True Test of Loyalty - Article in Quality Progress

Read the study by Bob E. Hayes, Ph.D. in the June 2008 edition of Quality Progress magazine titled The True Test of Loyalty. This Quality Progress article discusses the measurement of customer loyalty. Despite its importance in increasing profitability, customer loyalty measurement hasn’t kept pace with its technology. Using advocacy, purchasing and retention indexes to manage loyalty is statistically superior to using any single question alone. These indexes helped predict the growth potential of wireless service providers and PC manufacturers. You can download the article here.

Customer Loyalty 2.0, Part 6 - Advocacy and Purchasing Loyalty: Company Comparisons and Predicting Business Growth

The measurement of customer loyalty has been a hot topic lately. With the latest critiques of the Net Promoter Score coming in from the both practitioners and academic researchers, there is much debate on how companies should measure customer loyalty. I wanted to formally write my thoughts on this topic to get feedback from this community of users. Much of what I will present here will be included in the third edition of my book, Measuring Customer Satisfaction. I welcome your thoughts and critiques. Due to the length of the present discussion, I have broken down the entire discussion into several parts. I will post each of them weekly. Below is Part 5 of the discussion. If you missed them, read Part 1, Part 2, Part 3, Part 4 and Part 5.


The earlier analyses provided evidence of the reliability and validity of the the new loyalty measures, Advocacy Loyalty Index (PLI) and the Purchasing Loyalty Index (PLI). These measures had excellent measurement properties with respect to reliability (e.g., measurement precision and validity (e.g., appeared to be measuring distinct and meaningful constructs). The following analyses will extend the present findings to examine the use of the ALI and PLI across different companies. These analyses will help us determine if the new loyalty measures, ALI and PLI, have adequate measurement precision to be able to:

• detect loyalty differences across companies


• identify reasons for loyalty/disloyalty

Additionally, I will examine the degree to which the loyalty indices are predictive of future business growth. I will explore this possibility by correlating the subjective loyalty indices with objective loyalty metrics. This sort of analysis will help us identify the extent to which these loyalty indices are predictive of future business growth due to both new and existing customers.

Detect Loyalty Differences on Advocacy Loyalty and Purchasing Loyalty

For the two studies cited earlier, we are able to rank each of the companies surveyed within their industries on both advocacy loyalty and purchasing loyalty. To do so, we simply calculate the Advocacy Loyalty Index and Purchasing Loyalty Index by averaging the ALI and PLI over each of the company's respective respondents. Figure 9 contains the averages of the ALI and PLI for each of the PC Manufacturers and Figure 10 contains the averages of the ALI, PLI and RLI (Retention Loyalty Index) for each of the Wireless Service Providers.

Figure 9. Bar Graph of Loyalty Scores for PC Manufacturers

Figure 10. Bar Graph of Loyalty Scores for Wireless Service Providers

The results showed that the loyalty indices are able to detect meaningful difference across the companies. There were statistically significant differences across the companies within each of the industries. Specifically, the PC Manufacturer that ranks at the top for advocacy loyalty and purchasing loyalty is Apple. Among the remaining manufacturers, Hewlett-Packard, Compaq, and Dell have higher levels of advocacy loyalty among their customers compared to the rest. After considering Apple’s dominance in purchasing loyalty, the difference among the remaining PC providers is relatively small. Hewlett-Packard, however, has significantly higher levels of purchasing loyalty compared to Dell. Additionally, Gateway reported the lowest levels of purchasing loyalty.

The wireless providers that rank at the top for advocacy loyalty are Alltel and Verizon. The difference in the ALI among the top four providers, however, is not statistically significant. Advocacy loyalty for all four of these providers, however, is significantly higher than advocacy loyalty of Sprint/Nextel. Similar to the findings using advocacy, while Alltel and Verizon have the highest purchasing loyalty score, the top four providers (Alltel, Verizon, T-Mobile, and ATT) do not differ significantly amongst each other. However, Alltel, Verizon and T-Mobile have higher purchasing loyalty than Sprint/Nextel, suggesting that Alltel, Verizon and T-Mobile have customers who are more likely to increase their purchasing behavior compared to the customers of Sprint/Nextel. Results showed that Verizon, Alltel, and ATT have customers who are the least likely to switch to a different provider; about 25% of their customers say are likely to switch providers within the next 12 months). A higher percentage of T-Mobile and Sprint/Nextel customers say that they are likely to defect to their provider’s competition within the year.

The analyses show that the loyalty indices, ALI and PLI, are sensitive enough to detect differences across the different companies; it appears that the measurement precision of each of the loyalty scales allows researchers and practitioners the ability to understand differences across different groups of customers (in this case, the groups are companies).

Hayes Loyalty Grid and Potential Business Growth

The ALI measures the degree to which customers are advocates of the company. The PLI measures the degree to which customers are likely to increase their purchasing behavior. The two loyalty metrics, ALI and PLI, assess the types of potential business growth that companies are likely to experience in the future. The ALI assesses potential new customer growth while the PLI assesses potential purchasing growth. The Hayes Loyalty Grid charts the ALI and PLI which visually displays the different types of relative growth potential for each of the companies. Two examples of the Hayes Loyalty Grid appear in Figures 11 and 12. Figure 11 represents the Hayes Loyalty Grid for the PC industry and Figure 12 represents the Hayes Loyalty Grid for the wireless service provider industry industry.

PC Manufacturers

As is seen in Figure 11, there is considerable variability across PC manufacturers with respect to their growth potential. Clearly, Apple Computers have high levels of both advocacy loyalty and purchasing loyalty. They, compared to other PC manufacturers, should expect to see faster growth with respect to acquiring new customers and increasing the purchase behavior of existing customers.

As you can see in the Hayes Loyalty Grid, HP (HP) and Apple appear in the upper right quadrant, suggesting that both PC manufacturers are poised to experience faster growth with respect to customer acquisition and increased purchases from existing customers. HP (Compaq) and Dell’s growth potential are on par with the industry average. Located in the lower left quadrant, Gateway, Toshiba and emachines, relative to their competitors, will experience slower growth in both customer acquisition and increased purchases from existing customers.

Figure 12. Hayes Loyalty Grid for the PC Industry

Wireless Service Providers

As you can see in the Figure 12, Alltel and Verizon appear in the upper right quadrant, suggesting that they are poised to experience faster growth with respect to customer acquisition and increased purchases from existing customers. Additionally, T-Mobile customers indicate that they are likely to increase their purchase behavior at the rate comparable to the customers of Alltel and Verizon.

ATT's new customer growth potential is on par with their industry average. Located in the lower left quadrant, Sprint/Nextel, relative to their competitors, will experience slower growth in both customer acquisition and increased purchases from existing customers.

Figure 12. Hayes Loyalty Grid for the Wireless Industry

To understand how well the ALI and PLI predicted future growth, objective loyalty metrics for the Wireless Service Providers were collected for Q3 2007 (fiercewireless.com and quarterly reports from provider's respective Web sites). Each of the loyalty indices were correlated with each of the following objective loyalty metrics:

• Average Revenue Per User (ARPU) Growth (Q2-Q3 2007)


• Churn for Q3 2007 (*reverse coded so higher scores reflected better retention)


• % Total of New Customer Growth (Q2-Q3 2007) - estimated from churn rate and net new customers

These data are located in Table 5.

Table 5. Objective Loyalty Metrics for Wireless Service Providers

The correlations for each of the loyalty indices with each of the objective loyalty metrics are located in Figure 13. As we can see, survey data from Q2 2007 was closely linked to objective Q3 2007 business metrics, suggesting that survey data are predictive of future business growth. For example, ALI and PLI were both predictive of Average Revenue Per User (ARPU) growth. That is, companies who had higher ALI and PLI scores also reported greater ARPU growth from Q2 to Q3 2007.  Additionally, The Retention Loyalty Index was highly predictive of actual churn rates for Wireless Service Providers. That is, companies who had higher RLI scores had lower churn rates compared to companies who had lower RLI scores. Finally, all loyalty indices were related to the acquisition of new customers. Companies who had higher scores on they loyalty indices also experienced greater new customer growth than customers who had lower loyalty indices. While the present results are only based on the wireless industry, the findings showing the predictive power of the ALI and PLI are very compelling and deserve future research.

Figure 13. Relationship of Loyalty Indices with Objective Loyalty Metrics

Companies are not static entities; they can change business practices to address customer loyalty issues, which could ultimately impact their growth potential. The above results of the Hayes Loyalty Grid assume a static world. The results do not suggest that companies will not change their business practices to address customer loyalty issues. It is likely that these companies are currently enhancing their business processes to better manage their customers in order to provide a better customer experience and, consequently, enhance customer loyalty. The next section will use the loyalty indices to help us identify which business attributes are responsible for customer loyalty/disloyalty for each company. By identifying the top drivers of customer loyalty, specific companies will understand why their customers are loyal/disloyal and how they might be able to increase the loyalty of their customer base.

Identify Reasons for Loyalty/Disloyalty

We have seen an effective way of identifying the the business attributes that are responsible for customer loyalty/disloyalty. With the use of customer surveys, we are able to ask customers to rate the quality of the customer experience across a variety of business attributes as well as rate the level of their customer loyalty. With these data, we can calculate the correlation between each of the customer experience business attributes with customer loyalty ratings. The correlation coefficient reflects the degree to which the customer experience is responsible for customer loyalty. A high correlation indicates the business attribute is very important in ensuring customer loyalty. A low correlation indicates the business attribute is not important in ensuring customer loyalty. This correlation is referred to as derived importance.

Using the PC Manufacturer study, we can see how the different business attributes are relatively more or less important in driving customer loyalty across different companies and across the two loyalty indices. Two measures of the quality of the customer experience were calculated and used as drivers of loyalty. They were:1) PC Quality (average of three PC-related questions), and 2) Technical Support Quality (average of 5-technical support-related questions).

In general, I found that the importance of these two customer experience measures in predicting customer loyalty varied over different companies as well as the different types of customer loyalty. Specifically, in the PC Manufacturer study (See Figures 14 and 15), I found that, across all PC Manufacturers, Advocacy Loyalty is driven more by PC Quality than Technical Support Quality. Conversely, Purchasing Loyalty is driven equally by PC Quality and Technical Support Quality.

Drivers of customer loyalty varied over PC Manufacturer; PC Quality was a big driver of Advocacy Loyalty for all the PC Manufacturers except for Apple. Technical Support Quality was seen as a moderate driver of Advocacy Loyalty for most of the companies except for Apple and Toshiba.

Figure 14. Drivers of Advocacy Loyalty for PC Manufacturers

Figure 15. Drivers of Purchasing Loyalty for PC Manufacturers

Using the Wireless Service Provider study, we can see how the different business attributes are relatively more or less important in driving customer loyalty across different companies and across the three loyalty measures. Three measures of the quality of the customer experience were calculated and used as drivers of loyalty. They were: 1) Coverage/Reliability, 2) Handset Quality, and 3) Customer Service Representatives (average of four CSR-related questions).

In general, I found that the importance of these three customer experience measures in predicting customer loyalty varied over different companies as well as the different types of customer loyalty. Specifically, I found that, across all Wireless Service Providers, Coverage/Reliability and Customer Service Representatives, compared to Handset Quality, are top drivers of Advocacy and Purchasing Loyalty. Conversely, top drivers of Retention Loyalty vary greatly by the provider.

Figure 16 illustrates the impact of these attributes on Advocacy Loyalty. As we can see, while the three business attributes have a relatively large impact on advocacy loyalty for many of the providers, they have a significantly smaller impact on advocacy loyalty for Verizon Wireless. Additionally, both Coverage/Reliability and Customer Service Representatives have a larger impact on Advocacy Loyalty compared to Handset Quality (with the exception for Verizon Wireless).

Figure 16. Impact of Business Attributes on Advocacy Loyalty for the Wireless
Service Providers

Figure 17 illustrates the impact of the business attributes on Purchasing Loyalty. Overall, the business attributes tend to have a lesser impact on Purchasing Loyalty than on Advocacy Loyalty (exception is Verizon Wireless). For the majority of the providers, both Coverage/Reliability and Customer Service Representatives have a larger impact on Purchasing Loyalty than does Handset Quality. Interestingly, while business attributes had a greater impact on advocacy loyalty for Alltel compared to other companies, they had a relatively lesser impact on purchasing loyalty for Alltel compared to the other companies; in fact, handset quality had no appreciable impact on Alltel customers' purchasing behavior.

Figure 17. Impact of Business Attributes on Purchasing Loyalty for the
Wireless Service Providers

Figure 18 illustrates the impact of business attributes on Retention Loyalty. Overall, the business attributes have the lowest impact on Retention Loyalty. However, for Verizon Wireless, we see that Coverage/Reliability has a greater impact on Retention Loyalty than it does on Advocacy or Purchasing Loyalty.

Figure 18. Impact of Business Attributes on Retention Loyalty for the Wireless Service Providers

Summary

Some general conclusions can be drawn from the analysis above. These are general conclusions but, as we know, there are always exceptions to these general rules.

• Customers report higher levels of advocacy loyalty than purchasing loyalty. That is, people are more likely to be advocates of a company (e.g., recommend a company) than they are to increase their purchasing behavior toward that company.


• Advocacy loyalty, compared to purchasing loyalty, is more closely associated with the customer experience. The derived importance of the business attributes were more highly related to advocacy loyalty than purchasing or retention loyalty.
  
  
  • Other factors beyond the control of the company (e.g., customer needs, expendable income) likely play a role in the degree to which customers increase their purchasing behavior.
  
  
  • Improving the customer experience will likely improve Advocacy Loyalty more than it will improve Purchasing Loyalty
  
  
  
  


• The loyalty indices are predictive of future levels of objective loyalty metrics, suggesting that surveys are an effective way of measuring and managing customer loyalty.

For more information about the Advocacy Loyalty Index and the Purchasing Loyalty Index and more detailed information about the driver analyses in the studies reported here, you can download a free copy of executive reports on the two studies (Wireless Service Providers and PC Manufacturers) at Business Over Broadway.

Customer Loyalty 2.0, Part 4: Measurement and Meaning of Customer Loyalty: Advocacy Loyalty and Purchasing Loyalty

The measurement of customer loyalty has been a hot topic lately. With the latest critiques of the Net Promoter Score coming in from the both practitioners and academic researchers, there is much debate on how companies should measure customer loyalty. I wanted to formally write my thoughts on this topic to get feedback from this community of users. Much of what I will present here will be be included in the third edition of my book, Measuring Customer Satisfaction. I welcome your thoughts and critiques. Due to the length of the present discussion, I have broken down the entire discussion into several parts. I will post each of them weekly. Below is Part 4. If you missed them, read Part 1, Part 2 and Part 3.

Validity of Loyalty Indices

As we saw, one form of reliability, internal consistency reliability, can be indexed by Cronbach's alpha estimate. This estimate tells us the degree to which the items in the scale are correlated with each other. Scales that have items that are highly correlated with each other have higher reliability compared to scales that have items that are not highly related to each other. Validity, on the other hand, involves a process of understanding what the scale is actually measuring. Now that we have scales that are highly reliable, the next step is to gather evidence that helps us identify what the scales are actually measuring. Establishing the validity of scales is a more complex process than establishing the reliability of scales.

Content-related Evidence

Content-related evidence is concerned with the degree to which the items in the scale are representative of a “defined universe” or “domain of content.” The domain of content typically refers to all possible items that could have been used in the scale. The goal of content-related validity is to have a set of items that best represent the defined universe.

Our initial set of loyalty questions appears to be a good representation of our "defined universe" of possible customer loyalty items. We set out to measure customer's behaviors/attitudes that help companies grow. Company growth occurred in two general ways: 1) through acquiring new customer and 2) increasing purchasing behaviors of existing customers. While our set of loyalty items might not be exhaustive of all possible loyalty items that could have been asked in the survey, they do appear to be a representative sample of all loyalty questions.

Criterion-related Evidence

Criterion-related validity involves demonstrating the statistical relationship between the loyalty indices and other variables. Based on customer loyalty theory and research, we expect that our measures of customer loyalty would be related to certain types of variables (e.g., customer satisfaction). Previous loyalty models support the notion that customers who perceive the customer experience as good (e.g., good product and service quality) would report higher levels of customer loyalty compared to customers who perceive the customer experience as poor.

In this section, I will explore criterion-related evidence of the loyalty indices. First, I will present the descriptive statistics of and correlations among the loyalty indices. Second, I will examine the relationships of the customer loyalty indices with other variables (e.g., customer tenure, number of prior recommendations). In addition to helping us determine the quality of these loyalty indices, examining these relationships provides a practical vehicle by which we can better understand how companies can better manage the customer relationship and improve customer loyalty.

Descriptive Statistics of and Correlations among Loyalty Indices

The descriptive statistics of and correlations among the loyalty indices are presented for both the Wireless Service Provider study and the PC Manufacturer study below.

Table 1. Descriptive Statistics and Correlations for Wireless Service Provider Study

 

Table 2. Descriptive Statistics and Correlations for PC Manufacturer Study



We see that Advocacy Loyalty was substantially higher than Purchasing Loyalty for the Wireless study (7.29 vs. 5.63) and PC study (7.34 vs. 5.79). Customers generally report higher levels of advocacy loyalty compared to purchasing loyalty.

Next, let us examine the relationship among the customer loyalty indices. Although the loyalty indices are distinct constructs (as determined by the factor analysis), they are correlated with each other. Specifically, the correlation between the ALI and PLI is .64 and .67 for the Wireless study and PC study, respectively. This correlation shows us that customers who are advocates are also somewhat more likely to increase their purchase behavior compared to customers who are not advocates. Additionally, for the Wireless study, Retention Loyalty is more strongly related to the ALI (r = .61) than the PLI (r = .31).

In the next set of analyses, I wanted to understand how the ALI and PLI were related to three customer variables: 1) the number of years they have been a customer, 2) the number of people to which customers recommended in the past 12 months, and 3) age of the customer. The results appear below for the wireless service provider study.

Customer Tenure and Customer Loyalty

There is no statistically significant relationship between customer tenure and advocacy loyalty or purchasing loyalty (see Figure 1). Both advocacy and purchasing loyalty remain flat over customer tenure. Retention loyalty, however, is significantly related to customer tenure; Customers who have been with their providers for three or fewer years are less likely to defect compared to customers who have been with their providers for five or more years. Conversely, long-tenured customers are more likely to remain.

The extent to which customers will advocate or increase their purchasing behavior seems to be independent of the length of the customer relationship (a longitudinal study would be a better test of this hypothesis, though). The extent to which customers will switch to another provider, however, is related to the length of the customer relationship. This result might be indicative of the impact of the wireless service contracts on retention loyalty; Short-term customers who were likely to switch providers likely did so after their contract expired (two-year contract lengths are typical). Consequently, the remaining customers (those who become long-term customers), by the very definition of their segment, are made up of customers who are less likely to defect.

Figure 1. Relationship between Customer Tenure and Customer Loyalty

*Retention Loyalty is the reverse coded response to likelihood to switch question; so higher scores mean a lower likelihood of switching wireless service providers.

Number of Recommendations and Customer Loyalty

The next set of analyses looked at the relationship between each of the loyalty indices and the number of people customers recommend in the previous 12 months. In the survey, customers were asked to indicate the number of friends/colleagues they recommended the provider to in the past 12 months. As you can see in Figure 2, the number of previous recommendations is strongly related to both advocacy loyalty and purchasing loyalty and is weakly related to retention loyalty. Customers who did not recommend their wireless service provider to anybody showed lower levels of advocacy and purchasing loyalty compared to customers who did recommend their wireless service provider. Retention loyalty, however, showed less of a relationship to the number of recommendations; while there is a slight increase in customer retention for customers who recommend their provider to 1 to 5 friends, retention drops back down for those customers who recommended their provider to 6 or more friends.

Figure 2. Relationship between Number of Prior Recommendations and Customer Loyalty

*Retention Loyalty is the reverse coded response to likelihood to switch question; so higher scores mean a lower likelihood of switching wireless service providers.

Surprisingly, the extent to which customers recommended their service provider has a substantial impact on their future purchase behaviors. It appears that, to increase purchasing behavior, a company should not only improve the customer service delivery system, but should also find ways to simply encourage customers to recommend them to their friends. Humans tend to behave in ways that are congruent with their beliefs. The mere act of recommending a given service provider may have the ultimate impact on purchasing behavior (“If I am recommending them, I should be purchasing from them.”).

Wireless service providers should continue to find ways to encourage customers to recommend them to their friends. The current findings strongly suggest that customers who recommend their service provider to friends may not only improve financial performance through helping grow the customer base, but also through their own increased purchasing behavior.

Customer Age and Customer Loyalty

There was no significant relationship of age with advocacy loyalty. However, customers’ age was significantly related to purchasing loyalty and retention loyalty. Younger customers have higher levels of purchasing loyalty and retention loyalty compared to older customers. Purchasing loyalty is at its highest for customers between the ages of 26-30 years. The lowest is for customers who are 51+ years. Conversely, retention loyalty is at its highest for customers who are 51+ years and lowest for customers who are 31-40 years (see Figure 3).

The relatively greater likelihood that younger customers will increase their purchase behavior may be driven by characteristics of the younger customers (e.g., keeping up with friends) or marketing that is targeted at younger customers to purchase additional features from their service provider (e.g., text messaging, ring tones, Web access). These same same reasons may encourage customers to switch to providers who offer seemingly more attractive service packages.

Figure 3. Relationship between Age and Customer Loyalty

*Retention Loyalty is the reverse coded response to likelihood to switch question; so higher scores mean a lower likelihood of switching wireless service providers.

Summary

The evidence from two separate studies show that the Advocacy Loyalty Index (ALI) and the Purchasing Loyalty Index (PLI) measure two different types of loyalty. Even though the two types of loyalty are correlated (advocates tend to be purchasers), the relationship between the ALI and PLI is not perfect, suggesting that these loyalty indices measure unique constructs. We have good evidence that the loyalty indices are each measuring some unique aspect of customer loyalty.

Customer loyalty is not a unidimensional construct, but rather a multidimensional construct that can help reliably measured. When we say a customer group has high vs. low loyalty, we need to clarify to which loyalty we are referring. It is possible that a given customer group can have different levels of loyalty (e.g., high advocacy, low purchasing). It is clear that a blanket statement about levels of "customer loyalty" can be ambiguous.

Customers are more willing to sell their friends on the merits of their wireless service provider than they are to spend more of their own money on their wireless service provider. Perhaps purchasing loyalty is lower than advocacy loyalty due to other constraints that might limit the degree to which customers are able to purchase more (e.g., limited need, limited financial resources) yet not impact the degree to which customers could be advocates for a company/product/brand.

For more information about the Advocacy Loyalty Index and the Purchasing Loyalty Index, you can download a free copy of executive reports on the two studies (Wireless Service Providers and PC Manufacturers) at Business Over Broadway.
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Customer Loyalty 2.0, Part 2: Advocacy, Purchasing and Defection Loyalty

The measurement of customer loyalty has been a hot topic lately. With the latest critiques of the Net Promoter Score coming in from the both practitioners and academic researchers, there is much debate on how companies should measure customer loyalty. I wanted to formally write my thoughts on this topic to get feedback from this community of users. Much of what I will present here will be included in the third edition of my book, Measuring Customer Satisfaction. I welcome your thoughts and critiques. Due to the length of the present discussion, I have broken down the entire discussion into several parts. I will post each of them weekly. Below is Part 2 of the discussion. Part 1 can be found here.

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?"

To answer that question, we need to understand exactly what each loyalty question is measuring. Specifically, we want to know if each of the loyalty questions is an observable indicator of the construct of "customer loyalty." If we can provide evidence that the seemingly distinct loyalty questions are really measuring the same construct, it would be appropriate to create an overall index of customer loyalty. If we can provide evidence that each loyalty question, however, measures something different from the other loyalty questions, it would be appropriate to use each loyalty question as a unique measure of customer loyalty. A statistical analysis technique that is used to understand the meaning of items is called factor analysis. Factor analysis is a data reduction technique that explains the statistical relationships among a given set of variables using fewer unobserved variables (factors). The output of a factor analysis will tell us two things: 1) The number of factors that can explain the relationships among the set of observed variables and 2) Which variables are related to which factors. Specifically, for our problem, a factor analysis will help us identify if the relationship among the set of many loyalty items can be explained by fewer factors (constructs). It is important to note that an exploratory factor analysis involves some form of judgment when determining the number of factors as well as which variables are related to the smaller set of factors. A full discussion is beyond the scope of this discussion but the interested reader can read more about this topic (Hayes, 1997). In the next section, I apply factor analysis to seven loyalty questions.

Wireless Service Providers Study (2007)

The survey was fielded in June 2007, asking a sample of 994 general consumers in the United States ages 18 and older about their attitudes toward their current wireless cell phone provider. The survey data for this study was collected by GMI (Global Market Insite, Inc., www.gmi-mr.com), who provided online data collection and consumer panels.

This particular study on wireless service providers included seven (7) loyalty questions and additional quality-related questions. About 44% of the respondents were male. Sixty-one percent of the respondents were 40 years old or younger.

To examine the dimensionality of the loyalty items, a factor analysis was conducted on seven loyalty items (see Part 1 for items). The results of the factor analysis suggested that seven items measure three (3) constructs. The figure below represents the factor pattern matrix. The elements in the factor pattern matrix are called factor loading and essentially reflect the correlation between each item and the three factors. Higher factor loadings indicate a stronger relationship between the item and the underlying factor.

The items loading on the first factor are the standard loyalty questions typically used in customer loyalty research (e.g., overall satisfaction, choose again for first time, recommend, continue purchasing). The items loading on the second factor are the specific purchasing behavior questions. The remaining loyalty question appears to form its own factor. The factor pattern matrix suggest that, instead of thinking of each item as representing seven distinct variables, the first four items measure one underlying construct, the next two items measure a different underlying construct and the last item measures yet a different construct.

As a general rule, the naming of the factors should encompass the entire set of items that load on the factor; the items that load on the first factor appear to have a strong emotional component to them, reflecting the extent to which customers advocate the company. Consequently, this factor is labeled Advocacy Loyalty. The items that load on the second factor reflect specific purchasing behaviors. Consequently, this second factor is labeled Purchasing Loyalty. The item that represents the third factor reflects defection and is, therefore, labeled Defection Loyalty. The naming of factors in a factor analysis involves some level of creativity and subjectivity. Other researchers might label the factors with different words (but probably similar words); the underlying construct being measured, however, remains the same.

PC Manufacturers Study (2007)

This survey was fielded in July 2007, asking a sample of 1058 general consumers in the United States ages 18 and older about their attitudes toward their personal computer manufacturer. All respondents were interviewed to ensure they meet correct profiling criteria, and were rewarded with an incentive for filling out the survey. The survey data for this study was collected by GMI (Global Market Insite, Inc., www.gmi-mr.com), who provided online data collection and consumer panels.

This study was slightly different than the prior study. An additional purchasing loyalty question was created to tap another element of the purchasing loyalty construct (Likelihood to increase the frequency of purchasing) and the defection question was removed. A factor analysis was conducted on all seven (7) items. The results of this factor analysis suggested that the seven questions measure two underlying dimensions. The figure below reflects the factor pattern matrix of this factor analysis (after rotation).

The results of the factor analysis of these items are similar to the results using the Wireless Provider sample. That is, it appears that the seven seemingly disparate loyalty items actually measure two underlying constructs, Advocacy Loyalty and Purchasing Loyalty.

Summary

Based on the results of the two separate factor analyses, we see that the apparently disparate loyalty questions actually only reflect fewer distinct loyalty constructs. Rather than thinking of each loyalty item as measuring some unique dimension of customer loyalty, the results indicate that there is much overlap in loyalty questions (at least how customers respond to these questions). Customers tend to respond to loyalty questions in similar ways and do not make distinctions among general loyalty-related questions. Specifically, given the Advocacy Loyalty questions, if customers rate one loyalty question high, they will likely rate the other loyalty questions high. Conversely, if customers rate a question low, they will likely rate the other questions. The same can be said for responses to the Purchasing Loyalty questions.

Satisfaction, Recommend and Purchase Same

Of particular interest are three specific loyalty items that load on Factor 1: 1) satisfaction, 2) recommend, and 3) purchase same. The Net Promoter Score (NPS) developers state that the "recommend" question is the best predictor of business growth (Reichheld, 2003, 2006). This conclusion has come under recent attack from other researchers who have found that the "satisfaction" and "purchase same" questions are just as good as the "recommend" question in predicting business growth (Fornell, et al., 2006; Keiningham, et al., 2007; Morgan & Rego, 2006). The current results cast additional doubt on the conclusions by the NPS camp. The recommend question appears to measure the same underlying construct as these other two loyalty questions. Given these loyalty questions measure the same thing, we should not expect the "recommend" question to be a better predictor of business metrics compared to the "satisfaction" and "purchase same" loyalty questions.

Objective vs. Subjective Measures of Loyalty

It is important that we make the distinction between objective measures of loyalty and subjective measures of loyalty. These objective metrics of customer loyalty have minimal measurement error associated with them. Because these metrics are not subject to interpretation, these objective loyalty metrics have unambiguous meaning. The number of recommendations a customer makes is clearly distinct from the number of repeat purchases that customer makes. I’m not saying that these measures of customer loyalty are unrelated, but that they are measurably different constructs (similar to the fact that height and weight are different constructs but are related to each other – taller people tend to weigh more than shorter people).

Measuring customer loyalty via questions on surveys is an entirely different process; customers’ ratings of each loyalty question (e.g., likelihood to recommend, satisfaction, likelihood to repurchase) become the metric of customer loyalty. Even though we are able calculate separate loyalty scores from each loyalty question (e.g., NPS, Overall Satisfaction, Likelihood to Repurchase), the distinction among the loyalty questions may not be as clear as we think. Because of the way customers interpret survey questions and the inherent error associated with measuring psychological constructs, ratings need to be critically evaluated to ensure we understand the meaning behind the ratings.

When using questionnaires to measure constructs, we need to be mindful of how the customers interpret and respond to the questions. Questions might appear to contain very different content (e.g., recommend, satisfaction, purchase same) yet the customers apparently do not make those same distinctions when ratings these questions.

Conclusions

Customers' ratings of a set of loyalty questions suggest that there are two, very general, loyalty constructs, Advocacy and Purchasing; and a third construct, Defection loyalty. The present findings suggest that we can create composite loyalty scores. These composite scores and their definitions are:

Advocacy Loyalty: reflects the degree to which customers will advocate of the company

Purchasing Loyalty: reflects the degree to which customers will increase their purchasing behavior

Defection Loyalty: reflects the degree to which customers will switch to a different company

The next part of Customer Loyalty 2.0, I will explore the differences among the measures of customer loyalty. Toward this end, I will try to identify the different antecedents and consequences of customer loyalty. Rather than thinking of customer loyalty as a one-dimensional construct, this multi-dimensional approach to and measurement of customer loyalty can help companies better understand how to improve growth through both new and existing customers.

You can download a free copy of executive reports on the two studies (Wireless Service Providers and PC Manufacturers) at Business Over Broadway.

References

Fornell, C., Mithas, S., Morgensen, F. V., Krishan, M. S. (2006). Customer satisfaction and stock prices: High returns, low risk. Journal of Marketing, 70 (January), 1-14.

Hayes, B. E. (1997). Measuring Customer Satisfaction (2nd Ed.). Quaility Press. Milwaukee, WI.

Keiningham, T. L., Cooil, B., Andreassen, T.W., & Aksoy, L. (2007). A longitudinal examination of net promoter and firm revenue growth. Journal of Marketing, 71 (July), 39-51.

Morgan, N.A. & Rego, L.L. (2006). The value of different customer satisfaction and loyalty metrics in predicting business performance. Marketing Science, 25(5), 426-439.

Reichheld, F. F. (2003). The One Number You Need to Grow. Harvard Business Review, 81 (December), 46-54.

Reichheld, F. F. (2006). The ultimate question: driving good profits and true growth. Harvard Business School Press. Boston.