Net Promoter Debate: The Measurement and Meaning of Customer Loyalty (Free White Paper)

The Net Promoter Score (NPS) is used by many of today’s top businesses to monitor and manage customer relationships. Fred Reichheld and his co-developers of the NPS say that a single survey question, “How likely are you to recommend Company Name to a friend or colleague?”, on which the NPS is based, is the only loyalty metric companies need to grow their company. Despite its widespread adoption by such companies as General Electric, Intuit, T-Mobile, Charles Schwab, and Enterprise, the NPS is now at the center of a debate regarding its merits.

I have conducted some research on the Net Promoter Score (NPS). Turns out, the claims of the NPS developers are grossly overstated and misleading. I have summarized my findings (along with the current research on the topic) in a free white paper. You can download a free copy of the white paper by clicking the link below.

Free white paper on NPS debate

Bob

Customer Loyalty and Goal Setting

All companies who use customer loyalty surveys strive to see increases in their customer loyalty scores. Improving customer loyalty has been shown to have a positive impact on business results and long-term business success. Toward that end, executives implement various company-wide improvements in hopes that improvements in customer loyalty scores will follow.

One common method for improving performance is goal setting. There is a plethora of research on the effectiveness of goal setting in improving performance. In the area of customer satisfaction, what typically occurs is that management sees that their customer loyalty score is 7.0 (on a 0-10 scale) at the start of the year. They then set a customer loyalty goal of 8.0 for the end of the fiscal year. What happens at the end of the year? The score remains about 7.0. While their intentions are good, management does not see the increases in loyalty scores that they set out to attain. What went wrong? How can this company effectively use goal setting to improve their customer loyalty scores?

Here are a few characteristics of goals that improve the probability that goals will improve performance:

Specific. Goals need to be specific and clearly define what behaviors/actions are going to be taken to achieve the goal and in what time-frame or frequency these behaviors/actions should take place. For example, a goal stating, “Decrease the number of contacts with the company a customer needs to resolve an issue” does little to help employees focus their efforts because there is no mention of a rate/frequency associated with the decrease. A better goal would be, “Resolve customer issues in three or fewer contacts.”

Measurable. A measurement system needs to be in place to track/monitor progress toward the goal. The measurement system is used to determine whether the goal has been achieved and provides a feedback loop to the employees who are achieving the goal.

A common problem with using customer loyalty scores as the metric to track or monitor improvements is that satisfaction goals are still vague with respect to what the employees can actually do to impact satisfaction/loyalty scores. Telling the technical support department that the company’s customer loyalty goal is 8.0 provides no input on how that employee can affect that score. A better measure for the technical support department would be “satisfaction with technical support” or other technical support questions on the survey (e.g., “technical support responsiveness,” technical support availability”). We know that satisfaction with technical support is positively related to customer loyalty. Using these survey questions for goal setting has a greater impact on changing their behaviors compared to using vague loyalty questions. Because satisfaction with technical support is related to customer loyalty, improvements in technical support satisfaction should lead to improvements in loyalty scores.

An even better measure would be to use operational metrics for goal setting. The company must first identify the key operational metrics that are statistically related to customer satisfaction/loyalty. This process involves in-depth research via linkage analysis (e.g., linking satisfaction scores with operational measures such as hold time, turnaround time, and number of transfers) but the payoffs are great; once identified, the customer-centric operational metrics can be used for purposes of goal setting.

Difficult but attainable. Research has shown that difficult goals lead to better performance compared to goals that are easy. Difficult goals focus attention to the problem at hand. Avoid setting goals, however, that are too difficult and, consequently, not achievable. One way to set difficult and attainable goals is to use historical performance data to determine the likelihood of achieving different performance levels.

Relevant. Goals for the employees should be appropriate for the employees’ role; can the employee impact the goal? Additionally, the goal should be relevant to both the employee and the organization. Holding employees to be responsible for goals that are outside of their control (e.g., technical support representatives being responsible for product quality) is unfair and can lead to low morale.

Accepted (or mutually set). For goal setting to increase performance, employees should be allowed to participate in setting their goals. Goals that are not accepted by the recipient are not likely to be internalized and motivating. A good approach would be to get employees involved early in the process of goal setting. Let them help in identifying the problem, selecting (or understanding) the key measures to track, and setting the goal.

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 5: Measurement and Meaning of Customer Loyalty: Drivers of 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 5 of the discussion. If you missed them, read Part 1, Part 2 and Part 3 and Part 4.

Customer Loyalty 2.0 represents this advancement in the measurement and meaning of customer loyalty. The purpose of the following analyses is to provide additional validity evidence regarding the measures of loyalty, Advocacy Loyalty Index (ALI) and Purchasing Loyalty Index (PLI). A common approach to establishing the validity is to show how the ALI and PLI are related to attributes of service and product quality. Below, I employ a common method, loyalty driver analysis, used by companies to identify the key drivers of customer loyalty. This method includes analysis that shows the relationship between business attributes and customer loyalty. In the process of establishing validity evidence, I will provide real-life illustrations regarding the merits of conceptualizing customer loyalty in this multidimensional framework that can help companies increase growth through new and existing customers.

Loyalty Driver Analysis

Loyalty driver analysis enables companies to identify the business attributes that are important to ensuring customer loyalty. Companies can allocate resources to important business areas that have the greatest impact on increasing customer loyalty. While each response from a survey can be (and should be) examined to deal with specific causes of customer loyalty/disloyalty at an individual, customer level, driver analysis can be thought of as a macro look at the customer base (or customer segment). By analyzing data from a large segment of customers, driver analysis helps companies to identify the common causes of customer loyalty across these customers. Consequently, based on the results of the driver analysis, companies can make organization-wide improvements to their business processes that will have an impact on the customer segment of interest.

Two pieces of information are examined in driver analysis: 1) derived importance: the degree of impact of each business attribute on customer loyalty and 2) performance: the level of performance of each business attribute.

Derived Importance

The degree of impact each business attribute has on customer loyalty is determined. This degree of impact is indexed by a correlation coefficient (sometimes, referred to as “derived importance”) between ratings of a business attribute and a customer loyalty index (either ALI or PLI). Each business attribute has a corresponding “derived importance” that indicates the impact that the business attribute has on customer loyalty. The higher the correlation, the greater the impact that business attribute has on customer loyalty. The degree of impact (e.g., correlation coefficient) can vary from 0.0 (no impact) to 1.0 (perfect impact).

Using the Wireless Service Provider and PC Manufacturer studies, we can identify the derived importance of each of the business attributes. Given we have multiple measures of customer loyalty, each of the business attributes will have multiple derived importance, one for advocacy loyalty, one for purchasing loyalty and one for retention loyalty (Wireless study only). In both studies, respondents were asked to indicate the degree to which they agree or disagree with statements regarding their customer experience on a scale from 1 (strongly disagree) to 5 (strongly agree). Statements reflected business attributes that ranged from product quality (e.g., reliable service, PC reliability) to service quality (e.g., customer service reps, technical support reps). These ratings were correlated with each of the measures of customer loyalty, ALI, PLI and RLI.

Performance

Next, company’s performance on each business attribute (customer ratings) is calculated. Performance of a given business attribute is simply the average rating of agreement. Higher scores reflected better performance (better customer experience).  Possible performance scores could range from 1 (worst customer experience) to 5 (best customer experience). Business attributes that have low performance ratings have ample room for improvement. Business attributes that have high performance ratings have little room for improvement.

Wireless Service Provider Driver Analysis

I calculated the derived importance and performance for one of the Wireless Service Providers in the study. Table 3 contains the results for this particular Wireless Service Provider. The column labeled "Mean" reflects the performance for each of the business attributes; the columns labeled "Derived Importance on ALI," "Derived Importance on PLI," and "Derived Importance on RLI" reflect the importance for each of the business attributes on advocacy loyalty, purchasing loyalty and retention loyalty, respectively.

Table 3. Descriptive Statistics and Derived Importance of each Business Attribute for a Wireless Service Provider

In driver analysis, both the performance and derived importance are examined simultaneously to understand where improvements would have the greatest chance to improve customer loyalty. If business attributes that have a large impact on customer loyalty (high derived importance) and  have low performance ratings, companies might consider allocating resources to these business attributes in order to improve customer loyalty.  If ratings of business attributes are high, however, companies can promote these business attributes as strengths and best practices. Using both the derived importance of each business attribute and the performance (e.g., rating) of each business attribute, we can create a Loyalty Matrix (see figures below) that allows us to visually examine all business attributes at one time.

The abscissa (x-axis) of the Loyalty Matrix is the performance rating (agreement, performance, satisfaction) of the business attributes. The ordinate (y-axis) of the Loyalty Matrix is the impact (derived importance) of the business attribute on customer loyalty. The Loyalty Matrix is divided into quadrants using the average score for each of the axes. Key drivers appear in the upper left quadrant and are often referred to as Key Drivers. Key drivers reflect business attributes that have both a large impact on customer loyalty and have low performance ratings relative to the other business attributes (these key drivers appear in red). Because we have three customer loyalty indices, we can calculate three separate Loyalty Matrices, each for the specific customer loyalty index. Below are the Loyalty Matrices for a particular Wireless Service Provider.

Figure 4. Advocacy Loyalty Driver Analysis - Wireless Service
Provider

Figure 5. Purchasing Loyalty Driver Analysis - Wireless Service
Provider

Figure 6. Retention Loyalty Driver Analysis - Wireless Service
Provider

The results each of the driver analyses seem fairly consistent. We see that Customer Service Representatives (CSRs) have a relatively large impact on customer loyalty compared to the primary product offering attributes (Reliable service, Good coverage). Whether customers will be loyal to this Wireless Service Provider depend to a greater degree on the competency of the CSRs than on the product offering attributes.

Using the three Loyalty Matrices, we can draw some conclusions regarding how this particular Wireless Service Provider can increase advocacy, purchasing and retention loyalty. When employing the use of driver analysis, we typically focus on the the Key Drivers (those attributes in the upper left hand quadrant) as areas to focus if we want to improve customer loyalty. We do so because these are the attributes that have a large impact on customer loyalty and have much room for improvement. To improve loyalty, no matter how it is measured, the results of the driver analysis indicate that the company should focus on improving CSR attributes as these attributes have a relatively large impact on advocacy, purchasing and retention loyalty and have much room for improvement.

PC Manufacturer Driver Analysis

I calculated the derived importance and performance for one of the PC Manufacturers in the study. Table 4 contains the results for this PC Manufacturer. The column labeled "Mean" reflects the performance for each of the business attributes; the columns labeled "Derived Importance on ALI" and "Derived Importance on PLI" reflect the importance for each of the business attributes on advocacy loyalty and purchasing loyalty, respectively.

Table 4. Descriptive Statistics and Derived Importance of each Business Attribute for a PC Manufacturer

 

Below are the Loyalty Matrices for a particular PC manufacturer.


Figure 7. Advocacy Loyalty Driver Analysis - PC Manufacturer



Figure 8. Purchasing Loyalty Driver Analysis - PC Manufacturer

We see that there are differences in what drives advocacy loyalty and purchasing loyalty. With regard to advocacy loyalty, we see that both of the PC attributes (PC reliability, and PC features) have a big impact on advocacy loyalty, more so than the technical support attributes. Whether customers will be advocates of this PC manufacturer depend highly on the computer itself and less so on the quality of technical support. With regard to purchasing loyalty, however, we see that many of the technical support attributes (excellence, timeliness, understands needs, availability) have a relatively big impact on purchasing loyalty. Interestingly, PC attributes do not have a big impact on purchasing loyalty.

Using the two Loyalty Matrices, we can draw some conclusions regarding how this particular PC Manufacturer can increase advocacy loyalty and purchasing loyalty. To improve advocacy loyalty, the driver analysis seems inconclusive. While PC features are big determinants of advocacy, they are rated as relatively good. Consequently, there is not much room for improvement in these attributes. The technical support attributes, while rated as relatively low, do not have a large impact on advocacy loyalty. If this PC Manufacturer wants to improve purchasing loyalty, however, the results of the driver analysis indicate that they should focus on improving technical support attributes as these attributes have a relatively large impact on purchasing loyalty and have much room for improvement.

Missed Opportunities to Improve Customer Loyalty

When practitioners talk about "customer loyalty," they are usually referring to advocacy loyalty; many loyalty programs are, in fact, based solely on advocacy-related content. For example, the Net Promoter Score is based on the "likelihood to recommend" question. Additionally, the American Customer Satisfaction Index (ACSI) is based on the "satisfaction" question. If the PC manufacturer relied solely on this question, the results of the driver analysis suggests that they should focus ensuring that their PCs are reliable and has features their customers want. This driver analysis found that many of the technical support items were not important in improving advocacy loyalty (they appeared in the lower left quadrant). The use of advocacy-related loyalty questions as a way of measuring and defining customer loyalty limits improvements in acquiring new customers.

The driver analysis using the PLI painted an entirely different picture. We saw that many technical support items were now key drivers of customer loyalty. Expanding the definition of customer loyalty to include increased purchasing intentions clearly shows that loyalty can be improved beyond mere referrals of new customers. Companies can now identify business attributes that, when improved, would increase purchasing loyalty of existing customers. Improving technical support for the PC Manufacturer would increase revenue from existing customers through increasing their purchasing behavior (buy different products and increasing their purchasing frequency). Looking at both advocacy loyalty and purchasing loyalty, this PC manufacturer can maximize revenue through both new and existing customers.

Summary

The evidence from two separate studies shows 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.

The results of the present analysis show that, as expected, the measures of customer loyalty are logically related to the customer experience. Customers who have a better customer experience tend to have higher levels of customer loyalty. Furthermore, the impact that the business attributes have on customer loyalty depends on the customer loyalty index that is used.

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.