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 Article in Quirk's Marketing Research Review

Read the study by Bob E. Hayes, Ph.D. in the October 2008 edition of Quirk's Marketing Research Review magazine titled Customer Loyalty 2.0: The NPS Debate and the Meaning of Customer Loyalty. The article summarizes the NPS methodology, including its developers’ claims and opponents’ criticisms. Additionally, this paper includes research that examines the meaning of customer loyalty as it is measured through survey questions. You can view the article online here or you can simply download a pdf version of the article here.

Measuring Customer Satisfaction and Loyalty (3rd Edition)!

Updated book on measuring customer satisfaction: Measuring Customer Satisfaction and Loyalty (3rd Edition).

Bob E. Hayes, Ph.D. updates his best-seller about how to construct, evaluate, and use questionnaires, and adds a new chapter on customer loyalty.

Included are two different methods of sampling and determining an appropriate sample size for reliable results; the reliability and validity of results; real examples of customer satisfaction measures and how they can be used; guidelines for developing questionnaires; scale development; the concept of quality; frequencies; sampling error; two methods of determining important service or product characteristics as perceived by the customer; discussion on the measurement and meaning of customer loyalty, and methods for loyalty-based management.

Readers will gain a sound grasp of the scientific methodology used to construct and use questionnaires utilizing the author's systematic approach. They will be able to pinpoint and focus on the most relevant topics, and study both the qualitative and quantitative aspects of questionnaire design and evaluation. These and many more important scientific principles are presented in simple, understandable terms.

Buy the new book here.

The Downfall of the NPS: Customer Feedback Professionals Do Not Believe the NPS Claims

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.

Fred Reichheld, the co-developer of the NPS (along with Satmetrix and Bain & Company) has made very strong claims about the advantage of the NPS over other loyalty metrics. Specifically, they have said:

1. The NPS is “the best predictor of growth,” (Reichheld, 2003)

2. The NPS is “the single most reliable indicator of a company’s ability to grow” (Netpromoter.com, 2007)

3. “Satisfaction lacks a consistently demonstrable connection to… growth” (Reichheld, 2003)

There is considerable scientific evidence disputing the findings of the NPS camp (Hayes, 2008; Keiningham et al., 2007; Morgan et al., 2006). The basic finding is that the NPS is not the best predictor of business performance measures. Other conventional loyalty questions (e.g., overall satisfaction, continue to purchase) are equally good at predicting revenue growth. Reichheld’s claims are grossly overstated with regard to the merits of the Net Promoter Score. Despite the scientific research criticizing the NPS claims, the NPS developers still presses the claim that the NPS is the best predictor of company growth.

The Net Promoter developers have not refuted the current scientific research that brings their methodological rigor into question. Instead, they only point to the simplicity of this single metric which allows companies to become more customer-centric. That, however, is not a scientific rebuttal. That is marketing.

Current Study
I was interested in understanding the opinion of other customer feedback professionals regarding the NPS debate. I recently conducted a survey in which 277 customer feedback professionals (e.g., Senior Executives, Directors, Managers and Individual Contributors of Customer Feedback Programs (CFPs)) of enterprise, medium and small businesses were asked about their company’s customer feedback program. As part of this larger study, respondents were asked to give their opinion on the NPS methodology. Specifically, respondents were asked to indicate the degree to which they agree or disagree with the following two statements:

1. The Net Promoter Score (e.g., recommend intentions) is a better predictor of growth compared to other loyalty questions (e.g., satisfaction, repurchase intentions).

2. The Net Promoter Score (e.g., recommend intentions) is a better predictor of growth compared to other loyalty indices (aggregate of recommend, satisfaction, repurchase intentions).
Additionally, respondents were asked to indicate their company’s industry percentile ranking with respect to customer loyalty. Loyalty Leaders were defined as companies whose industry percentile ranking of customer loyalty scores was 70% or higher. Loyalty Laggers were defined as companies whose industry percentile ranking of customer loyalty was below 70%.

Results
Over 80 Customer Feedback Professionals answered the two NPS questions (see Table 1). When asked to compare the NPS with other loyalty questions/items, only 26% of the customer feedback professionals agreed that the NPS is a better predictor of growth compared to other loyalty questions. When asked to compare the NPS with other loyalty indices, again, only 26% of the customer feedback professionals agreed that the NPS is a better predictor of growth compared to other loyalty indices.

When we examined the difference between the Loyalty Leaders and Loyalty Laggers, the results are much different. More Loyalty Laggers (42%) believe the NPS is better than other loyalty indices compared to Loyalty Leaders (14%).

Table 1. Percent of respondents who agreed that NPS was better than other loyalty items or indices.
Summary
The results clearly show that the NPS claims are not widely supported by customer feedback professionals. This finding is more remarkable for customer feedback professionals from companies who are Loyalty Leaders.

Yes, the NPS is a simple metric, but the issue regarding its merits is much deeper. The simplicity of the NPS does not make it the right solution; the simplicity of the NPS does not minimize the problems (e.g., research bias) of the NPS research as well as their misleading claims regarding the superiority of the NPS over other loyalty metrics. The current study showed that customer feedback professionals seem to be aware of the limits of the NPS claims. Customer Feedback Professionals need to share their concerns (along with the recent research on the NPS) with their CEOs and CMOs.

References
Hayes, B. E. (2008). Net promoter score debate: The measurement and meaning of customer loyalty. Business Over Broadway.

Hayes, B. E. (2008). Customer feedback programs best practices: An empirical investigation. Business Over Broadway.

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.
Netpromoter.com (2007). Homepage.

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.

Customer Loyalty and Customer Lifetime Value

Customer loyalty and customer lifetime value are two different, yet related, areas of study. The purpose of this discussion is to outline each area and highlight how knowledge in both areas is necessary to better understand how to grow a company.  Companies are not static entities; they make business decisions in hopes to increase customer loyalty and grow their business. The key to business growth is to make decisions that will improve customer loyalty. Customer loyalty management is the practice of determining how to maximize customer loyalty. To understand how improvements in customer loyalty will improve business growth, we need to first understand the value of customers to the organization.

Customer Lifetime Value (CLV)
Customer lifetime value reflects the present total value of a customer to the company over his or her lifetime. The concept of CLV implies that each customer (or customer segment) differs with respect to their value to the company. When we discuss CLV, we typically refer to the value of a single customer, whether that customer represents the typical customer overall or the average customer within a customer segment (e.g., West coast customer vs. East coast customer).

The generic model of CLV can be broken down as a function of four elements:

• NC: Number of customers


• NP: Number of times the average customer make a purchase each year


• CL: Average customer life (in years)


• PPS: Average profit per sale (total sales revenue – costs)/number of sales

Using these elements, we can calculate the customer lifetime value for the entire customer base (or customer segment):
CLV = NC x NP x CL x PPS

Increasing the Lifetime Value of the Customers
Organizations, using this CLV model, can now view customers as assets with a specified value that, in turn, becomes the basis for making business decisions. The goal for management, then, is to maximize the CLV to the company. To increase the lifetime value of the customers, organizations can do one or more of the following four things:

• Increase size of the customer base (or customer segments)


• Increase the number of purchases customers makes


• Increase the average customer life


• Increase profits per sale

We see that higher CLV equates to greater financial growth with respect to profits and a greater likelihood of long-term business success. It is important to note that, because the CLV is a multiplicative function of four elements, a negative value of profits (costs are greater than revenue) results in a negative CLV no matter how large the other elements of the CLV become. Therefore, before trying to manage the loyalty of a particular customer segment, it is important to know if the customer segment is worth growing or even worth having. This step involves calculating the profits per sale.

Providing a value for profit is oftentimes game of guesswork due to the lack of understanding of costs associated with a given customer relationship. Costs may be difficult to quantify due to the lack of available data needed to make such precise calculations or costs may be overlooked due to a lack of understanding of the company resources necessary to maintain relationships with customers. The estimation procedure of the profit value should be transparent and shared across the organization to ensure assumptions about its calculation are reviewed by all interested parties.

While the concept of CLV has been traditionally applied in the sales/marketing field to understand the cost of attracting new customers, more comprehensive CLV models include costs associated with other phases of the customer lifecycle. Consider the customer lifecycle model in Figure 1. We see that a customers’ tenure with the company involves three general phases, attraction (marketing), acquisition (sales), and service (service). Within each customer lifecycle phase, company resources are required to maintain a relationship with customers. Accordingly, to get a more accurate estimation of the customer lifetime value, organizations are now including the costs to service the customers. Extending beyond the costs of attracting and acquiring customers, servicing costs expend organizational resources such as customer service staff costs and employee training costs, just to name a few. These service costs, along with sales/marketing costs, should be included in the estimation of profit per sale.





Figure 1. Customer Lifecycle

Once these costs associated with a given customer group can be established, the lifetime value of that customer group can be determined. While some customer segments could be very profitable, other customer segments might not be profitable at all.

Customer Loyalty Measurement
After identifying the extent to which a customer segment is profitable or not, the organization now must make a choice whether or not they want to invest in that customer segment to increase the lifetime value of the customers in that segment. Clearly, a customer segment that is costing more to maintain than the revenue it generates should raise red flags across the organization. In this situation, the organization can either attempt to decrease the costs of maintaining these relationships or simply attrite these relationships. For a customer segment that is profitable, the organization can determine how best to increase the lifetime value of that segment through loyalty management.

In the next step, the organization needs to understand how they can increase the lifetime value of the customers in the profitable customer segments. From the remaining elements of the CLV, the organization can improve the CLV by focusing on one or more of the customer-focused elements of the CLV model. Specifically, a company can increase the size of the customer base, increase the purchasing behaviors of the customer base, and increase the tenure of the customer base.Each of the customer-centric elements of the CLV correspond directly to each the three facets of customer loyalty identified through our research on customer loyalty. These three facets of customer loyalty include 1) Advocacy Loyalty, 2) Purchasing Loyalty and 3) Retention Loyalty.

Business Model and Customer Lifetime Value
Customer surveys can result in hundreds of thousands of data points for large organizations. Consider one high-tech company who surveys their customer base bi-annually with a survey that contains roughly 50 questions (both loyalty questions and business attribute questions). Given a sample size of 14,000 respondents per survey period, this company has 1.4 million pieces of customer feedback data annually with which to help them manage their customer relationship! While most companies might not have the magnitude of customer feedback data as this company, even smaller amounts of data (20,000 data points from 20 questions and 1000 respondents) can overwhelm a company trying to use their data intelligently to manage their customer relationships.

Business Model. To help us understand how to use the data, we can employ models to help us put the data in context. There are many different types of models regarding customer satisfaction and loyalty (ACSI, 2008; Heskett, Sasser & Schlesinger, 1997) but they all have basic elements in common. A basic model incorporating common elements of each of these models appears in Figure 2.






Figure 2. Marketing/Sales/Service Business Model and the Relationship among Key Organizational Variables
(Adapted from Heskett, Sasser & Schlesinger, 1997 and ACSI, 2008)


This business model illustrates the interrelatedness of the organizational variables that ultimately impact the company’s financial performance. Empirical research shows that business growth is stimulated primarily by customer loyalty. Loyalty is a direct result of customer satisfaction, and satisfaction is largely influenced by the value of services provided to customers. Value is created by satisfied, loyal, and productive employees. Employee satisfaction, in turn, is impacted by the business strategies and internal systems that enable employees to deliver results to customers. Partners also provide products and services to joint customers and help to impact customer loyalty to the partnering company.

Customer Lifetime Value. Older models examining the relationships among organizational variables, whether purposefully or not, do not make the distinction among the different facets of customer loyalty. The distinction of the facets of customer loyalty goes beyond recent thinking regarding the simplistic notion that customer relationship management can be effectively conducted with a single loyalty item.

Increasing the lifetime value of customers requires the management of all three types of loyalty in the customer base (or segment). By measuring each type of customer loyalty, executives can more effectively manage their customer relationships by examining each type of loyalty. Loyalty management allows companies to address customer growth, purchase behavior, and customer retention. To improve the CLV, business decisions can now be targeted to address specific types of loyalty concerns.

Customer Feedback Programs Best Practices: An Empirical Investigation

Improving the customer relationship is seen as the key to improving business performance (Ang & Buttle, 2006; Reinartz, Krafft & Hoyer, 2004). In the course of this endeavor, popular business strategies emerged that have shined a spotlight on the importance of understanding customers’ attitudes, expectations and preferences. Customer-centric business strategies, such as CRM (customer relationship management) and CEM (customer experience management), focus on managing customers’ attitudes about their experience, fueling the proliferation of customer feedback programs (CFPs).

Customer feedback programs (CFPs) reflect a variety of types of customer programs where formal customer data are collected on customers’ perceptions and satisfaction programs, customer advocacy programs and customer loyalty programs. This study was designed to identify best practices regarding customer feedback programs.

A web-based survey was used to collect information from 112 customer feedback professionals on their company’s CFP. Survey administration was conducted using a Web-based survey tool provided by GMI (Global Market Insite, Inc.). Respondents were provided by CustomerThink.com and through the author’s professional network.

CFP Best Practices

Widely used (adopted by 80% or more) CFP business practices by Loyalty Leaders (companies whose industry percentile ranking of customer loyalty was 70% or higher) are located in the top half of Table 1 (in descending order of adoption rate).



Additionally, customer loyalty percentile rankings and satisfaction with CFP in managing customer relationships were compared for companies who adopted a specific CFP business practice and companies who did not. Results (see Table 1) showed that companies who adopted specific CFP business practices, compared to companies who did not adopt the business practices, had higher customer loyalty percentile rankings (17% difference) in their industry and higher satisfaction with CFP in managing customer relationships (1.5 difference on a 0 to 10 scale).

Applied research helps companies gain superior customer insight through in-depth customer-centric research. This research extends well beyond the information that is gained from the typical reporting tools offered through survey vendors. Applied research can take the general form of linking operational metrics to customer feedback data. Additionally, research can also take the form of linking other constituent’s attitudinal data (e.g., employee, partner) with customer feedback data. Companies that conduct this sort of in-depth research gain the knowledge of how to better integrate the customer feedback into daily processes.

Executive support and use of customer feedback data as well as communication of the program goals and the customer feedback results helps embed the customer-centric culture into the company milieu. Executive use of customer feedback in setting strategic goals helps keep the company customer-focused from the top. Additionally, using the customer feedback in executive dashboards and for executive compensation solidifies the importance of customers as a key business metric. Sharing of the customer feedback results (as well as results of applied research) throughout the company helps ensure all employees are aligned with top management’s view regarding the importance of the customer in daily operations.

The combination of a rigorous applied customer-centric research program, top executive support and use of customer feedback results, and the communication of program goals and results are key ingredients to a successful customer feedback program.

You can find the free executive summary here. For more information about the study, please contact Bob Hayes at Business Over Broadway.

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.