Digging a little Deeper into NPS metrics

Previously I discussed how the NPS metric is flawed by including satisfaction and dissatisfaction within the same metric. I pointed out that the satisfaction scale and the dissatisfaction scale do not have the same metrics/components and hence cannot be combined into a single metric such as the NPS metric. Of course, there may be other metrics that may be able to combine satisfaction and dissatisfaction based on the assumption that they will be represented accurately on the same scale. In order to understand the differences between these two scales, it is necessary to fully describe the parameters of each of the scales (satisfaction and dissatisfaction).

Although both satisfaction and dissatisfaction are often described using a Likert scale, there is no reason to believe that the units on each scale have the same value or meaning. For example, a measure on the satisfaction scale of "satisfied" (second-highest to "very satisfied") may not have the same intensity or value with a value of "dissatisfied" (second-highest to "very dissatisfied") on the dissatisfaction scale.  From another perspective, one might want to believe that the amount of customer involvement of moving from "satisfied" to "very satisfied" is equivalent to the amount of customer intention of moving from "dissatisfied" to "very dissatisfied".  Of course, this movement can be expected when we are using ordinal scales but that does not transfer to equality.  Remember that ordinal scales only indicate order but does not include a metric of distance along the scale.  Thus, the movement from one value either up or down on the scale does not require that the distance be the same.  Likewise, movement on a Likert scale for satisfaction does not require the same value for movement on the dissatisfaction scale.  Although the scales are numbered sequentially, there is no requirement for the linear relationship of the ordinal scale to accurately reflect equivalent linear values for both the satisfaction and the dissatisfaction scales. 

If in fact, decreasing satisfaction does not generally lead to dissatisfaction, then a better terminal value for least satisfied would more likely be the customer is "indifferent".  Hence a more accurate satisfaction scale would follow the sequence very satisfied, satisfied, slightly satisfied, and indifferent.  A similar scale for dissatisfaction would follow the same sequence of very dissatisfied, dissatisfied, slightly dissatisfied, and indifferent.  I am using the term "indifference" to describe in non-psychological terms that point in the customer's attitude where the customer stops caring. In this zone of "indifference".  At this point, the customer is no longer connected with the evaluation process and has become indifferent. 

As an example, I once had a consulting relationship with the company that had very high satisfaction scores and believed that their customer relationships were all strong. However, once we analyzed all the customer relationships we found that a relationship that we thought had no meaning was in fact very important. It turns out that one of the company’s largest customers had been put on credit hold because of some delay in the paperwork. Even though the customer represented the largest account, personnel in accounts receivable were trained only to collect accounts. There’s no customer training included for personnel in the Accounts Receivable department. Although the large customer did not change the supplier, it did introduce some significant stress to the company management.  The satisfiers were well taken care of and the areas of dissatisfaction that were measured were small. Hence, the NPS score was very high.  The simple answer might be that the company did not include the Accounts Receivable department as part of its metric. In that sense, a better NPS score may have been computed. However, the values of satisfaction and dissatisfaction on the metric scale would not have given had a significant cause for concern since it would not have been included in the calculated NPS metric. In this case, it almost led to the loss or at least a diminished relationship with a very large customer.

The bottom line is that using a single Likert scale for satisfaction and dissatisfaction is attempting to mix two very different parameters. Previously I have indicated that lower levels of satisfaction do not generally lead to values of dissatisfaction. Similarly, using the same logic, reducing levels of dissatisfaction does not generally lead to satisfaction. It is time to create complementary scales for satisfaction and dissatisfaction so that more accurate assessment of the satisfiers and the satisfiers of the customer relationship can be presented and understood.  It is usually reasonable to believe there is no perfect customer relationship. All customer relationships have strengths and weaknesses.   Not only is a time to create complementary scales for satisfaction and dissatisfaction, it is also time to incorporate into the metrics of the customer relationship, multiple factors that more accurately describe the company – customer relationship. Simplicity is no longer the answer.

Low-Hanging Fruit - Part 3

In the previous two blogs that discuss the elephant in the room, this blog takes the next step in describing the elephant. As we describe the elephant using such terms as customer satisfaction, customer experience, customer loyalty, net promoter score, etc. various metrics are provided with the hope of describing the elephant. Metrics used by each of these majors often contain similar measures.

Each of the metrics that researchers derived for the various characteristics of the customer have assumed that the individual metrics captured sufficient information to describe the company/customer relationship. However, as described in parts one and two of this series of blogs, are only characterizing a fraction of the elephant. What has really been captured in the various metrics noted above is the “low hanging” descriptors of the elephant. The elephant, which I have used symbolically to describe the entirety of the company/customer relationship, has only been defined using easily measured parameters.  These parameters are generally limited to the direct interaction between the customer and the company and have only short-term and limited impact on long-term memory.

First Conclusion:  current metrics represent only the low hanging measures of the company/customer relationship

The first question that comes to mind once it’s recognized that only low hanging metrics are being used to describe the company/customer relationship is what measures are missing.  Within that question are the questions relating to other customer contacts not included with the low hanging measures and what measures are missing that are not direct measures.

A comprehensive measure of the company/customer relationship requires two components; namely, direct metrics and indirect metrics.  

            The DIRECT METRICS consist of the metrics of the direct interaction between the customer and the company.

            The INDIRECT METRICS consist of the non-interactive relationship be the customer and the company continues to have an impact on both the customer and the company.

Second Conclusion: the perfect relationship between a company and a customer occurs when all metrics are maximized.

Until all the metrics, between direct and indirect, are known, measured, and maximized the likelihood of understanding the state of the customer/company relationship can not be assessed.

is there an elephant in the room? part 2

Each of the individual metrics appears to capture the chaos embedded in the relationship between the customer and the company. So when each of the metrics is carefully examined, it appears to be possible to gain insight into how well the metric works. More importantly, it becomes possible to see where the metric identifies aspects of the elephant, even though that metric is known to contain errors; either in the metric itself or the variation in the shape of the elephant.

A fact: all measurements contain errors.

Hence when all these metrics are combined, the shape of the elephant becomes more difficult to visualize and understand.  One of the reasons for this lack of visibility describing the elephant is that each of the metrics may contain different forms of measurement error which may pose a challenge to understanding the impact of each error. While it would be convenient and yield better understanding to know all aspects of the elephant, the real objective for these measurements previously described, is to increase the understanding between the customer and the company.

A fact: it is not necessary to have a perfect vision of the elephant

The real value is knowing how well the processes that are being used by the company to provide its products and services to the customer are performing. If the processes are perfect and there are no errors in the implementation of the processes, the company has delivered what it believes to be the best way of improving relationships with the customer.  There are two ways in which the company can fail to maximize its relationship with the customer.  The two ways it can fail are:

1.    1. Unable to deliver the products and services perfectly with no error

2.   2. Unable to provide the best products and services.

This information noted above provides some of the key objectives for managers and executives; namely, managers must constantly assure the processes continue to be provided errorless, and executives must constantly review the products and processes to ensure that each one meets and hopefully exceeds the expectations of the customer.

However, there one aspect that has not been considered; namely, the customer.  Only those aspects of the customer that has an active connection with the company have been considered.  BUT, is there more?   

The picture is not complete.  The metric needs further definition.

Part 3  What is the metric?

Is there an elephant in the room? Part 1

There are numerous metrics that measure the different relationships between a customer and the company.  Customer satisfaction,  customer experience, customer effort,  customer rage, customer loyalty, there may be others.

I am reminded of an elephant in the room described by several people each with a limited perspective. Each is asked to describe the animal in the middle of the room. One person grabs the tail and gives a comprehensive description. Another touches the leg and provides a comprehensive description of the leg. Another touches the trunk and like the others gives a comprehensive description of the elephant’s trunk. I could go on with more descriptions of the elephant by others with similar limited vision, but I think you get the idea, 

While it is true that each description of one aspect of the elephant is accurate there is no consistency between the metrics to define the entire elephant in the room. This analogy has some relevance to the metrics that are being used today to understand the relationship between the customer and the company. There are many people who have carefully constructed metrics that are statistically accurate and allow hypotheses to be extracted and yield valid data to describe an aspect of a customer/company relationship.

There appears to be a lot of dissent between those who have rigorously developed a specific metric to describe the elephant with others who also developed a metric different from all the others. In fact, virtually all of the metrics have been rigorously developed to describe the elephant. However, the sum of all the metrics don’t seem to accurately describe the elephant.  What is missing is a comprehensive metric that would support the many individual metrics from the tail, leg, trunk, and other specific measures of the elephant.  However, at this time there appears to be no single metric that integrates the various specific metrics currently used.

Part 2 will take the next step.

Everything is broken

  Another way of saying that everything is broken is by saying that nothing is perfect. The next several blogs are going to describe how entropy can be used as one of the most efficient ways of increasing performance in the service organization. The following list of quotes has been extracted from several articles that support this focus on entropy.

1  1. Entropy is a measure of disorder. It’s a physical law of nature that left untouched, everything will steadily deteriorate.  Taken from head scratchers.com/January 2013 post

    2.  Managing entropy in business: a demon that slowly erodes, disrupts, and destroys organizations. Taken from bizshifts.com/July 2016

    3. Entropy can show you where additional resources would make your business more efficient.  Taken from yourbusiness.azcentral.com.  No date identified.

    4. Entropy occurs in every aspect of the business. Taken from FS.blog/2018

    5. Understanding the law of nature about entropy can help prevent business deterioration. Entropy is the potential energy that is not available for work. Taken from slideshare.net/June 2013

    6. Entropy is a quantifiable value analogous to the amount of randomness in the system. Taken from fastCompany.com.  no date identified

    7. Entropy is the tendency of ordered systems to move towards disorder if left unattended. All organized systems are like this. You have to continually work to maintain them or they’ll fall into disarray. Taken from nfx.com/post/entropy. No date identified

    8. Entropy is the tendency of ordered systems to move towards disorder if left unattended.  Taken from nfx.com/entropy

    9. We find that the tendency of operational routines to decay is widespread. Taken from pubsonline.informs.org. No date identified.

As you can see in these comments taken from various articles entropy is a measure of disorder or reduction in productivity.  The purpose of this blog and following blogs is to identify the various components of entropy that can be found within the service organization. As we identify each component of entropy, the metrics needed for that component of entropy will likewise be identified.

Finally, we will create a model to combine the various components of entropy to provide management with sufficient information to reduce the disorder in the appropriate areas of the business.

Some of the components of entropy that have been identified are:

     1. Cultural entropy – measure dysfunction     

     2. Economic entropy – measure waste of capital, human resources, and materials

     3. Process entropy – measure scale entropy, capability entropy, and speed entropy.

The bottom line is that while many authors have addressed the various aspects of entropy in service, there is no integrated perspective that I have found in the literature.  Until we identify the components of entropy, we cannot develop processes that will minimize possible degradation and increase entropy.  

A closer look at apples and oranges

I recently wrote a blog about apples and oranges as a comparison with satisfaction and dissatisfaction. In that blog, I particularly related how the NPS metric was making a large assumption about satisfaction and dissatisfaction that doesn’t appear to be appropriate. I am going to take a deeper look at these two parameters. I think when we examine them we can see what makes them different.

It all starts with the fact that metrics such as satisfaction and dissatisfaction do not naturally fall onto a numeric scale. Most often these parameters are measured on what is called an ordinal scale. An ordinal scale is known for its ability to provide order to a series of values such as flavor (a little sweet, sweet, very sweet), color (dull, flat, bright) or shape (straight, slight curves, very curvy). The parameters such as satisfaction (slightly, somewhat, very satisfied) and dissatisfaction (slightly, somewhat, very dissatisfied) are most often also characterized on ordinal scales. Most customer satisfaction surveys use some form of an ordinal scale with both satisfaction and dissatisfaction identified on the scale. A simple five-point scale usually consists of the ranking from dissatisfied, slightly dissatisfied, neutral, slightly satisfied, and satisfied.

This is where the trouble begins. The first assumption is that the customer attitude change between dissatisfied and slightly dissatisfied is approximately the same as the attitude change between slightly dissatisfied and neutral. This assumption follows between two consecutive majors on this ordinal scale. Of course, is not possible to use arithmetic operations on these numbers. One of the strongest violations of this first assumption is the assumption that the attitude change between slightly satisfied and satisfied is equivalent (and this is the real problem) between slightly dissatisfied and dissatisfied.

The fallacy of this first assumption is that customer attitudes of satisfaction have the same impact of dissatisfaction. What is happening is that when the scale is designed we are saying that satisfaction and dissatisfaction on the same ordinal scale implicitly assumes is that reduction in dissatisfaction leads to satisfaction and similarly, reduction in satisfaction leads to dissatisfaction.

Tom Peters and other authors have noted that dissatisfied customers appear to have stronger feelings about dissatisfaction than satisfaction. They measured this by the number of people they communicate with. Those who are dissatisfied will generally tell more people about their dissatisfaction than those who are satisfied. The ratio has most often been noted as three or more times more people are told of a dissatisfaction event to those that are told of a satisfaction event. The old wives' tale would say “bad news travels fast.”

The bottom line is that satisfaction scales and dissatisfaction scales are more than numbers in a sequence. Satisfaction causes significantly different responses in customers than does dissatisfaction. It is time that we start to pay attention to the reality that measuring satisfaction and dissatisfaction on the same scale is not just misleading but wrong.

The Tyranny of the Urgent

I have a somewhat unusual combination of education and job experience.  I think I am one of the few college professors who have worked in industry as a service manager in a line management position.  From that background, I have had the opportunity to consult with a number of service managers in the computer industry.  In all of my consulting perhaps the most common problem I see with service managers is what I call the “tyranny of the urgent.”  I am sure that the phrase "tyranny of the urgent" has been used many times I am not the creator of this phrase but a firm believer in it. I believe it is a phrase that accurately captures the business of service today

Before I became a service manager with line management responsibility I would drink one or two cups of coffee a day.  Within 6 months of becoming a line manager, I was drinking six to eight cups a day.  I always had a cup of coffee in my hand.  There was always something going on that demanded my attention.  I am not sure if I got more of a rush from the adrenaline of urgent activities or caffeine.  A customer would be down and there would be no one available in the field or the call might come from the President of the company that he just received a call from a customer and thought I should look into the matter.

Urgency is a way of life in the service business.  We never seem to get involved until things aren’t working right or the customer is in trouble or the new product that was shipped has a design error that has to be fixed at the customer site.  With all of these activities crying for our attention, we become experts at handling the urgent.

It is ironic that this urgency can become a personal dictator if we let it.  This is the “tyranny of the urgent.”  The sooner you recognize it, the sooner you can stop it.  In a way, urgent activities are good because we can get an immediate “attaboy’ when we finish them.  Most of us get great satisfaction from “attaboys” and thus the cycle begins.  Perhaps that is what led us into the service profession.  I have seen service managers thrive on constant crisis.  They are not happy unless they have all of their employees working as hard as possible, a stack of calls to return (ASAP) and negotiations going on for credit on out-of-warranty returned goods.

On the other hand, there are activities that can best be described as important rather than urgent. Spending time strictly on urgent matters will not improve the performance of the service organization. It is only when time is spent on important activities (such as service planning, training, and teambuilding) that improvements can be designed and implemented which will lead to a better service organization.

The bottom line is that those urgent activities come and go, but never diminish in number or intensity.  This is the nature of the business of service.  Unfortunately, that is not the only nature of our business.  There is another type of activity in our business – IMPORTANT activities.  All of our business activities can generally be classified into one of these two groups, but usually not into both.  Success comes to service when both urgent and important activities are well-managed.

Apples and Oranges - a Problem for NPS

I think we have been missing the mark when we use NPS as a critical measurement for customer satisfaction. The challenge to this metric is that it appears to be combining "apples and oranges". The metric is defined as the difference between attractors and detractors. That is the problem!

What makes this an apples and oranges metric is that we are combining two different characteristics. The assumption in the NPS metric is that attractors and detractors are measured on the same scale. If attractors are apples and detractors are oranges, you can't subtract oranges from apples. Let me expand on this notion in the following paragraphs.

According to the literature, attractors characterize satisfaction. It carries with it the notion that attractors represent aspects of the product or service that encourages the customer to continue using the product or service because the customer is satisfied. Further, detractors characterize dissatisfaction. Dissatisfaction represents some aspect or aspects of the product or service that has a negative influence on the customer.

Here is the problem. Satisfaction is not the opposite of dissatisfaction.  A low satisfaction score may lead to indifference.  As satisfaction scores drop satisfaction does not directly lead to dissatisfaction.  Nor is dissatisfaction the opposite of satisfaction for the same reasons. Another way of saying this is that dissatisfaction is not the absence of satisfaction, and satisfaction is not the absence of dissatisfaction. Each of these terms, satisfaction, and dissatisfaction, have separate scales that may have the same number of items (such as 0 to 10). You can have a scale of 0 to 10 for satisfiers and a complementary scale of 0 to 10 for dissatisfiers. The current assumption for NPS  that the units of satisfaction are the same as units of dissatisfaction. An example of this is a one-unit positive movement of satisfaction that is assumed to be identical to a one-unit negative movement of dissatisfaction.

Consider an example of a customer who purchases at McDonald's. McDonald's uses a business model that focuses on providing food quickly (it is a fast-food restaurant) and consistent quality (although it may not be the highest quality), it is designed to provide consistency for food quality). Customers would have levels of satisfaction for speed of service and quality of food. And the scale for each could be from 0 to 10. Another component of the service experience of the customer might include using the restroom facility. The restroom facility does not have a satisfaction component. Customers don't usually seek out McDonald's for the quality of the restrooms. The restrooms are considered a convenience for customers rather than a satisfier (a feature that would encourage customers to return). The restroom scale would act as a dissatisfaction scale. It does not add to the satisfaction of the customer experience.  The restroom may use a dissatisfaction scale from 0 to 10. In this case, 10 would indicate no dissatisfaction and zero would indicate complete dissatisfaction.

When we put this customer experience in perspective we see satisfiers (apples) and dissatisfiers (oranges).  A simple question might be whether one unit on the satisfaction scale is equivalent to one unit on the dissatisfaction scale.

I hypothesize that satisfiers and dissatisfiers have different scale values. Hence the assumption that the scale for satisfaction and dissatisfaction used by NPS is equivalent is most likely not true. Customers do not stop going to McDonald's if the service is slow or the quality of food is not as good as usual. Customers will most likely stop going to McDonald's if the restrooms do not appear sanitary.  Thus dissatisfiers may act significantly different than satisfiers. 

The bottom line is that the use of the NPS metric gives a distorted view of the customer relationship.  It is based on the faulty assumption that satisfaction and dissatisfaction are equivalent and can to be measured on the same scale.  Companies that use this metric are likely to be misled about the quality of the relationship with their customers by making decisions based on this metric. It's time that we provide a metric that legitimately considers satisfiers differently than dissatisfiers.

CSAT vs CES - Does It Matter

Many commercial businesses that provide after-sale services seek ways to improve customer loyalty through their customer service organizations. In addition, companies want to utilize their resources as productively as possible. Since there are many dimensions to customer service, companies want to understand how best to utilize limited resources. The basic premise that most companies rely on is that satisfied customers are more likely to increase loyalty than non-satisfied customers. For that reason a number of metrics have been developed which attempt to measure the influence of various customer interactions. While metrics can be used both tactically and strategically, this research has focused on metrics used to measure tactical performance.

Two of the more popular metrics are Customer Satisfaction (CSAT) and Customer Effort Score (CES).  The goal of this research is determine which of these two metrics will have more impact on customer loyalty. Since each of these metrics has a different objective, the question is whether they could be used interchangeably.  Is one more effective than the other, or should they be used complementary. The CSAT metric is designed to maximize satisfaction whereas the CES metric is designed to minimize customer effort.  The first issue is whether the two metrics yield the same service factors for resource allocation. The second issue is, if the metrics do not yield the same factors for research allocation, then which is preferable. The decision criteria for selection of which metric would be preferable is the strength of the correlation relationship between the independent service factors and either the CSAT or CES metric.  The metric with the higher correlation would make the most sense to use.

This study included 6381 customers from the medical electronics and IT industries. Surveys were taken during the months of April and May 2015 and included customers in both North America and Europe. The results of the surveys indicated a relatively low correlation (0.51) between the two metrics, CSAT and CES; however, the results yielded similar relationships between the independent service factors and each metric when the total sample was considered. These results also held true when the total sample was examined independently for field service and technical support. However, when the sample was examined separately for medical electronics and IT equipment the most highly correlated independent factors  with CSAT and CES were different for the two products.

As a result of this research we notice that maximization of satisfaction and minimization of customer effort only provide similar rankings for the independent variables. Since the two product groups which were included in the study yielded different rankings for the independent variables, a generalization seems inappropriate for concluding that the two metrics are interchangeable.

 Because this research focused on two very different groups of technology products, the solution for businesses wanting to increase customer loyalty should be to consider the use both metrics since it is not clear what the criteria should be for using one metric in lieu of the other. When both metrics are used the question is which of the independent variables are most appropriate for the products.  Until an individual product area is evaluated, there is no obvious preferred method. To choose one over the other without the understanding of the relationships between the individual independent variables and CSAT or CES may lead to inappropriate allocation of resources.

Loyalty Model – part 2

In the previous blog, the general form for the construction of a loyalty model was postulated.  A base equation hypothesized that loyalty could be described by measuring the strength of the relationship between the company and its customers.   A sub-model was proposed to show that the strength of the company-customer relationship could be explained by various factors.  In this blog, the two major components of the model will be discussed.

The two components that describe the company-customer relationship are those that strengthen of the relationship and those that diminish the relationship.

Factors that enhance and strengthen the company-customer relationship may be considered relationship builders; “satisfiers.” Satisfiers represent activities or involvements that yield positive experiences by the customer and also provide value to the company. In the service business, response time tends to act as a satisfier.  For example, when services are requested, the time to respond will strengthen the relationship between the customer the company as long as the response time meets or exceeds the customer’s expectation. Consistently meeting a customer’s expectation of response time for service has been shown to strengthen the relationship with the company. More discussion of satisfiers will be provided in a separate blog.

Factors that diminish the company-customer relationship may be considered “dis-satisfiers.” A dis-satisfier is generally not the opposite extreme of a satisfier. As an example, consider customers who frequent a fast food establishment. Customers expect a reasonable quality of food to be delivered quickly. If either the quality of the food or the service delivery time does not meet the normal expectations, the customers may be disappointed but probably not dis-satisfied. However, if the restroom at the same fast food establishment has not been adequately maintained, many customers may refrain from returning to the restaurant due to a concern that lack of good hygiene in the restroom may be an indicator of lack of good hygiene in the kitchen. In this case, the quality of the hygiene in the restroom can be a dis-satisfier; but the hygiene quality of the restroom is surely not the major attraction of the fast food establishment and is not considered a satisfier. More discussions of dis-satisfiers will be discussed in a separate blog.

Company-customer relationships are not linear.  They are not a function of adding the satisfiers and subtracting the dissatisfiers, which is similar to the NPS metric when it subtracts the detractors from the promoters.  This simplification of the loyalty model makes no sense since it is equally equating positive values (satisfiers) to negative values (of the satisfiers or possibly dissatisfiers).  Beyond the basic NPS score, many analysts mistakenly assume that the relationship between each component of the model has a linear relationship with the strength of the relationship. 

As we examine the components that increase the value of the customer relationship do not assume that each component is independent and has a linear relationship with the strength of the customer relationship. There will be some satisfiers that may increase the strength of the customer relationship dramatically, whereas other satisfiers will only provide an incremental increase of improvement in the strength of the relationship.

The same logic also holds true when we discuss the dis-satisfiers in the future blogs. Always challenge the assumptions! Be curious.   

Building a Loyalty Model

The kind of loyalty model I will be discussing in the next several blogs is a basic business model that is often used in strategic management. The basic premise of the model is that customer loyalty leads to profitability. The purpose of this model is to develop an understanding of the business components that contribute to the loyalty of the customers.

In this blog I will describe the general loyalty model and a sub-model which feeds into the general loyalty model.

The hypothesis upon which the loyalty model is based is that loyalty is a direct function of the strength of the relationship between the company and its customers. Logically this makes sense since a strong relationship between the company and its customers should produce greater loyalty than a weak relationship. The heart of the loyalty model is built around understanding the components that make up the strength of the relationship. There are many variables that contribute positively or negatively to the strength of the relationship. Recent experiences between the customer and company would be an obvious component of the strength of the relationship.  A single experience between customer and the company may not significantly influence the strength of the business relationship. In fact, there is a “zone of tolerance” that ranges from minimally acceptable to extremely exceptional. As long as the other factors exist even an incident that was less than minimally acceptable may not change the strength of the relationship. Of course, this assumes that any negative experience may be resolved by the company. Otherwise, there may indeed be a negative change to the strength of the relationship between that individual customer and the company.

Therefore the simplest form of the loyalty model is as follows:

            Loyalty = constant x (strength of the relationship).

The value of the constant associated with the strength of the relationship will vary with respect to product, geography, and possibly other variables. Thus, a company with multiple products may have a different strength of relationship with their customers based solely on the product. An example would be the strength of the relationship between the Apple iPhone and its customers versus the strength of the relationship between the Apple iPad and its customers. iPhone users are known to have a very strong loyalty connection with Apple; whereas, iPad users may not have the same level of loyalty.

The strength of the relationship (loyalty) is another way of describing the business relationship between the company and its customers. The model proposed would consider variables such as level of satisfaction, recent experience, product quality, commitment to the relationship by either the company or, the customers, and the bonds may exist between the company and its customers.

The bonds that exist between the company and its customers may fall into a number of categories; such as, legal bonds (contracts), technology bonds (shared or licensed technology), knowledge bonds (shared information), social bonds, geographical bonds, cultural or ethnic bonds, and economic bonds. There may be other bonds as well.  The fact is there are many bonds that may exist and give strength to the relationship between the company and its customers (such as loyalty points).

The general form of the model for the strength of relationship is:

            S (Strength of relationship) = function of (recent experience, level of satisfaction, product     quality, service quality, commitment to the relationship, and appropriate bonds).

This model presents a challenge of quantifying each variable and assessing the strength of the relationships between each variable. This model can be simplified by assuming minimum interaction between the variables contributing to the strength of the relationship and assuming the variables are linearly related.

This discussion will be continued in the next blog where a simplified version of this loyalty model will be presented with an example. At this point, a simple loyalty model is presented that includes most of the significant variables that are usually included in loyalty discussions. Be curious.

Another look at your customers

In my previous blog I discussed the tyranny of the urgent and why is such a limiting perspective in understanding customers. I closed the blog by pointing out that the focus should always be on all your customers not just the ones who have urgent concerns. In this blog I will point out several techniques which may be useful in understanding the needs of our customers.

Several years ago I was working with the company that had a very complex product which required significant service support. The concern of the company was that the users of the product were not the decision-makers but had significant influence about decisions regarding the product. The product was a complex computer that used very sophisticated software. The end users of the product were scientists. The computer was managed by the IT department. Neither the users of the product nor the IT department made the decisions regarding the product. The decisions were made at a management level above the IT department and the users department (which included all the scientists).

The challenge with this assignment was that there was no department that was responsible for evaluating the performance of the product. Each of the three groups (users, IT, and management) were involved in the decision-making process regarding the performance of the computer. The measure of satisfaction for any one group is not sufficient to understand how well the product is meeting the needs of the customer. This product required a multidimensional model of customer satisfaction that incorporated satisfaction metrics from each of the three departments. The objectives of the metrics were to assess the satisfaction with each organization department and evaluate any inconsistencies in the measures of satisfaction between them. In other words, although satisfaction of each department was important, it was equally important to determine if there were inconsistencies or discrepancies between the scientists, IT department and upper management.

While the metrics for a current customer is important, and is the basis of most customer surveys, some additional areas of interest include the following:

1.       1. measurement of concerns from lost customers,

2.       2. specific measurements directed toward ultra-valuable customers, and

3.       3. measurement of the gaps between customer expectations and the performance delivered.

Reflecting back on the previous blog, the intention here is to provide some areas of interest beyond the basic customer satisfaction survey. The previous blog pointed out the need to separate normal survey responses from responses to customers with urgent needs for support. Curiosity is the watchword for surveys. There are many dimensions of involvement between the company and its customers. Not all contacts between the customer and company are from the end-user.

As an example, consider a previous assignment with another technology company led to the surprising conclusion that the Accounts Receivable department did not have customer skills training and was the primary reason for customer irritation. In fact, the Accounts Receivable department put IBM on credit hold because their payment was overdue. Needless to say IBM was not happy with this treatment.

If your curiosity is great enough, you may find more connections between your company and the customer that may be worth exploring. You may also find that many of the employees involved in those connections do not have customer management skills training.  Be Curious!

Who are you listening to?

The “tyranny of the urgent” is a phrase that is commonly used when working in a service environment.  The phrase reminds people that “urgent” requests often take priority over “important” requests.  Service managers often find themselves in the role of “firefighting” - putting out “the fires” of customer complaints.  Before the manager realizes it, the firefighting consumes all of his time. The executives will note what a wonderful job the service manager is doing by managing customer complaints.

In this situation the only customers whose voices are heard are those who are complaining. The large and small customers who were not complaining are overlooked. When the urgent requests consume most or all of the service manager’s time, there is little time remaining to consider what is not urgent.

In his book Exit, Voice and Loyalty, Albert Hirschman argues that unsatisfactory conditions (such as service problems) may lead customers to (1) to exit or leave the company without trying to resolve an issue, or (2) speak up and try to remedy the situation.  The “nice” customer will exit without telling you why and never return.   A “good” customer, on the other hand, is willing to speak up and tell you what is wrong and is willing to work with you to resolve an issue. A “good” customer is really your best consultant because he will tell you exactly what is wrong.

It is often said that the urgent requests are like customers pounding on the chest of the service providers and managers while the other customers who do not require immediate assistance or attention are ignored.  The fact is that customers’ lack of urgency requests may actually have more important information to share with the company. Many companies spend too much time listening to the urgent customers while ignoring other customers.

Urgent needs of some customers inhibit service managers’ time to anticipate, plan and develop future service requirements. Too many managers spend most of their time in a reactive mode. To get beyond the “tyranny of the urgent,” companies, especially service organizations, need to fundamentally change the way in which they listen to customers.

From a strategic perspective, a balance is needed between urgent problems and important problems. The urgent solutions generate “at-a-boys” for the service personnel while the non-urgent solutions pave the way for long-term success.  A simple first step to managing the “tyranny of the urgent” is to set aside a specific amount of time each week or each month to solve the problems of how to increase productivity, review service skills, parts inventory, and technology.

The bottom line is to focus on the needs of all your customers.  There are three tactics that will provide the first steps to leave the “tyranny of the urgent”.

Step one: Conduct interviews with customers who have left your company.

Step two: Engage those customer segments that are not being addressed by those customers with urgent needs.

Step three: Understand why customers left you in the past. 

Be curious! You may discover/uncover challenges and opportunities you never realize existed.

A Different Perspective

I found this short comment from the Brisbane, Australia newspaper.  I am not sure when it was published –but it is reasonable to assume this aspect of loyalty is still around.  I doubt this has happened only once – it may be more pervasive than we want to believe.  There is a naïve perspective that perfection of loyalty programs is near – maybe not.

Be a loyal customer, be taken for a fool

I was a passive consumer, believing loyalty is virtuous and appreciated by business. Having chosen products and providers I stick with them, anticipating that long-term allegiances will have their rewards.

I have learnt, however, that a more apt description of my conduct was ''mug punter''.

For more than 15 years I have happily coughed up for cable internet that served its purpose adequately. The cost disappeared painlessly from our bank account each month and we rarely troubled our allotted usage limitation.

Then we did. On the last day of the month our internet connection slowed to a crawl. Thinking it a technical malfunction, I rang a fellow at an Indian help desk only to learn that I had been ''throttled back'' for the day, having finally burst through a 12-gigabyte glass ceiling.

After 15 years of good behavior I thought that I might have earned a little leeway. Why, even the RTA and the courts have a heart. Perhaps a small draw down from the huge bank of unused gigabytes built up over the years.

I started to look around. My limit had become minuscule with the passing of the years and technological advances. Non-customers were being tempted with gigabytes beyond imagining, at breakneck speeds, for a pittance. Whither my perceived reward for loyalty?

I spoke to another friend in India. He immediately more than quadrupled my limit, increased my speed fivefold and knocked $10 a month off my bill.

Rather than feeling a surge of gratitude, I felt cheated. They had been ripping me off, probably for years. I decided to test all the waters in which I swim. My gas and electricity tariffs dropped. My insurance renewal premium quote was instantly reduced. All goodwill I might have felt towards those I once thought of as preferred suppliers evaporated. Worse, I now have ill will towards those who took advantage of my loyalty.

I now realize one can't sit idly by and expect anyone to realize the absolute value of a lifetime customer. So I whine and moan and price check. It's a waste of my time and ultimately an expensive way for business to do business, but at least I know that if it chews up the gigabytes, I only have to blast a call center somewhere to rectify the problem.

Illegal Loyalty Programs

What could be illegal about a customer loyalty program? It seems that almost every business has some form of loyalty program.  There are loyalty programs for grocery and clothing stores, airlines, and just about every other business that would like to create some form of relationship (loyalty) with their customers. An article written by Tim J. Smith, PhD identifies a dimension of customer loyalty that the government finds illegal.

A particular case noted in his article is Eaton Corporation that was found guilty of antitrust on the grounds that Eaton’s loyalty program harmed their competitor in 2014. Apparently Eaton was found guilty of improper pricing because they held 80% market share that totally dominated only a few competitors. In addition the pricing structure included rebates which encouraged their customers to continue to buy from Eaton and not from their competitor. The Eaton loyalty program required customers to use a high percentage of the Eaton transmissions, and to require customers to identify Eaton as their preferred transmission supplier while also encouraging their customers to price Eaton transmissions below those manufactured by its competitors.

Apparently the loyalty program itself is not illegal; however the fact that Eaton was a dominant provider in the market impacted the competitors that had much smaller market shares. It turns out that Eaton was not found to be selling their transmissions below cost but the program did harm Eaton’s competitors by locking in their (Easton’s) customers which effectively limited the market. The damage as a result of this antitrust lawsuit is expected to exceed more than $1 billion.

The key ingredient to defining a loyalty program as being illegal is that the company has a dominant market position and effectively controls the market. If your company is a giant in the marketplace with a significant market share, it is certainly susceptible to an antitrust lawsuit. Dr. Smith points out that there is no clear definition of what level of market share makes you a giant in the market. Basically, if the government can determine that your loyalty pricing program has done harm to a competitor, it may be vulnerable.

A similar lawsuit was brought against Intel in 2009. Intel’s smaller competitor AMD was paid $1.25 billion in damages. Intel’s loyalty program was based on requiring customers to use a high percentage of Intel chips in their products. Intel was not selling their chips below cost; however, since Intel held a dominant market share of 80% in the global microprocessor market with its considerable influence on the market, this was deemed sufficient to warrant the lawsuit and fine.

The bottom line is for companies that dominate the marketplace to be very careful and include a legal review of their loyalty program. For those who do not dominate there does not appear to be any legal constraints on your loyalty program or the how it is run unless the loyalty program would lead to selling your equipment or services below cost.  For example, if your loyalty program includes credit for a trade-in, the combined purchase price minus the trade-in may lead to a net of selling below cost which continues to be illegal in the United States.  Otherwise, the market is yours for the taking.

Words have value too!

A lot of time and energy is spent quantitatively analyzing the results of surveys. The survey may be based on metrics such as customer satisfaction, NPS, customer effort, or customer experience. This quantitative analysis may take on many dimensions of statistical methodology. However, the comments that are made associated with the individual scores are seldom included in the statistical quantitative analysis. There are several methodologies available for including the analysis of the comments (referred to as content analysis) with the numerical analysis.  It can be a worthwhile endeavor to compare the results of the quantitative and content analyses. 

A McKinsey study suggests that positive emotions correlate strongly with profits. The study indicated that after a positive customer experience more than 85% of the customers purchased more and after a negative experience more than 70% purchased less.

In this blog two different types of qualitative analyses are discussed; namely, descriptive measures of word usage and content analysis (which is also referred to as sentiment analysis).

Analysis of word usage includes grouping for individual words or word groups into categories. Words or word groups are usually grouped into three categories; namely, negative comments, positive comments or general comments. If the comment relates to a specific activity then those comments will be grouped by specific survey question(s). Within this grouping of word usage, descriptive measures provide a complementary presentation to the quantitative results. The descriptive measures are limited primarily to the number of comments associated with a specific question or questions on the survey.

The general hypothesis is that the descriptive measure of positive comments will be similar to that measured quantitatively. Obviously, the hypothesis is also extended to the negative remarks which should be reflected by the negative scores on the survey. This qualitative procedure does not interpret the intensity of any feelings implicit in the wording. In adition, the words will not pick up such subtleties as sarcasm or other emotions.

Content analysis (Sentiment analysis) is used to determine how customers feel with respect to a product or service. The primary purpose of sentiment analysis is to capture strong feelings that may be embedded in emotionally laden words. The two dimensions of sentiment analysis identify the feelings as being either positive or negative and the individual words or word phrases can be used to calibrate the magnitude of the sentiment.

Content analysis for product support is generally focused on the statistics associated with word usage (positive and negative). Typically the primary goal of content analysis is to validate the quantitative aspects of the survey metrics. Hence, the need for sentiment analysis is often limited. Only when word usage analysis indicates conflicting results with the quantitative analysis does sentiment analysis become a worthwhile addition to add to the perspective of the customers.

Too often surveys are conducted to understand the strengths and weaknesses of product support. The comments included with surveys are often reviewed individually but rarely analyzed in sufficient detail to provide verification of the quantitative measures taken.

The bottom line is that qualitative analysis is often overlooked when examining the relationship between customers and the products and services provided to them by the company. It may be time to reconsider what aspects of survey analysis should be included. It is likely that the best answer is both quantitative and qualitative analyses provide the greatest insight about the customers.

A note to remember is that feelings persist much longer than the score presented on the survey. An unkempt restroom at a restaurant can have a lasting effect on the customers that use it. It may be the only reason the customer never comes back. An emergency service provided beyond expectation may create a customer for life. Emotions are powerful.

Why bother with loyalty?

Some research suggests the customer loyalty may not be enough. A study was done by Timothy Keiningham, Lerzan Aksoy, Alexander Buoye, and Bruce Cooil that has some interesting findings. They performed a two-year longitudinal study of more than 17,000 consumers and examining purchases in more than a dozen industries in nine countries. According to the study, they asked a broad range of questions and purchase histories including satisfaction and loyalty measurements. Their analysis was based on the largest and most rigorous of its kind of survey at the time which indicated an interesting correlation; namely, the rank consumers assigned to a brand relative to other brands in the industry can be used as a predictor of “share of wallet.” They refer to the results of this outcome as the Wallet Allocation Rule (published by Wiley, 2015).  The details of survey methodology, survey questions, and statistical tools used in the analysis were not available.  This blog is based on their article published in the Harvard Business Review (which should be sufficient evidence that the methodology meets Harvard’s criteria for a valid survey).

Here are two of the findings that should be considered.

1.     Correlation between changes in satisfaction or intention to recommend and “share of wallet” was 0.1 – suggesting that satisfaction has little impact on changes in “share of wallet.”

2.     Correlation between changes in “share of wallet” score using their metric compared with a customer’s actual measured “share of wallet” was 0.8.

The Wallet Allocation Rule is proposed by the authors to be a better metric than customer satisfaction or NPS. In the survey they asked the consumers to assign a rank of the brand relative to other brands that the customers were using. The result was that ranking with respect to competitors was demonstrated as being more important than customer satisfaction when it comes to increasing “share of wallet.” The point they make is that the parameters which drive changes in “share of wallet” may not be the same parameters that drive the change in customer satisfaction.

This methodology is particularly useful from a sales perspective.  However, this methodology has limited usefulness when operating in a supportive environment with limited, if any, competition of other brands of products (such as occurs with product support).

The evidence from the survey provides a strong case that ranking in the marketplace has more value than customer satisfaction when compared with the competition.

A secondary consideration is the impact of ranking on loyalty. The implication of loyalty follows from focusing on the parameters that have the greatest impact on rank. If the consumers are increasingly drawn to a particular brand, it is not unreasonable to believe there will be a component of loyalty that will follow that move of higher rank with the brand.  The assumption that ranking has a greater long-term impact on loyalty that customer satisfaction needs further investigation. 

  

The bottom line is that “share of wallet” may be the best metric when focused on increasing ranking with respect to competition.  Although this study provides valid statistical support, its value is diminished when used as a metric for product support where “ranking” with competition has little meaning.

Whoever heard of bad loyalty?

Did you ever imagine that there could be an issue with customer loyalty? Could there be a time when customer loyalty may not be the best answer? When we think of customer loyalty we often think of it as a way to help a company survive difficult market conditions. We think of customer loyalty synonymously with the phrase customer retention. Many marketing personnel will support the notion that selling to a loyal customer is more cost-effective than creating a new customer.

One of my favorite phrases is that customers are what make paydays possible. So to think that customer loyalty might have a negative component is surprising. Tim Keiningham and Lerzan Aksoy wrote a note several years ago that discussed the negative component of customer loyalty.  This blog attempts to capture the essence of their note.

What we would like our loyal customers to be is customers who can clearly understand why it is important to support us during the good times and the bad. This clearly occurs most often when the customers can differentiate our company’s offerings of products and services from a competitor. They will know what the company stands for.

So, what kind of loyalty is bad loyalty? The authors have suggested that price driven loyalty is the lowest form of loyalty. They make the point that the company is not offering any differentiated value to customers. It also implies the price-driven loyalty does not bring levels of profitability that often occur when selling to loyal customers.  Many marketing studies confirm that the cost of selling to a loyal customer is usually less than the cost of acquiring a new customer. Hence a price-driven loyal customer is more likely to provide a lower margin/profit to the company.

The obvious conclusion is that price-driven loyalty is not really loyalty at all. Customer loyalty is created when the company can provide products and services that are differentiated from its competition. Thus the customer finds the value derived from this relationship better meet its needs.

The bottom line is that real customer loyalty is good loyalty only when the company provides consistent differentiation of its products and services from the competitors. The notion that price creates loyalty does not meet the standard for long-term differentiation. Yes, there is bad customer loyalty.

A win-win for Social Media

 Most companies in the US are using social media as an important channel for communicating with customers. One of the primary reasons for using the social media is to provide information regarding products and services. Several studies such as one done by Simply Measured indicates that only about one third of the companies that use social media provide a link for customer service. Other studies suggest there is an increasing trend to provide customer service through social media.

The purpose of this blog is to examine the relationship between social media and customer service. The conclusion suggests that the proper use of social media for customer service is a win for the customers and a win for the company. Hence, using the social media for customer service reduces the cost of customer service while simultaneously improves response time of customer service to customer requests.

The advantages for customer service using social media include the following:

1.                   1..     Companies do not need to have support personnel available 24/7. This will allow the company to maximize the use of support personnel and reduce idle time. This improvement in productivity should be sufficient to justify using the social media as a way to acquire customer concerns as well as accolades.

2.                2.         Companies can utilize self-help diagnostics to reduce the number of customer interactions.

3.                 3.       Companies can acquire data from the social media to improve customer satisfaction and customer loyalty.

The advantages for customers using social media to contact the company include the following:

1.       The customer can contact the company 24/7. Thus the customer is not inconvenienced by waiting until company personnel are available.

2.       The customer can resolve made his concerns through the use of self-help diagnostics.

3.       The customer can provide detail regarding the purpose of the call prior to the review by the company. This allows company respond more knowledgeably.

4.       The customers will frequently perceive the responsiveness to their concerns more positively than if they had to wait until someone is available.

Thus the customer and company each benefit by having a direct path through social media. As customers become increasingly aware of having customer service available through social media, other methods of communication will undoubtedly diminish. Companies need to examine how social media is being used by their customers. It is not unreasonable to expect that the trend for communication between the customer and the company in the social media will continue to increase.

The bottom line is that companies who provide customer service should be developing plans (if they have not already) to restructure the customer service strategy to include connection to social media. The signs are clear that customers will increasingly use the Internet to communicate with companies.

The companies who understand this trend and plan their customer service organization to address this change in the way we do business will have a distinct advantage over their competition.