in real-time. Surveys ask about expected levels of loyalty-related behavior and lets companies “look into the future” regarding customer loyalty.

While there has been a change in business nomenclature around the application of customer surveys from “customer relationship management” to “customer experience management,” the analytical techniques used to understand the survey data (e.g., segmentation analysis, driver analysis) remain exactly the same. The ultimate goal of customer loyalty survey analyses, no matter what business nomenclature you use, is to identify the reasons why customers are loyal or disloyal. You might think of customer loyalty as the ultimate criterion in customer relationship/experience management.

Customer Loyalty and Financial Performance

There are several objective measures of customer loyalty:

• Number of referrals: Word of mouth/Word of mouse
• Purchase again
• Purchase different products
• Increase purchase size
• Customer retention/defection rates

Based on the objective measures of customer loyalty, we can see how company financial growth can occur through the increase in customer loyalty. Through the referral process, companies can grow through the acquisition of new customers. The idea is that the customer acquisition process relies on existing customers to promote/recommend the company to their friends, who, in turn, become customers. Another way of strengthening the financial growth of a company is through increased purchasing behavior (e.g., increase amount of purchases, purchase different products/services) of existing customers. Finally, company growth is dependent on its ability to not lose existing customers at a faster rate than they acquire them. For example, customer defection rate is an important metric in the wireless service industry where customer defections are common.

Measurement and Meaning of Customer Loyalty

Customer loyalty, when measured through surveys, is typically assessed through the use of standard questions or items, mirroring the objective measures listed earlier. For each item, customers are asked to rate their level of affinity for, endorsement of, and approval of a company. The items usually ask for a rating that reflects the likelihood that the customer will exhibit positive behaviors toward a company. Commonly used customer loyalty survey questions include the following items:

• Overall satisfaction
• Likelihood to choose again for the first time
• Likelihood to recommend
• Likelihood to continue purchasing same products/services
• Likelihood to purchase different products/services
• Likelihood to increase frequency of purchasing
• Likelihood to switch to a different provider

The first question is rated on a scale (e.g., 0 = Extremely dissatisfied to 10 = Extremely satisfied. The remaining questions allow respondents to indicate their likelihood of behaving in different ways toward the company (e.g., 0 = Not at all likely to 10 = Extremely likely. Higher ratings reflect higher levels of customer loyalty.

Attitudinal Measures of Psychological Constructs

Constructs are unobservable entities we use to describe a set of observable indicators. In the survey world, these observable indicators are responses to questions. We use constructs in everyday life when we describe the state of people. We say Mary is “happy” because she laughs, smiles, and jokes. We say that John is “depressed” because he is frowning, slouching, and is looking downward. In this case of attitudinal measures, we use constructs to describe a set of responses to standard questions. Tests/Surveys are developed that measure “anxiety,” “job satisfaction,” “supervisor support,” “introversion/extroversion,” and “customer loyalty.” The questions on these tests/surveys bring the constructs into the observable world. Questions in inventories measure personality traits; questions in surveys measure customer loyalty; questions in employee surveys measure supervisor support. Figure one illustrates the relationship between the construct and the observable indicators (questions).

When measuring a particular psychological construct, researchers develop many items in order to calculate an overall score as a measure of that particular construct. Everything being equal, we know that scores based on many questions are more reliable than any one of the single scores. Consider measuring your child’s math ability in college. In general, you would have more confidence that a score based on a 50-item math test would be a more reliable indicator of your child’s math ability than a score based on any single item from that test.

Individual Loyalty Items vs. Composite Loyalty Scores

Customer surveys, oftentimes, include multiple loyalty questions. There are different approaches in how these loyalty questions are used. One approach is to use single loyalty questions as the loyalty measure. For example, Reichheld (2006) recommends the use of the “likelihood to recommend” as the single best question to use as a measure of customer loyalty. Still other researchers use “overall satisfaction” as their key measure of customer loyalty (Fornell, et al., 2006). Another approach is to use a composite score (typically averaging across items) based on several loyalty questions. The  question now becomes, “When multiple loyalty items are used in a customer survey, should we use a composite score as our ultimate loyalty criterion or use each item as unique measures of customer loyalty?”