A DHL Express survey of 1200 British adults give some interesting insights into the value of customer service and some of the dissatisfiers. Some of the results of the survey include:
1. 35% of the Brits think that products and services have the most impact on their purchasing decisions. However, 27% think that customer service has the most impact.
2. Women are more concerned than men about customer service 29% for women and 24% for men.
3. The top dissatisfier for customer service among the Brits is waiting times (83%). The second was language barriers (80%). The third was lack of knowledge (74%).
4. The average Brit prefers face-to-face communication (39%). A close second is computer-based communication (35%). Face-to-face communication is more popular with men than women (43% for men versus 36% for women).
5. Age makes a difference. Consumers between the ages of 16 and 24 were more tolerant of poor customer service. Only 44% would seek an alternative provider. Consumers over the age of 55 would seek an alternative provider 64% of the time after receiving poor customer service.
6. 86% of the Brits say that poor customer service affects their decision to make future purchases from a company and 95% say that poor customer service affects their perception of the business as a whole. These percentages are dramatically different that the statistic in item #1 above and appear to be in congruent with the previous statistic. Since respondents are often not congruent in their thinking, this may not be a survey problem.
Since I would expect DHL Express to perform a valid survey, I am concluding that the results are truly indicative of the Brits perspective of customer service. The bottom line is that these findings give another perspective of the impact of customer service on loyalty to a business. Having spoken to several Brits at a conference in London in October, these results track their anecdotal remarks.
Saturday, November 29, 2008
Friday, November 28, 2008
The Real Impact of Customer Complaints
John Goodman, past president of TARP and currently president of e-satisfy has given some factual data behind some of the statistics that are bandied around in the customer service business. I was glad to see his statistics and understand where they came from. While this data is relatively old, it contains data that I see in the blogs on a regular basis. (This might suggest that many people are satisfied with using old data and just using the phrase "research has shown." The information shown below was published by John Goodman in Competitive advantage, June, 1999.
1. 50% of consumers will complain to a front line person and in the B2B environment the number is 75%.
2. Only 1 to 5% will escalate the complaint to a local manager or corporate HQ. For large ticket items 50% go to the front line and 5 to 10% escalate to local management or corporate HQ. Interestingly, if the company has an 800 number the percentages double. The most important statistic is that only 1 out of 100 to 500 complaints will ever be seen by a senior executive.
3. If the complaint results in out-of-pocket loss the complaint rate is 50 to 75%. Otherwise, the complaint rate for mistreatment, quality and incompetence evoke only a 5 to 30% complaint rates.
4. Tom Peters used to suggest that for every bad experience 10 people will be told and for every good experience only 2 people will be told. From this he noted that bad news travels faster than good news. John Goodman's data suggests that the ratio is really 2 to 1. The original study for Coca-Cola in 1981 showed that a median of 5persons heard about a good experience and 10 heard about a bad experience. A later study for Coca-Cola found that resolution of a problem on the first contact improved loyalty 10% higher than resolution via multiple contacts. A similar study for a domestic auto manufacturer found the numbers to be 8 and 16 respectively.
5. The general rule of thumb I have heard for many years is that it costs five times as much to acquire a new customer as it does to retain an existing customer. Once again, John Goodman gives us the history. They studied new auto customers and found that there was a cost of $375 in advertising for each auto sold. Since the company had a 50% base of loyal customers, the actual advertising cost to acquire a new customer was actually $750 per auto sold. The goodwill expense to retain a customer was $150. Hence the ratio of cost to acquire a new customer versus the cost to retain a customer was 5 to 1.
6. One of the final statistics is one that seems to vary from person to person. According to a study first published in 1988, customers who complain and are satisfied are 8% more loyal than if they had no problem at all. From my studies, I have found similar statistics.
7. I have heard the statistic that 68% of all people who defect leave because of a poor employee attitude. The statistic that John Goodman offers, that is based on a study by TARP suggests that 20% are caused by employee actions, 40% by corporate products and processes and 40% are caused by customer mistakes or incorrect expectations.
The bottom line is that some of the more often quoted customer complaint statistics seem to be constant and do not change with time. At least now we know where some of these "rule of thumb" statistics were derived. The statistics noted above were derived from studies while John Goodman was president of TARP.
1. 50% of consumers will complain to a front line person and in the B2B environment the number is 75%.
2. Only 1 to 5% will escalate the complaint to a local manager or corporate HQ. For large ticket items 50% go to the front line and 5 to 10% escalate to local management or corporate HQ. Interestingly, if the company has an 800 number the percentages double. The most important statistic is that only 1 out of 100 to 500 complaints will ever be seen by a senior executive.
3. If the complaint results in out-of-pocket loss the complaint rate is 50 to 75%. Otherwise, the complaint rate for mistreatment, quality and incompetence evoke only a 5 to 30% complaint rates.
4. Tom Peters used to suggest that for every bad experience 10 people will be told and for every good experience only 2 people will be told. From this he noted that bad news travels faster than good news. John Goodman's data suggests that the ratio is really 2 to 1. The original study for Coca-Cola in 1981 showed that a median of 5persons heard about a good experience and 10 heard about a bad experience. A later study for Coca-Cola found that resolution of a problem on the first contact improved loyalty 10% higher than resolution via multiple contacts. A similar study for a domestic auto manufacturer found the numbers to be 8 and 16 respectively.
5. The general rule of thumb I have heard for many years is that it costs five times as much to acquire a new customer as it does to retain an existing customer. Once again, John Goodman gives us the history. They studied new auto customers and found that there was a cost of $375 in advertising for each auto sold. Since the company had a 50% base of loyal customers, the actual advertising cost to acquire a new customer was actually $750 per auto sold. The goodwill expense to retain a customer was $150. Hence the ratio of cost to acquire a new customer versus the cost to retain a customer was 5 to 1.
6. One of the final statistics is one that seems to vary from person to person. According to a study first published in 1988, customers who complain and are satisfied are 8% more loyal than if they had no problem at all. From my studies, I have found similar statistics.
7. I have heard the statistic that 68% of all people who defect leave because of a poor employee attitude. The statistic that John Goodman offers, that is based on a study by TARP suggests that 20% are caused by employee actions, 40% by corporate products and processes and 40% are caused by customer mistakes or incorrect expectations.
The bottom line is that some of the more often quoted customer complaint statistics seem to be constant and do not change with time. At least now we know where some of these "rule of thumb" statistics were derived. The statistics noted above were derived from studies while John Goodman was president of TARP.
Wednesday, November 26, 2008
Loyalty Accounting
I am not an accountant, but I read an article that raised a red flag at me. The article was addressing what are the accounting principles that can be applied to loyalty programs. I found the article in "Customer World" and it references an article by Rajiv Goyal in the DNA Money magazine in India.
I have chosen to address this topic for the following reasons:
1. I have seen very little written about this topic
2. Understanding the way accountants deal with loyalty programs may be a real issue in measuring their performance (success or failure).
3. I would like to be able to factor in the accounting impact into the loyalty math models.
4. It is about time that accountants start thinking about customers and become a part of the customer-centric company
The problem as I understand it concerns how the value of loyalty points can be accounted for before they are redeemed. When a company (such as an airline, credit card company, or even a clothing store) offers customers loyalty points, there is no easy way of knowing when the points will be redeemed. At the time when the points are awarded, the company takes on a liability for the value of those points. However, the time at which the points will be redeemed is not known so that there is no way of allocating the funds to a specific accounting period. Thus, the accountant can become frustrated because there is no way that the books can be reconciled without doing something with those "ugly" loyalty points.
As the number of loyalty points increases, the value of the accumulated loyalty points increases (as the airlines are seeing) and the financial impact can become staggering.
Of course, all this time the company, and especially the marketing department that created the loyalty points program, is basking in the increased business that has accrued from the program. The sales and marketing people are looking forward to a big bonus check for the great job they have done in recapturing some old customers and increasing the sales from existing customers. And well they should - they did just what management wanted them to do.
There are two points to consider; namely,
1. not all the points will be redeemed, and
2. the value given for each point was computed before the program was initiated so that the profit margin on the products will still be sufficient to meet company profit goals even if all points are redeemed.
The issues that still remain for the accountant are:
1. Since the loyalty points act like a discount on sales, there is no way to predict how much of a discount to use since not all points will be redeemed.
2. There is no way to determine what provision should be made for the liabilities.
3. There is also no way to determine the time period in which the discounts will occur.
4. There are no specific guidelines in the accounting standards in India (and probably anywhere else) on how to handle these discounts.
In general, the accountants do not see customers in the same way that sales and marketing see them. The article points out the need for customer-centric by accountants. I agree.
The bottom line is we need to get all areas of the firm to be customer-centric and that includes accounting if we are to be able to truly focus on the customer.
I have chosen to address this topic for the following reasons:
1. I have seen very little written about this topic
2. Understanding the way accountants deal with loyalty programs may be a real issue in measuring their performance (success or failure).
3. I would like to be able to factor in the accounting impact into the loyalty math models.
4. It is about time that accountants start thinking about customers and become a part of the customer-centric company
The problem as I understand it concerns how the value of loyalty points can be accounted for before they are redeemed. When a company (such as an airline, credit card company, or even a clothing store) offers customers loyalty points, there is no easy way of knowing when the points will be redeemed. At the time when the points are awarded, the company takes on a liability for the value of those points. However, the time at which the points will be redeemed is not known so that there is no way of allocating the funds to a specific accounting period. Thus, the accountant can become frustrated because there is no way that the books can be reconciled without doing something with those "ugly" loyalty points.
As the number of loyalty points increases, the value of the accumulated loyalty points increases (as the airlines are seeing) and the financial impact can become staggering.
Of course, all this time the company, and especially the marketing department that created the loyalty points program, is basking in the increased business that has accrued from the program. The sales and marketing people are looking forward to a big bonus check for the great job they have done in recapturing some old customers and increasing the sales from existing customers. And well they should - they did just what management wanted them to do.
There are two points to consider; namely,
1. not all the points will be redeemed, and
2. the value given for each point was computed before the program was initiated so that the profit margin on the products will still be sufficient to meet company profit goals even if all points are redeemed.
The issues that still remain for the accountant are:
1. Since the loyalty points act like a discount on sales, there is no way to predict how much of a discount to use since not all points will be redeemed.
2. There is no way to determine what provision should be made for the liabilities.
3. There is also no way to determine the time period in which the discounts will occur.
4. There are no specific guidelines in the accounting standards in India (and probably anywhere else) on how to handle these discounts.
In general, the accountants do not see customers in the same way that sales and marketing see them. The article points out the need for customer-centric by accountants. I agree.
The bottom line is we need to get all areas of the firm to be customer-centric and that includes accounting if we are to be able to truly focus on the customer.
Saturday, November 22, 2008
The Problem with Assumptions
I just finished reading an article by Michael Lieberman in the November Issue of Quirk's Marketing Research Review. The article is titled Measuring and maximizing the ROI of a loyalty program. Needless to say, it caught my attention.
First let me say that Mr. Lieberman has presented an excellent case for showing how an ROI can be computed for a given loyalty program. He uses a technique referred to as Monte Carlo Simulation. This technique uses a series of random trials (events) to see how a system that is not deterministic will respond by going through the process a large number of times. By using some statistical distributions to represent the different ways elements of the system (customers) could act, the output provides a picture of those many trials. This technique has been used for a long time and has been well tested and accepted.
There are two very important aspects to performing a Monte Carlo simulation. The first is to understand the assumptions used and second is to compare the simulation results to actual results. Unfortunately, the magazine did not give Mr. Leiberman the space to fully demonstrate the power of Monte Carlo simulation and answer these two aspects.
The first aspect which was inadequately discussed was a clear and complete discussion of the assumptions in his model. The second aspect was to compare the simulation results to the real world that he was simulating. There was no there, there. The results of his simulation were not compared to actual data.
Some of the assumptions that should have been explained in the article might include:
1. the relationship between the dollar value of the purchases and the incentives given. Is the relationship linear, non-linear or ???
2. the variation of margins for different product mixes. Was this considered a constant?
3. the percentage of loyalty points that will be redeemed (if the data is categorized then the assumptions should be noted by group unless it is further assumed that the groups are all the same).
4. the belief that a customer's spending behavior will change because their spending has changed.
The second aspect is that results must ALWAYS be compared with the real world. Yes, computers can produce a lot of output that seems to represent the real world, but until it is tested and verified in the real world, it is just computer output. The fact is that the computer output may be 100 percent accurate or 10 percent accurate, but you will never know it unless it is checked against the actual system in place.
For example, when I worked at Xerox, we were investigating the idea of going to team service. One of the variables we needed to understand was the statistical distribution of the time to complete a service call. The assumption was that the service completion time was statistically distributed according to the negative exponential distribution. Before we ran our simulation model we built an analytical queuing model to get an idea what the optimum team size should be. We could not easily include meetings, lunches and parts chasing into our model so the only way to get an accurate assessment of the optimum team size was to build the simulation model to account for the vagaries that were not easily modeled analytically. As it turned out the service completion time distribution was not a simple negative distribution, it was a significantly different variant of the negative exponential distribution and one that made a difference in our results. While it did not change the optimum team size, it did impact the value of the team.
The point here is that you must carefully describe the model and ALL its assumptions so that you are not misled by the computer results. A friend of mine once said that GIGO does not stand for "Garbage - In Garbage Out." Rather it stands for "Garbage In - Gospel Out."
The bottom line is that Monte Carlo Simulations provide an excellent tool for pre-testing business policies. I believe that Michael Lieberman did do the work and managed the assumptions. Unfortunately, his article doesn't reflect that. Sometimes an editorial pen can kill you.
First let me say that Mr. Lieberman has presented an excellent case for showing how an ROI can be computed for a given loyalty program. He uses a technique referred to as Monte Carlo Simulation. This technique uses a series of random trials (events) to see how a system that is not deterministic will respond by going through the process a large number of times. By using some statistical distributions to represent the different ways elements of the system (customers) could act, the output provides a picture of those many trials. This technique has been used for a long time and has been well tested and accepted.
There are two very important aspects to performing a Monte Carlo simulation. The first is to understand the assumptions used and second is to compare the simulation results to actual results. Unfortunately, the magazine did not give Mr. Leiberman the space to fully demonstrate the power of Monte Carlo simulation and answer these two aspects.
The first aspect which was inadequately discussed was a clear and complete discussion of the assumptions in his model. The second aspect was to compare the simulation results to the real world that he was simulating. There was no there, there. The results of his simulation were not compared to actual data.
Some of the assumptions that should have been explained in the article might include:
1. the relationship between the dollar value of the purchases and the incentives given. Is the relationship linear, non-linear or ???
2. the variation of margins for different product mixes. Was this considered a constant?
3. the percentage of loyalty points that will be redeemed (if the data is categorized then the assumptions should be noted by group unless it is further assumed that the groups are all the same).
4. the belief that a customer's spending behavior will change because their spending has changed.
The second aspect is that results must ALWAYS be compared with the real world. Yes, computers can produce a lot of output that seems to represent the real world, but until it is tested and verified in the real world, it is just computer output. The fact is that the computer output may be 100 percent accurate or 10 percent accurate, but you will never know it unless it is checked against the actual system in place.
For example, when I worked at Xerox, we were investigating the idea of going to team service. One of the variables we needed to understand was the statistical distribution of the time to complete a service call. The assumption was that the service completion time was statistically distributed according to the negative exponential distribution. Before we ran our simulation model we built an analytical queuing model to get an idea what the optimum team size should be. We could not easily include meetings, lunches and parts chasing into our model so the only way to get an accurate assessment of the optimum team size was to build the simulation model to account for the vagaries that were not easily modeled analytically. As it turned out the service completion time distribution was not a simple negative distribution, it was a significantly different variant of the negative exponential distribution and one that made a difference in our results. While it did not change the optimum team size, it did impact the value of the team.
The point here is that you must carefully describe the model and ALL its assumptions so that you are not misled by the computer results. A friend of mine once said that GIGO does not stand for "Garbage - In Garbage Out." Rather it stands for "Garbage In - Gospel Out."
The bottom line is that Monte Carlo Simulations provide an excellent tool for pre-testing business policies. I believe that Michael Lieberman did do the work and managed the assumptions. Unfortunately, his article doesn't reflect that. Sometimes an editorial pen can kill you.
Wednesday, November 19, 2008
One Positive Aspect of Using a Survey
Professors Paul Dholakia and Vicki Morwitz completed a research study that tested the impact of a customer survey impact on loyalty. Their research study has been published in the Harvard Business Review. The objective of their study was to determine whether or not the process of performing a survey causes significant changes in the future behavior of those receiving the survey.
They worked with a financial institution and selected customers with high levels of satisfaction and created two groups. The first group participated in a customer survey of customer satisfaction. The second group (control group) did not participate in any research. The behaviors and profitability of each group was tracked for one year following the survey.
The observed differences included:
1. Survey participants were THREE times as likely to open new accounts with the firm.
2. Survey participants were LESS THAN HALF as likely to defect.
3. Survey participants had a profitability profile that was significantly better than the non participation group.
4. Survey participants continued to open hew accounts at a faster rate and to defect at a slower rate than the nonparticipants even after a year.
Professors Dholakia and Morwitz believe the effects of survey participation will last anywhere from THREE to EIGHT years.
The most important aspect of this research is that surveys themselves have a positive effect on customers. The effects may, in fact, cause sustained changes in customer behavior. The evidence appears to demonstrate that the differences are quantifiable with a measurable impact on the financial performance of the firm.
One important research question is why this is happening. There are many hypotheses that might explain the customer behavior. Let us hope that the work of Professors Dholakia and Morwitz will continue their research that appears to have such a significant impact on survey work.
One caveat to this research is that it was performed on only one company in only one industry. It will be interesting to see if these results can be replicated in other companies in other industries.
They worked with a financial institution and selected customers with high levels of satisfaction and created two groups. The first group participated in a customer survey of customer satisfaction. The second group (control group) did not participate in any research. The behaviors and profitability of each group was tracked for one year following the survey.
The observed differences included:
1. Survey participants were THREE times as likely to open new accounts with the firm.
2. Survey participants were LESS THAN HALF as likely to defect.
3. Survey participants had a profitability profile that was significantly better than the non participation group.
4. Survey participants continued to open hew accounts at a faster rate and to defect at a slower rate than the nonparticipants even after a year.
Professors Dholakia and Morwitz believe the effects of survey participation will last anywhere from THREE to EIGHT years.
The most important aspect of this research is that surveys themselves have a positive effect on customers. The effects may, in fact, cause sustained changes in customer behavior. The evidence appears to demonstrate that the differences are quantifiable with a measurable impact on the financial performance of the firm.
One important research question is why this is happening. There are many hypotheses that might explain the customer behavior. Let us hope that the work of Professors Dholakia and Morwitz will continue their research that appears to have such a significant impact on survey work.
One caveat to this research is that it was performed on only one company in only one industry. It will be interesting to see if these results can be replicated in other companies in other industries.
The Government and Good Customer Service
I think I have found a real oximoron. The surprising news is that public satisfaction with government web sites is increasing. Holy Cow!!!
According to the ACSI, one of the most reputable survey services in the US notes that the aggregate score of 73.9 out of 100 points increased from 72.9 in the previous quarter. This latest score is among the highest since the index was launched in 2003 and nearly matching the high of 74 points that occurred in 2006.
According to Larry Freed, President and CEO of Foresee Research, which published the report 27 percent of the government sites surveyed scored 80 or above on the index. If some can achieve scores of 80 or more, that means that there are still some government web sites that are not serving the public very well.
Some of the other AMAZING and positive facts noted in the report are:
1. GSA's main web site is up 9 points from last year and 22 points from when it was first measured.
2. National Center for Drug Abuse made significant gains.
3. national Resource Conservation Service also made significant gains.
While the private sector still out-performs the government, the government appears to be working to improve their site satisfaction.
Let's hope the government continues to improve its web sites. We the people deserve web sites that work and are easy to use. It's about time the government figured this out!
According to the ACSI, one of the most reputable survey services in the US notes that the aggregate score of 73.9 out of 100 points increased from 72.9 in the previous quarter. This latest score is among the highest since the index was launched in 2003 and nearly matching the high of 74 points that occurred in 2006.
According to Larry Freed, President and CEO of Foresee Research, which published the report 27 percent of the government sites surveyed scored 80 or above on the index. If some can achieve scores of 80 or more, that means that there are still some government web sites that are not serving the public very well.
Some of the other AMAZING and positive facts noted in the report are:
1. GSA's main web site is up 9 points from last year and 22 points from when it was first measured.
2. National Center for Drug Abuse made significant gains.
3. national Resource Conservation Service also made significant gains.
While the private sector still out-performs the government, the government appears to be working to improve their site satisfaction.
Let's hope the government continues to improve its web sites. We the people deserve web sites that work and are easy to use. It's about time the government figured this out!
Monday, November 17, 2008
Loyalty Metrics That Make Sense
There are probably as many loyalty metrics as there are consultants. At one extreme some say you only need one while the other extreme says you need a PhD in statistics to understand all the interactions that a customer has with your organization. My personal leanings are toward the middle but nearer the low end.
I was recently reading the work of Jeanne Bliss, the author of "Chief Customer Officer" and thought she had a pretty good idea of the kind of metrics that makes sense and yet is reasonably comprehensible. She devotes a chapter to these metrics in her book and refers to the metrics as Guerrilla Metrics. I think she calls them Guerrilla Metrics because it will take Guerrilla tactics to get them implemented in your organization. I think she may be over-stating the problem, but she appears to be speaking from experience.
Guerrilla metrics consist of five measures (reasonable and comprehensive in my mind). The following descriptions of the metrics reasonably describe them in simple language.
1. Measure the number of new customers in a specific reporting period (weekly, monthly or quarterly). The idea is not to just count them but also categorize them at the same time. By that I mean classify them according to the types of products and services they are buying and then assess them in terms of value (lifetime or some other measure that differentiates the high value customer from the low value customer).
2. Measure the number of customers you lose in the same reporting period. This is one of the most overlooked aspects of customer loyalty. Many companies have no idea when they lose a customer. Unless you know why you are losing customers, you may continue to lose them. Remember the sage quote "if you don't know your history you are bound to repeat it."
3. Repeat customers or customers who renew on-going contracts. Just as it is important to know why customers leave, it is also important to know why they come back.
4. Measure the revenue and profitability of each of the categories of customers identified previously. There are two reasons for this measure; namely, (i) to rank order each group in terms of profitability and (ii) to see if there is migration from one group to another - preferably from a lower profit and revenue group to a higher one - but just as important to note when migration starts to move from higher revenue and profit groups to lower ones.
5. Measure the number of referrals from existing customers. This is similar to the NPS measure except it requires that you track the rate of referrals overall but more specially by customer group. If your company is not getting referrals that may be a sign of a customer disconnect somewhere along the customer corridor.
These five measures are all great measures in my opinion. However, they are also very difficult measures to accurately assess. The bottom line is that we know that there is a strong relationship between customer-centric organizations and ROI. Hence, we can conclude that it probably takes a comprehensive customer metric system requiring multiple measures to examine all aspects of customers (coming ,going, staying and referring new customers). For example, using NPS one would only be measuring the fifth component of the five Guerrilla measures.
With these measures one could easily start building a business model that would show the net customer perspective each reporting period (new + existing + newly referred customers - lost customers). This could be done by each business segment to show both quantity of customers and value. I would be interested to know if there are any companies that use this type of customer metric system. Drop me a comment if you know of one or, better yet, if you are one.
I was recently reading the work of Jeanne Bliss, the author of "Chief Customer Officer" and thought she had a pretty good idea of the kind of metrics that makes sense and yet is reasonably comprehensible. She devotes a chapter to these metrics in her book and refers to the metrics as Guerrilla Metrics. I think she calls them Guerrilla Metrics because it will take Guerrilla tactics to get them implemented in your organization. I think she may be over-stating the problem, but she appears to be speaking from experience.
Guerrilla metrics consist of five measures (reasonable and comprehensive in my mind). The following descriptions of the metrics reasonably describe them in simple language.
1. Measure the number of new customers in a specific reporting period (weekly, monthly or quarterly). The idea is not to just count them but also categorize them at the same time. By that I mean classify them according to the types of products and services they are buying and then assess them in terms of value (lifetime or some other measure that differentiates the high value customer from the low value customer).
2. Measure the number of customers you lose in the same reporting period. This is one of the most overlooked aspects of customer loyalty. Many companies have no idea when they lose a customer. Unless you know why you are losing customers, you may continue to lose them. Remember the sage quote "if you don't know your history you are bound to repeat it."
3. Repeat customers or customers who renew on-going contracts. Just as it is important to know why customers leave, it is also important to know why they come back.
4. Measure the revenue and profitability of each of the categories of customers identified previously. There are two reasons for this measure; namely, (i) to rank order each group in terms of profitability and (ii) to see if there is migration from one group to another - preferably from a lower profit and revenue group to a higher one - but just as important to note when migration starts to move from higher revenue and profit groups to lower ones.
5. Measure the number of referrals from existing customers. This is similar to the NPS measure except it requires that you track the rate of referrals overall but more specially by customer group. If your company is not getting referrals that may be a sign of a customer disconnect somewhere along the customer corridor.
These five measures are all great measures in my opinion. However, they are also very difficult measures to accurately assess. The bottom line is that we know that there is a strong relationship between customer-centric organizations and ROI. Hence, we can conclude that it probably takes a comprehensive customer metric system requiring multiple measures to examine all aspects of customers (coming ,going, staying and referring new customers). For example, using NPS one would only be measuring the fifth component of the five Guerrilla measures.
With these measures one could easily start building a business model that would show the net customer perspective each reporting period (new + existing + newly referred customers - lost customers). This could be done by each business segment to show both quantity of customers and value. I would be interested to know if there are any companies that use this type of customer metric system. Drop me a comment if you know of one or, better yet, if you are one.
Friday, November 14, 2008
Impact of Dissatisfiers
I have written extensively about the very significant difference between satisfiers and dissatisfiers. I have made the point that customers will tolerate a temporary decline in performance of a company process or product that is a satisfier (such as the temperature of a hamburger at McDonalds that is not really warm), and continue to do business with the company. However, when a customer experiences a decline in a dissatisfier, the impact can be immediate loss of that customer (such as a finding a dirty restroom at the McDonalds).
This difference has been validated by a survey of 500 Australian adults by StollzNow Research. On the positive side they found that 28 percent of consumers will remain loyal to companies that provide them with the best service. But consider the impact of the dissatisfiers.
1. 79 percent of consumers have stopped doing business with an organization because of a bad experience (you can bet it had to do with a dissatisfier).
2. 71 percent of consumers tell others about their negative experience with the aim of preventing other consumers from doing business with the company at fault.
The primary reason I am using this wonderful example of the impact of dissatisfiers is to point out that most surveys do not have the ability to sort out the satisfiers from the dissatisfiers. Companies can recover from a decline in performance of a product or service which is known to be a satisfier but will begin to lose customers immediately when a product or service that is a dissatisfier declines.
The bottom line is that companies must be aware of what the dissatisfiers are. They must be ever vigilant to manage the dissatisfiers because of the immediacy of the impact on their business.
This difference has been validated by a survey of 500 Australian adults by StollzNow Research. On the positive side they found that 28 percent of consumers will remain loyal to companies that provide them with the best service. But consider the impact of the dissatisfiers.
1. 79 percent of consumers have stopped doing business with an organization because of a bad experience (you can bet it had to do with a dissatisfier).
2. 71 percent of consumers tell others about their negative experience with the aim of preventing other consumers from doing business with the company at fault.
The primary reason I am using this wonderful example of the impact of dissatisfiers is to point out that most surveys do not have the ability to sort out the satisfiers from the dissatisfiers. Companies can recover from a decline in performance of a product or service which is known to be a satisfier but will begin to lose customers immediately when a product or service that is a dissatisfier declines.
The bottom line is that companies must be aware of what the dissatisfiers are. They must be ever vigilant to manage the dissatisfiers because of the immediacy of the impact on their business.
Thursday, November 13, 2008
Satisfiers and Loyalty for Rental Cars
JD Power just published the 2008 Rental Car Satisfaction Study. The study was based on more than 13,400 evaluations from business and leisure travelers who rented a vehicle at an airport location between September 2007 and October 2008.
The study showed the top three rental car companies were Enterprise, Hertz and Alamo in that order. The scoring was based on 6 general factors:
1. costs and fees
2. pick-up process
3. rental car
4. return process
5. reservation process
6. shuttle van/bus.
Note that these are general variables and should/must have some additional detail measures behind them to make them actionable. The report indicates there are some additional factors that have a particularly strong impact on satisfaction; namely,
1. adequate prep of the vehicle
2. ensure customers receive the vehicle model they reserve
3. minimizing wait times for vehicle pick up and drop off.
Recall that in previous my previous blogs I have identified the three components of loyalty as product, process and people. Other than the cost/fees component the other five factors contain a combination of process and people. In order to translate the factors into actionable items each of those five factors must be broken into its component parts that relate to the service process and the people in the service process. For example, the satisfaction of having a short wait time for pick up can be dramatically reduce by a driver who has dirty clothes and a bad attitude. Of course the other side of the coin is that satisfaction resulting from a long wait can be dramatically improved with a very courteous driver with a can-do attitude. The point is that all three components of loyalty must be included in order to maximize the impact on customer loyalty.
Another point to be made from examining these factors is that there is no discussion of which factors are satisfies and which are dissatisfiers. The difference is critical. Recall that satisfiers have room to move with little impact on overall satisfaction whereas dissatisfiers can cause customers to leave immediately with a simultaneous major negative impact on overall satisfaction.
Some further statistics from the study suggests the impact of an imbalance of the three loyalty components. When the data is parsed into groups the following statistics are observed:
1. When customers say they experience a problem, overall satisfaction declines more than 10% compared to customers that had no problems.
2. If the problem is not resolved, overall satisfaction drops approximately an additional 5%.
The final set of statistics relate to the typical loyalty questions; (i) likely to use the car company again, and (ii) likely to recommend. Below are the statistics that relate these two measures to the measure of overall satisfaction:
1. For customers who are highly committed, 86 percent said they will definitely use the care company again and 88 percent said they would definitely recommend the company to others.
2. For customers with only a medium commitment, 21 percent said they would definitely use the car company again and 20 percent would definitely recommend the car company to others.
The bottom line is that there is an obvious impact of satisfaction on loyalty when you examine this data. One interesting point is that customers with a high level of commitment are about 4 times more likely to return and recommend. The economic impact of this statistic would warrant careful consideration by all rental car companies to focus on satisfaction. There is one additional point to be made from the data. Namely, there apparently are about 20% of the customers with only a medium commitment who act like apostles. Maybe some of these customers with only medium commitment are part-time apostles.
The study showed the top three rental car companies were Enterprise, Hertz and Alamo in that order. The scoring was based on 6 general factors:
1. costs and fees
2. pick-up process
3. rental car
4. return process
5. reservation process
6. shuttle van/bus.
Note that these are general variables and should/must have some additional detail measures behind them to make them actionable. The report indicates there are some additional factors that have a particularly strong impact on satisfaction; namely,
1. adequate prep of the vehicle
2. ensure customers receive the vehicle model they reserve
3. minimizing wait times for vehicle pick up and drop off.
Recall that in previous my previous blogs I have identified the three components of loyalty as product, process and people. Other than the cost/fees component the other five factors contain a combination of process and people. In order to translate the factors into actionable items each of those five factors must be broken into its component parts that relate to the service process and the people in the service process. For example, the satisfaction of having a short wait time for pick up can be dramatically reduce by a driver who has dirty clothes and a bad attitude. Of course the other side of the coin is that satisfaction resulting from a long wait can be dramatically improved with a very courteous driver with a can-do attitude. The point is that all three components of loyalty must be included in order to maximize the impact on customer loyalty.
Another point to be made from examining these factors is that there is no discussion of which factors are satisfies and which are dissatisfiers. The difference is critical. Recall that satisfiers have room to move with little impact on overall satisfaction whereas dissatisfiers can cause customers to leave immediately with a simultaneous major negative impact on overall satisfaction.
Some further statistics from the study suggests the impact of an imbalance of the three loyalty components. When the data is parsed into groups the following statistics are observed:
1. When customers say they experience a problem, overall satisfaction declines more than 10% compared to customers that had no problems.
2. If the problem is not resolved, overall satisfaction drops approximately an additional 5%.
The final set of statistics relate to the typical loyalty questions; (i) likely to use the car company again, and (ii) likely to recommend. Below are the statistics that relate these two measures to the measure of overall satisfaction:
1. For customers who are highly committed, 86 percent said they will definitely use the care company again and 88 percent said they would definitely recommend the company to others.
2. For customers with only a medium commitment, 21 percent said they would definitely use the car company again and 20 percent would definitely recommend the car company to others.
The bottom line is that there is an obvious impact of satisfaction on loyalty when you examine this data. One interesting point is that customers with a high level of commitment are about 4 times more likely to return and recommend. The economic impact of this statistic would warrant careful consideration by all rental car companies to focus on satisfaction. There is one additional point to be made from the data. Namely, there apparently are about 20% of the customers with only a medium commitment who act like apostles. Maybe some of these customers with only medium commitment are part-time apostles.
Monday, November 10, 2008
Cultural Impact on Customer Loyalty
As I have noted in previous blogs, I have a large and growing data base of customer satisfaction data from information technology services. One of the areas I have studied is help desks (often referred to as technical support). I have been finding that there is a definite cultural component to high technology service. This may also be true for other areas but I can only speak to the service support for high technology equipment.
The largest difference I found in a recent study of help desks was between the United States and the Benelux countries. The sample sizes were large and met the criteria of a probability sample. For the United States the sample was 90,437 and for the Benelux countries the sample was 6600. Clearly, the sample sizes are sufficient to draw valid statistical conclusions. The really good news was the sample had wonderful characteristics; namely,
1. All data taken by the same survey organization.
2. All equipment had relatively the same complexity.
3. There were at least 10 companies in the data and all have very similar SLAs (Service Level Agreements).
4. All scales were the same (1 to 5 with 5 being highest and 1 being lowest).
To give an indication of the difference between customer satisfaction for help desk the United States and the Benelux countries, the 61.4 percent of the customers in the United States sample scored help desk satisfaction 5 whereas 28 percent of the Benelux countries sample scored help desk satisfaction 5.
The question is why would United States customers score a 5 for help desk service more then twice as often as the Benelux customers. Remember, the survey was taken for high technology companies that provide world-wide service at about the same service level.
This becomes even more interesting when I compared the percent of customers who scored 1 for help desk satisfaction. The United States had 3.3 percent and Benelux had 3.0 percent of their customers who decided that help desk satisfaction was very unsatisfactory.
The percent difference for scores of 5 are DRAMATICALLY HIGHER for the United States than Benelux but the difference for scores of 1 are not nearly as dramatic.
The difference is definitely at the high end of the scale. I am sure there are some excellent sociologists that cold give a plausible explanation. My first hypothesis about why this difference exists is that the attitude of the personnel on the help desk is different. I have not yet tested this but here is my reasoning:
1. It is easier to get rid of an under-performing technician in the United States than in the Benelux countries; therefore, the US tech has a greater downside for not performing well than the Benelux tech.
2. The customers in the Benelux countries may have a higher standard for service than the US customers.
3. Workload levels may be different between the United States and Benelux countries.
4. The training levels may be different and more comprehensive in the United States than Benelux countries.
5. The fact that the help desks in the Benelux countries has to deal with multiple languages could have an effect.
The bottom line is that customer satisfaction and the implied customer loyalty appears to have a very strong component. The next step is to isolate each of the contributing factors and assess which ones can be controlled and which ones are going to be there no matter what.
The largest difference I found in a recent study of help desks was between the United States and the Benelux countries. The sample sizes were large and met the criteria of a probability sample. For the United States the sample was 90,437 and for the Benelux countries the sample was 6600. Clearly, the sample sizes are sufficient to draw valid statistical conclusions. The really good news was the sample had wonderful characteristics; namely,
1. All data taken by the same survey organization.
2. All equipment had relatively the same complexity.
3. There were at least 10 companies in the data and all have very similar SLAs (Service Level Agreements).
4. All scales were the same (1 to 5 with 5 being highest and 1 being lowest).
To give an indication of the difference between customer satisfaction for help desk the United States and the Benelux countries, the 61.4 percent of the customers in the United States sample scored help desk satisfaction 5 whereas 28 percent of the Benelux countries sample scored help desk satisfaction 5.
The question is why would United States customers score a 5 for help desk service more then twice as often as the Benelux customers. Remember, the survey was taken for high technology companies that provide world-wide service at about the same service level.
This becomes even more interesting when I compared the percent of customers who scored 1 for help desk satisfaction. The United States had 3.3 percent and Benelux had 3.0 percent of their customers who decided that help desk satisfaction was very unsatisfactory.
The percent difference for scores of 5 are DRAMATICALLY HIGHER for the United States than Benelux but the difference for scores of 1 are not nearly as dramatic.
The difference is definitely at the high end of the scale. I am sure there are some excellent sociologists that cold give a plausible explanation. My first hypothesis about why this difference exists is that the attitude of the personnel on the help desk is different. I have not yet tested this but here is my reasoning:
1. It is easier to get rid of an under-performing technician in the United States than in the Benelux countries; therefore, the US tech has a greater downside for not performing well than the Benelux tech.
2. The customers in the Benelux countries may have a higher standard for service than the US customers.
3. Workload levels may be different between the United States and Benelux countries.
4. The training levels may be different and more comprehensive in the United States than Benelux countries.
5. The fact that the help desks in the Benelux countries has to deal with multiple languages could have an effect.
The bottom line is that customer satisfaction and the implied customer loyalty appears to have a very strong component. The next step is to isolate each of the contributing factors and assess which ones can be controlled and which ones are going to be there no matter what.
Wednesday, November 5, 2008
Proactive versus Reactive Customer Management
There was an interesting survey performed by Shape the Future, a market research company in the UK. While there was no information on the sample size of their survey, the results are interesting. The major statistic is that 70.3 percent of the companies are measuring customer satisfaction, which implies that 29.7 percent are not. They note that most of the companies that say they are measuring customer satisfaction are employing simple and informal tactics, such as relying on unsolicited customer feedback. The exact breakdown of the 29.7 percent not measuring customer satisfaction offer the following reasons:
1. Believe that customer will tell them if there is a problem.
2. Never thought about measuring customer satisfaction.
3. Are too busy to measure customer satisfaction
4. Plan to measure customer satisfaction in the future.
The first thing that comes to mind is that there is no understanding of the difference between a "good" customer and a "nice" customer. They must believe that all their customers are good customers. If I haven't noted this difference before, a good customer is one who will tell you when something is not done satisfactorily. A nice customer doesn't want to take the time to tell you or is not willing to confront you with the problem. The nice customer just goes away without saying anything. Of course we all want good customers but, unfortunately, not all customers are good customers.
One of the comments by Shape the Future, which is in agreement with my comment above, is that the results are saying too many businesses assume that people will give them useful feedback. Nope! There are too many "nice" customers in the market. They do point out that unhappy customers often leave without telling them why.
The point I want to make in this blog is that companies seem to think they know their customers when, in fact, most of them are out-of-touch. The study several years ago by Bain & Company surveyed 362 companies in the US and found that only 8% of their customers described their experience as "superior," yet 80% of the companies surveyed believe the experience they have been providing is indeed "superior." Many firms have CRM (Customer Relationship Management) programs but only know the customers after there is a record of a customer interaction. Thus, these CRM programs are lagging programs since they only look at the touch points after an interaction.
The bottom line is that customer experience with the company should be viewed from a leading perspective. Rather than waiting for an interaction, companies should be looking at the customer's experiences to determine where the gaps are between what the customer perceives and what the customer experiences. This approach will lead to a better understanding of how to increase loyalty and share of pocket by proactively filling the product and service gaps.
1. Believe that customer will tell them if there is a problem.
2. Never thought about measuring customer satisfaction.
3. Are too busy to measure customer satisfaction
4. Plan to measure customer satisfaction in the future.
The first thing that comes to mind is that there is no understanding of the difference between a "good" customer and a "nice" customer. They must believe that all their customers are good customers. If I haven't noted this difference before, a good customer is one who will tell you when something is not done satisfactorily. A nice customer doesn't want to take the time to tell you or is not willing to confront you with the problem. The nice customer just goes away without saying anything. Of course we all want good customers but, unfortunately, not all customers are good customers.
One of the comments by Shape the Future, which is in agreement with my comment above, is that the results are saying too many businesses assume that people will give them useful feedback. Nope! There are too many "nice" customers in the market. They do point out that unhappy customers often leave without telling them why.
The point I want to make in this blog is that companies seem to think they know their customers when, in fact, most of them are out-of-touch. The study several years ago by Bain & Company surveyed 362 companies in the US and found that only 8% of their customers described their experience as "superior," yet 80% of the companies surveyed believe the experience they have been providing is indeed "superior." Many firms have CRM (Customer Relationship Management) programs but only know the customers after there is a record of a customer interaction. Thus, these CRM programs are lagging programs since they only look at the touch points after an interaction.
The bottom line is that customer experience with the company should be viewed from a leading perspective. Rather than waiting for an interaction, companies should be looking at the customer's experiences to determine where the gaps are between what the customer perceives and what the customer experiences. This approach will lead to a better understanding of how to increase loyalty and share of pocket by proactively filling the product and service gaps.
Monday, November 3, 2008
The Black Swan for Loyalty
I have included the book by Nassim Taleb (The Black Swan) on my book list. The Wall Street Journal today (November 3rd) had an article about Black Swans and Nassim Talib in particular (Section C - Money and Investing, page C1). It turns out the Nassim Taleb is a professor of mathematical finance at NYU and has a hedge fund that is based on black swans. If you haven't already read this book, I highly recommend it. The subtitle of the book is "the impact of the highly improbable." To save a little time, a black swan event "has three principle characteristics; namely, it is unpredictable, it carries massive impact; and after the fact we concoct an explanation that makes it appear less random, and more predictable that it was." Nassim Taleb believes that black swans underlie almost everything about our world, from the rise of religions to events in our personal lives. It is this concept of the highly improbable that led him to describe them as "black swans" since black swans were once thought to be an impossible animal until they found one.
I don't want to summarize the book, my intention is to point out that black swans also occur in business and with customers. In fact, the whole intention of this blog is to make the case that a black swan event with a customer is one that seals customer loyalty and create the kind of "apostle" that is mentioned in other articles about loyalty.
A black swan event is one step beyond a "WOW" event. I described a black swan event on this blog August 11, 2007. Another example of a black swan event for me that created customer loyalty was with a customer who ran a food processing plant in Mississippi. The plant ran two shifts each day from 8am until midnight. It was important the the food was properly cooked to prevent salmonella or some other bacteria from getting into the food. I was head of service for Taylor Instrument company and received a call from the plant manger who suggested I resign from Taylor because I was overcharging the customers. He had a bill with 8 hours of overtime plas 1 hour of regular time. He pointed out that the sign-in sheets for his plant could only verify the 1 hour of regular time.
He wanted me to know that he intended to call the president of Taylor and recommend that he fire me. I suggested that he wait for 24 hours and that I would get back to him with an explanation for the charges. To make a long story short, our local technician had traveled all night to the next closest technician to get a critical part that was needed for the repair just so that the food processing plant would not be shutdown the next day while awaiting the replacement part.
When I explained the reason for the charge I had an instant apostle. He was amazed that someone who did not work for him would spend the entire night traveling 400 miles round trip to make sure his plant was operational when the 8am shift came in the next day.
The bottom line is that a highly improbable event (such as a technician driving all night just to make sure that a plant was not shutdown) creates an apostle. No company can expect to have black swans occur on a regular basis - that would negate the black swan inference. However, companies must be prepared to deliver a black swan event when the opportunity occurs (and they do occur). The question I would ask any company that has a desire to build customer loyalty is "are you prepared to respond to a black swan event." Then I would ask "will you know a black swan event if you see it."
I don't want to summarize the book, my intention is to point out that black swans also occur in business and with customers. In fact, the whole intention of this blog is to make the case that a black swan event with a customer is one that seals customer loyalty and create the kind of "apostle" that is mentioned in other articles about loyalty.
A black swan event is one step beyond a "WOW" event. I described a black swan event on this blog August 11, 2007. Another example of a black swan event for me that created customer loyalty was with a customer who ran a food processing plant in Mississippi. The plant ran two shifts each day from 8am until midnight. It was important the the food was properly cooked to prevent salmonella or some other bacteria from getting into the food. I was head of service for Taylor Instrument company and received a call from the plant manger who suggested I resign from Taylor because I was overcharging the customers. He had a bill with 8 hours of overtime plas 1 hour of regular time. He pointed out that the sign-in sheets for his plant could only verify the 1 hour of regular time.
He wanted me to know that he intended to call the president of Taylor and recommend that he fire me. I suggested that he wait for 24 hours and that I would get back to him with an explanation for the charges. To make a long story short, our local technician had traveled all night to the next closest technician to get a critical part that was needed for the repair just so that the food processing plant would not be shutdown the next day while awaiting the replacement part.
When I explained the reason for the charge I had an instant apostle. He was amazed that someone who did not work for him would spend the entire night traveling 400 miles round trip to make sure his plant was operational when the 8am shift came in the next day.
The bottom line is that a highly improbable event (such as a technician driving all night just to make sure that a plant was not shutdown) creates an apostle. No company can expect to have black swans occur on a regular basis - that would negate the black swan inference. However, companies must be prepared to deliver a black swan event when the opportunity occurs (and they do occur). The question I would ask any company that has a desire to build customer loyalty is "are you prepared to respond to a black swan event." Then I would ask "will you know a black swan event if you see it."
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