I recently responded to a blog that was critiquing the NPS measurement. My point was that measurement accuracy is important. Some of the comments in the blog suggested that even though NPS might not be accurate, it is simple to compute and easy to present to "the boardroom." Thus, the measure of NPS should be used. I disagreed with this approach and made the point that we must strive for measurement accuracy. Anything less has two major problems; namely,
1. The measurement may provide misleading results that would encourage a company to invest in areas that may have little or no impact on the company performance. This is especially true with NPS since its major selling point is that its use will lead to improved financial performance.
2. Perhaps even more important than providing misleading information is the notion that those of us who are in the customer measurement business are responsible for the accuracy of the measurements we use. If there is any doubt about the validity of the measurements, it will reflect on us.
An interesting measurement occurred while I was employed by Xerox Corporation. At the time I was responsible for measuring service parameters for all copiers and service operations in the United States. The measurements were important since they were used to establish the field service budet (which at that time was large enough to rank the service business as a Fortune 500 company). We introduced a new desktop copier and projected the installation time to be about 1 hour. We thought we would check this out by tracking the first several thousand installations and compare the actual installation times with our estimate so that we could provide the best estimate for the field budget.
After several thousand installations we examined the data and found that the average install time was 1.00 hours with ZERO standard deviation. This is easily translated into the following statement "EVERY IINSTALLATION TOOK EXACTLY 1.00 HOURS." There is no way that this could be true! So, what did we learn about this measurement?
1. We learned that this measurement was not accurate since there was no chance of tha many copiers to take exactly 1 hour of installation time.
2. We learned that the field service organization had inside information that we had projected a 1 hour installation time.
3. The field organization believed that by giving us what we projected they were helping us.
4. We now had no idea of how long it took to install that copier and hence could not provide an install time for the field budget.
The good news is that this process was easily fixed by pointing out the impact of their actions and to my knowledge it has never happened again.
So, the moral of this episode at Xerox has several dimensions which can easily be extrapolated to the current situation with the NPS measurement.
1. Be careful what you measure.
2. Make sure the measurement makes sense.
3. Know who is providing the data,
4. Make sure you know the agenda of those providing the data.
I think it is necessary for the industry to make sure that the measurements we support are valid; otherwise, we will reap the consequences.
Tuesday, August 28, 2007
Friday, August 24, 2007
Customer Turnoffs - Another Perspective of Loyalty
I think Paul Timm, professor at Brigham Young University's Marriott School of Management and author of the book "Seven Power strategies for Building Customer Loyalty" has taken a different course in his research into customer loyalty than others who are researching the topic. He has focused his research for a number of years on surveying business customers and consumers by asking them "what turns you off as a customer?"
He has found that the responses boil down to a disconnect between service intentions and reality. The fact that customer expectations are not met is generally the result of the company failing to met their own performance levels that they established.
It is amazing that it takes only three categories of customer turnoffs to account for 97% of all responses.
The first turnoff: VALUE. The customers perceives that they are not getting what they paid for. This could include inadequate guarantees, inferior quality, and high prices relative to the perceived value of the product. (this is an excellent description of the product dimension of customer loyalty - as described in a previous blog)
The second turnoff: SYSTEMS PERFORMANCE. When systems do not meet customer expectations, customers experience a systems turnoff. This could look like transactions or processes that are unnecessarily complicated or inefficient. It could look like employees who lack the knowledge to answer customer questions. The number one system process problem noted by Timm is slow service. (the process dimension of customer loyalty also described previously).
The third turnoff: PEOPLE. Companies are composed of people and when those employees lack courtesy or attention, demonstrate inappropriate or unprofessional behavior or have an indifferent attitude, customers are definitely turned off. All these characteristics can be summed up by any behaviour that conveys a lack of care or consideration for the customer. (the relationship dimension of customer loyalty also described previously).
The bottom line is that Professor Timm's research is consistent with my previous blog that characterized the three real components of customer loyalty as product, process and relationship. The good news is that he discoved these factors by looking at customers from the negative side.
He has found that the responses boil down to a disconnect between service intentions and reality. The fact that customer expectations are not met is generally the result of the company failing to met their own performance levels that they established.
It is amazing that it takes only three categories of customer turnoffs to account for 97% of all responses.
The first turnoff: VALUE. The customers perceives that they are not getting what they paid for. This could include inadequate guarantees, inferior quality, and high prices relative to the perceived value of the product. (this is an excellent description of the product dimension of customer loyalty - as described in a previous blog)
The second turnoff: SYSTEMS PERFORMANCE. When systems do not meet customer expectations, customers experience a systems turnoff. This could look like transactions or processes that are unnecessarily complicated or inefficient. It could look like employees who lack the knowledge to answer customer questions. The number one system process problem noted by Timm is slow service. (the process dimension of customer loyalty also described previously).
The third turnoff: PEOPLE. Companies are composed of people and when those employees lack courtesy or attention, demonstrate inappropriate or unprofessional behavior or have an indifferent attitude, customers are definitely turned off. All these characteristics can be summed up by any behaviour that conveys a lack of care or consideration for the customer. (the relationship dimension of customer loyalty also described previously).
The bottom line is that Professor Timm's research is consistent with my previous blog that characterized the three real components of customer loyalty as product, process and relationship. The good news is that he discoved these factors by looking at customers from the negative side.
Tuesday, August 21, 2007
The Ugly Side of Customer Loyalty
Yes, Virginia, there is an ugly side to customer loyalty. Customer loyalty is often viewed as the ultimate goal and without blemish. After all, what could possibly be wrong with customer loyalty. Well, there is a dark side to customer loyalty and when that dark side appears it usually occurs through unintended or uncontemplated use of customer information.
The good news is that customer information has great value within an organization. With the development of analytic and statistical models companies can learn a lot about their customers. Companies can segment the customers into groups based on customer value or by market segment or any one of a number of other demographics. This is the beatiful side of customer loyalty. The knowledge gained from the customers helps the company fine tune its product and service offerings to better meet the customer needs and hence become more efficient with a corresponding improvement in profitability.
HOWEVER, there is a dark side and one that should be considered whenever there is customer information to be mined. One way to turn to the dark side is to create analytic models that describe how much pain a customer can take before they stop doing business with you. An example would be the current status of airline service today. An analysitic model indicates how much more an airline can take away in terms of on-board service before the customer says ENOUGH - I won't fly this airline anymore - they have cut too much. The airline model seems to be testing how much service can be eliminated before there is customer defection.
Another dimension of the dark side is when the customer data base is used to segment customers so that some customers or customer segments receive better treatment than others. Marketing departments like to segment customers and find segmentation one very effective way of improving company performance and better meeting the needs of the various customer segments. While this makes sense to the company, it also becomes apparent to the customers who are not receiving the better treatment because they are in the segment that receives less value (or pays higher prices). If they have little chance of moving up to become one of the customers in the "privileged" group and reap the benefits of being "privileged", the consequence might be customer defection. Those nifty analaytic models can often be inferred by a savvy customer base and those customers may choose to take their business elsewhere when the "hand writing on the wall" tells them they are not privileged and may never be.
The bottom line is customer data is extremely valuable and should be examined carefully before any use is made of the information. Unintended consequences can bring unintended surprises such as increased customer defections.
The good news is that customer information has great value within an organization. With the development of analytic and statistical models companies can learn a lot about their customers. Companies can segment the customers into groups based on customer value or by market segment or any one of a number of other demographics. This is the beatiful side of customer loyalty. The knowledge gained from the customers helps the company fine tune its product and service offerings to better meet the customer needs and hence become more efficient with a corresponding improvement in profitability.
HOWEVER, there is a dark side and one that should be considered whenever there is customer information to be mined. One way to turn to the dark side is to create analytic models that describe how much pain a customer can take before they stop doing business with you. An example would be the current status of airline service today. An analysitic model indicates how much more an airline can take away in terms of on-board service before the customer says ENOUGH - I won't fly this airline anymore - they have cut too much. The airline model seems to be testing how much service can be eliminated before there is customer defection.
Another dimension of the dark side is when the customer data base is used to segment customers so that some customers or customer segments receive better treatment than others. Marketing departments like to segment customers and find segmentation one very effective way of improving company performance and better meeting the needs of the various customer segments. While this makes sense to the company, it also becomes apparent to the customers who are not receiving the better treatment because they are in the segment that receives less value (or pays higher prices). If they have little chance of moving up to become one of the customers in the "privileged" group and reap the benefits of being "privileged", the consequence might be customer defection. Those nifty analaytic models can often be inferred by a savvy customer base and those customers may choose to take their business elsewhere when the "hand writing on the wall" tells them they are not privileged and may never be.
The bottom line is customer data is extremely valuable and should be examined carefully before any use is made of the information. Unintended consequences can bring unintended surprises such as increased customer defections.
Saturday, August 18, 2007
The Satisfaction Trap
I recently ran across an old Harvard Business School article by Frederick Reichheld ("Learning from Customer Defections", March-April 1996) that has some interesting thoughts that I often forget. He notes that satisfaction surveys have two principle problems. Some of the comments below are direct quotes from the article. I have tried to maintain the context of the quotes.
The first problem is that satisfaction scores become an end in themselves. I have seen this in many of my consulting assignments where the satisfaction score is one, if not the only, factor in computing salary increases and bonuses. This can also be seen by noting the emphasis on the J.D.Power survey results for just about everything - especially cars. The score is all that matters! This is a problem because in operating a business we can't manage scores, we can only manage people and processes. Another reason why this is a problem is that most surveys represent only a sample of customers and each sample has a different set of outcomes. In fact, sample statistics will absolutely change every time a sample is taken. When companies do not understand the notion of sampling error, chaos can occur within the management ranks when the sample scores fall (within the range of the sampling error). Management believes that customers are not as satisfied as they were in the previous period and hence it is time to agressively attack the problem. In fact, there is no problem, just sampling error. This can best be described as the "keystone cop approach to management" and is one management philosophy I do not support.
The second problem is that surveys of measure the wrong activity or the wrong customers. The fact is that designing a survey requires some significant training. Most companies believe that they can assign the survey to an employee with little or no training in survey design because "all you have to do is write down a couple of questions." When I am asked to review surveys and survey results from these companies, most of the time I find the survey flawed in one or more ways. The worst case is when a group designs a survey with a specific outcome in mind and constructs the survey to verify the outcome. No need to discuss this situation the any further. A point I almost always make to these companies is that even a poorly designed survey will provide a measurement and if that measurement is incorrect, it may lead to wrong conclusions and decisions.
Here are some of the excellent points that are made in the article:
1. Whenever rewards are based on satisfaction scores decoupled from repurchase loyalty and profits, the result is unproductive behavior.
2. Surveys almost never provide the information that managers need to pick the investments that will maximize customer value and, in turn, cash flow.
3. Surveys ignore critical distinctions among customer segments.
4. In business after business, 60% to 80% of lost customers reported on a survey just prior to defecting that they were satisfied or very satisfied.
This article was written about the same time Mr. Reichheld's book "The Loyalty Effect" was published and prior to his later work on NPS. I am not a fan of NPS but that is left for a future blog.
The first problem is that satisfaction scores become an end in themselves. I have seen this in many of my consulting assignments where the satisfaction score is one, if not the only, factor in computing salary increases and bonuses. This can also be seen by noting the emphasis on the J.D.Power survey results for just about everything - especially cars. The score is all that matters! This is a problem because in operating a business we can't manage scores, we can only manage people and processes. Another reason why this is a problem is that most surveys represent only a sample of customers and each sample has a different set of outcomes. In fact, sample statistics will absolutely change every time a sample is taken. When companies do not understand the notion of sampling error, chaos can occur within the management ranks when the sample scores fall (within the range of the sampling error). Management believes that customers are not as satisfied as they were in the previous period and hence it is time to agressively attack the problem. In fact, there is no problem, just sampling error. This can best be described as the "keystone cop approach to management" and is one management philosophy I do not support.
The second problem is that surveys of measure the wrong activity or the wrong customers. The fact is that designing a survey requires some significant training. Most companies believe that they can assign the survey to an employee with little or no training in survey design because "all you have to do is write down a couple of questions." When I am asked to review surveys and survey results from these companies, most of the time I find the survey flawed in one or more ways. The worst case is when a group designs a survey with a specific outcome in mind and constructs the survey to verify the outcome. No need to discuss this situation the any further. A point I almost always make to these companies is that even a poorly designed survey will provide a measurement and if that measurement is incorrect, it may lead to wrong conclusions and decisions.
Here are some of the excellent points that are made in the article:
1. Whenever rewards are based on satisfaction scores decoupled from repurchase loyalty and profits, the result is unproductive behavior.
2. Surveys almost never provide the information that managers need to pick the investments that will maximize customer value and, in turn, cash flow.
3. Surveys ignore critical distinctions among customer segments.
4. In business after business, 60% to 80% of lost customers reported on a survey just prior to defecting that they were satisfied or very satisfied.
This article was written about the same time Mr. Reichheld's book "The Loyalty Effect" was published and prior to his later work on NPS. I am not a fan of NPS but that is left for a future blog.
Saturday, August 11, 2007
A True Loyalty Story - a PVC Plant in Trouble
I worked in industry for about 20 years and during that time had some wonderful and exciting experiences with customers. This is one of the most memorable.
In the late 1970's I was the head of service for Taylor Instrument Company, a company located in Rochester, New york, that manufactured and sold process control systems and equipment. We had a complete process control system installed in a large PVC plant in Texas. This plant ran 24x7x365 and the plant manager (I still remeber his name) had a significant portion of his salary linked to plant output which meant he was ALWAYS very concerned about keeping his plant running. I had personally visited the plant and met the plant manager and was well aware of these circumstances.
A few facts are important. PVC is a plastic that is made in a liquid and then a catalyst is added. Once the catalyst is added there is only a short time before the PVC "sets" (hardens). If the PVC is in the plant piping, values, etc. when the time to set occurs, the plant must be re-piped at considerable expense. (At least this is the story I was told). In order to prevent this occurence, the plant used a dual computer system with a shared memory so that if there was a computer failure, the other computer would continue to control the operation. Just to make things a little more complicated, I was told that PVC in its liquid state is carcenogenic. All the more reason to make sure nothing happens.
Now, the story. I was at home in Rochester when I receive a call from the plant manager at 11:00pm on a Saturday evening. I knew this was not a social call! He explained that his plant had been shut down since 2:00pm that afternoon. His technician had thought he solved the problem but when the system was turned on the parts that had been replaced also failed. Now they called our technician who lives in Houston to come and help. The technician from Houston, a well-qualified technician, came and after several hours of diagnosis installed his spare parts into the system and turned it on, only to have the system fail again. This was the situation described to me. The plant manager then said something that really started my heart beating at an elevated pace. He had rented a private jet to come to Rochester to pick up three parts which they were sure would solve the problem. The jet had left Texas and should be arriving in about two hours. YIKES!
Two very important points are that even though he gave me the part numbers, I had no idea what they were. Secondly, our plant in Rochester was only open Monday through Friday and this was Saturday night. Soooooo, after I had a short but severe panic attack, I started tracking down people on my staff that would know the parts and how to check them out to be sure they were functional. (The last thing I wanted to have happen was to send parts that were DOA - "dead-on-arrival") The next step was to get my staff authorization to get through security and into the plant in the middle of the night. I found some of them at parties and others at a local bowling alley where they were in a late Saturday night league. I finally got a cadre of people to go to the plant. Since the service organization did not have inventory, I instructed them to rob from the systems in manufacturing and check out 2 of each parts that were ordered. I did not get into trouble once the circumstances were known.
The good news was that we were successful. They met the plane and sent 2 of each part number to the plant in Texas. To say that the plant manager was a happy man when I called him on Monday, is an understatement. He became a VERY loyal customer. That Saturday episode yielded a loyal customer AND the following financial benefits to Taylor:
1. He paid for the overtime o the people to get the parts to the plane and the list price for the parts.
2. He and I agreed they should keep more parts at his site. So we sold him $100,000 in additional parts at list price (70% gross margin).
3. We never had to negotiate the annual service contract.
4. He became an advocate for Taylor and was partially responsible for selling several more of those systems to other companies (usually for about $2,000,000+).
While there are several lessons that one can take away from this story; the greatest lesson that I learned was that the more critical the need, the greater the impression on the customer (good or bad). Of course, another lesson I learned was the value to have in place a system whereby I would NEVER be surprised by a customer call. The regional service manager in Texas also learned this lesson.
In the late 1970's I was the head of service for Taylor Instrument Company, a company located in Rochester, New york, that manufactured and sold process control systems and equipment. We had a complete process control system installed in a large PVC plant in Texas. This plant ran 24x7x365 and the plant manager (I still remeber his name) had a significant portion of his salary linked to plant output which meant he was ALWAYS very concerned about keeping his plant running. I had personally visited the plant and met the plant manager and was well aware of these circumstances.
A few facts are important. PVC is a plastic that is made in a liquid and then a catalyst is added. Once the catalyst is added there is only a short time before the PVC "sets" (hardens). If the PVC is in the plant piping, values, etc. when the time to set occurs, the plant must be re-piped at considerable expense. (At least this is the story I was told). In order to prevent this occurence, the plant used a dual computer system with a shared memory so that if there was a computer failure, the other computer would continue to control the operation. Just to make things a little more complicated, I was told that PVC in its liquid state is carcenogenic. All the more reason to make sure nothing happens.
Now, the story. I was at home in Rochester when I receive a call from the plant manager at 11:00pm on a Saturday evening. I knew this was not a social call! He explained that his plant had been shut down since 2:00pm that afternoon. His technician had thought he solved the problem but when the system was turned on the parts that had been replaced also failed. Now they called our technician who lives in Houston to come and help. The technician from Houston, a well-qualified technician, came and after several hours of diagnosis installed his spare parts into the system and turned it on, only to have the system fail again. This was the situation described to me. The plant manager then said something that really started my heart beating at an elevated pace. He had rented a private jet to come to Rochester to pick up three parts which they were sure would solve the problem. The jet had left Texas and should be arriving in about two hours. YIKES!
Two very important points are that even though he gave me the part numbers, I had no idea what they were. Secondly, our plant in Rochester was only open Monday through Friday and this was Saturday night. Soooooo, after I had a short but severe panic attack, I started tracking down people on my staff that would know the parts and how to check them out to be sure they were functional. (The last thing I wanted to have happen was to send parts that were DOA - "dead-on-arrival") The next step was to get my staff authorization to get through security and into the plant in the middle of the night. I found some of them at parties and others at a local bowling alley where they were in a late Saturday night league. I finally got a cadre of people to go to the plant. Since the service organization did not have inventory, I instructed them to rob from the systems in manufacturing and check out 2 of each parts that were ordered. I did not get into trouble once the circumstances were known.
The good news was that we were successful. They met the plane and sent 2 of each part number to the plant in Texas. To say that the plant manager was a happy man when I called him on Monday, is an understatement. He became a VERY loyal customer. That Saturday episode yielded a loyal customer AND the following financial benefits to Taylor:
1. He paid for the overtime o the people to get the parts to the plane and the list price for the parts.
2. He and I agreed they should keep more parts at his site. So we sold him $100,000 in additional parts at list price (70% gross margin).
3. We never had to negotiate the annual service contract.
4. He became an advocate for Taylor and was partially responsible for selling several more of those systems to other companies (usually for about $2,000,000+).
While there are several lessons that one can take away from this story; the greatest lesson that I learned was that the more critical the need, the greater the impression on the customer (good or bad). Of course, another lesson I learned was the value to have in place a system whereby I would NEVER be surprised by a customer call. The regional service manager in Texas also learned this lesson.
Thursday, August 9, 2007
Service Innovation Impact on Process Loyalty
this past March executives from IBM and Oracle founded the Service Research & Innovation (SRI) initiative. Since then this Silicon Valley non-profit has grown and now includes representatitves from leading tech companies including HP, Microsoft and Cisco. Arizona State, the Univeristy of Maryland and the European Commission (the executive body of the European Union) have also joined.
I think some of the reasons for this initiative are:
1. The 10 industries with the most dramatic salary growth in the U.S. are all in the service-providing sectors.
2. The employment in the manufacturing sector is expected to drop 5% by 2014.
3. Goods producing industries will decline to 13% of total American employment in the decade 2004-2014 which is down from 15% in 1994-2004 decade.
4. Service workers now outnumber farmers for the first time.
5. A service-innovation research firm, Peer Insight, noted that the 10 companies they analyzed saw 65% of 2005 revenues from service and yielded 85% of the profits.
A significant point is that consumers now see services as added value to products. This has brought about a remarkable change in business models from the past when many businesses thought that a good product was all they needed to keep their customers happy. The new model tracks the four dimension customer loyalty model I have noted in previous blogs that includes products, services and customer relationships in addition to rewards (which I believe is not a true component of loyalty).
I think some of the reasons for this initiative are:
1. The 10 industries with the most dramatic salary growth in the U.S. are all in the service-providing sectors.
2. The employment in the manufacturing sector is expected to drop 5% by 2014.
3. Goods producing industries will decline to 13% of total American employment in the decade 2004-2014 which is down from 15% in 1994-2004 decade.
4. Service workers now outnumber farmers for the first time.
5. A service-innovation research firm, Peer Insight, noted that the 10 companies they analyzed saw 65% of 2005 revenues from service and yielded 85% of the profits.
A significant point is that consumers now see services as added value to products. This has brought about a remarkable change in business models from the past when many businesses thought that a good product was all they needed to keep their customers happy. The new model tracks the four dimension customer loyalty model I have noted in previous blogs that includes products, services and customer relationships in addition to rewards (which I believe is not a true component of loyalty).
Tuesday, August 7, 2007
Process versus Relationship
Experian Integrated Marketing surveyed 1,000 consumers in the UK about their help desk/call center experience. I think their findings are consistent with what we might expect in the US. One summary comment from the research indicates that brands are making it too complex for customers to deal with them BECAUSE OF TECHNOLOGY. The following statistics seem to support their summary comment:
1. 78% of the respondents believe brands place too much emphasis on technology at the expense of customer service.
2. 70% of the respondents find the technology inflexible.
3. 67% of the respondents admit to having suffered telephone or Web customer service problems in the past year; yet, 89% still instinctively reach for the phone.
4. Only 7% of the respondents believe that technology delivers what they expect as part of good customer service.
5. 59% of the respondents believe technology should be able to synchronise all customer information across various parts of the business (this technology is available now).
6. 60% of the respondents want a seemless process of communication (this technology is also available now).
7. 37% of the respondents believe that technology will not answer their needs for at least five years.
One other interesting point is that the telephone is still the predominant mode for contact that was noted in this survey. It is used about 6 times as often as online or email. This ratio of phone use to online and email contact may not be consistent within the US market.
One conclusion I can make after reviewing these results is that customers want the personal touch more than they want technology. However, another conclusion is that customers see that technology may ultimately help them.
1. 78% of the respondents believe brands place too much emphasis on technology at the expense of customer service.
2. 70% of the respondents find the technology inflexible.
3. 67% of the respondents admit to having suffered telephone or Web customer service problems in the past year; yet, 89% still instinctively reach for the phone.
4. Only 7% of the respondents believe that technology delivers what they expect as part of good customer service.
5. 59% of the respondents believe technology should be able to synchronise all customer information across various parts of the business (this technology is available now).
6. 60% of the respondents want a seemless process of communication (this technology is also available now).
7. 37% of the respondents believe that technology will not answer their needs for at least five years.
One other interesting point is that the telephone is still the predominant mode for contact that was noted in this survey. It is used about 6 times as often as online or email. This ratio of phone use to online and email contact may not be consistent within the US market.
One conclusion I can make after reviewing these results is that customers want the personal touch more than they want technology. However, another conclusion is that customers see that technology may ultimately help them.
Monday, August 6, 2007
Customer Relationship really matters
A recent survey conducted by Harris Interactive for Verint Systems seems to indicate that customer service really matters. Consider some of the results of the survey:
1. 88% reported they find a company with good customer service much more enticing than one with the new and innovative products; that leaves 12% who prefer a company with innovative products.
2. 90% said that, on average, they tell at least one other person about a bad customer experience and 85% said they tell more than one person.
3. 88% said that, on average, they tell at least one person about a good customer experience and 81% tell more than one person.
This continues to demonstrate the old adage that bad news travels either faster or farther than good news. However, it seems that good news is starting to catch up with bad news in terms of speed of travel.
The bottom line of this survey is that it strongly supports the notion that customer relationships are a critical component of customer loyalty. These statistics also suggest that the relationship dimension of customer loyalty may be stronger than either the product or process dimension.
1. 88% reported they find a company with good customer service much more enticing than one with the new and innovative products; that leaves 12% who prefer a company with innovative products.
2. 90% said that, on average, they tell at least one other person about a bad customer experience and 85% said they tell more than one person.
3. 88% said that, on average, they tell at least one person about a good customer experience and 81% tell more than one person.
This continues to demonstrate the old adage that bad news travels either faster or farther than good news. However, it seems that good news is starting to catch up with bad news in terms of speed of travel.
The bottom line of this survey is that it strongly supports the notion that customer relationships are a critical component of customer loyalty. These statistics also suggest that the relationship dimension of customer loyalty may be stronger than either the product or process dimension.
Friday, August 3, 2007
Loyalty - NOT
There is an article dated July 30th by Stuart Evans of ICLP, a marketing agency in the UK, that suggests that the word "loyalty" is a misnomer. Stuart makes the point by using some statistics from frequent flyer programs (ffp). He notes some recent research by his company and another firm, Priority Pass, indicated the most important factors among airline travellers are 'price' (62%), 'schedule' (54%), 'ffp' (21%), and 'brand loyalty' (13%). Although the percentages don't add up, they do indicate some ranking that does make sense. He makes the point that companies should move away from the mindset of loyalty and shift towards factors that reward customers purchasing behavior.
However, he has some counter data that certainly clouds the issue he has proposed. He points out that research (not specified when or by whom) indicates that 'ffp' is a key factor in determining choice of hotel or airline. A whopping 70% believe it is fairly or extremely important in deciding their choice while only 30% said it was unimportant.
Stuart believes that many loyalty programs have failed to radically change customer behavior over time or deliver bottom line contribution to profit.
I love this guy! He is shouting what I have been saying in my recent blogs that loyalty in a single dimension is a problem. This is consistent with my previous definition that loyalty has four dimensions and that reward loyalty is probably one of the weakest because it is not based on company performance - only a reward. The problem with using a reward to create customer loyalty is that it is just a reward and can be exceeded by another company at any time; hence, the loyalty may persist only as long as there is no better reward in the market. The other dimensions of loyalty are product loyalty, process loyalty and relationship loyalty (previously described in recent blogs).
Stuart also makes the following statement: "an emotional attachment to a brand only via its loyalty program is illogical and does not happen." In this case he seems to be identifying loyalty only with reward programs. I would strongly agree with his statement as long as he continues to define loyalty only in terms of reward programs.
The bottom line for me was that once again when someone defines loyalty simplistically, it doesn't make sense. This is particularly true when the single dimension is a rewards program. Customer loyalty should (and I believe it does) come from company performance not a give-away.
However, he has some counter data that certainly clouds the issue he has proposed. He points out that research (not specified when or by whom) indicates that 'ffp' is a key factor in determining choice of hotel or airline. A whopping 70% believe it is fairly or extremely important in deciding their choice while only 30% said it was unimportant.
Stuart believes that many loyalty programs have failed to radically change customer behavior over time or deliver bottom line contribution to profit.
I love this guy! He is shouting what I have been saying in my recent blogs that loyalty in a single dimension is a problem. This is consistent with my previous definition that loyalty has four dimensions and that reward loyalty is probably one of the weakest because it is not based on company performance - only a reward. The problem with using a reward to create customer loyalty is that it is just a reward and can be exceeded by another company at any time; hence, the loyalty may persist only as long as there is no better reward in the market. The other dimensions of loyalty are product loyalty, process loyalty and relationship loyalty (previously described in recent blogs).
Stuart also makes the following statement: "an emotional attachment to a brand only via its loyalty program is illogical and does not happen." In this case he seems to be identifying loyalty only with reward programs. I would strongly agree with his statement as long as he continues to define loyalty only in terms of reward programs.
The bottom line for me was that once again when someone defines loyalty simplistically, it doesn't make sense. This is particularly true when the single dimension is a rewards program. Customer loyalty should (and I believe it does) come from company performance not a give-away.
Thursday, August 2, 2007
Another Loyalty Model
Another loyalty model was developed at Harvard Business School. It is referred to as the Apostle Model and uses both customer satisfaction and loyalty to define a 2x2 matrix with customer satisfaction plotted on the horizontal axis and customer loyalty on the vertical axis. The model suggests dividing up the matrix such that the four quadrants are named Loyalists, Hostages, Mercenaries and Defectors. Each of these groups is defined as follows:
1. Loyalists - have high levels of customer satisfaction and loyalty
2. Hostages - have high levels of customer loyalty and low levels of satisfaction
3. Mercenaries - have high levels of customer satisfaction and low low levels of loyalty
4. Defectors - have low levels of customer satisfaction and loyalty.
I think each of these groups are relatively easy to understand.
The logic of the model makes sense and I certainly understand that high loyalty and high customer satisfaction would seem to identify a loyalist (one who is truly loyal).
Jonathan Barsky and Lenny Nash are principles of Market Matrix LLC and applied this model to the hospitality industry. They plotted results from 140,000 customer surveys completed in 2006 and found the model provides a robust method of understanding and grouping customers. They found Ritz-Carlton and Four Seasons to be the only two hotels to fall into the Loyalists quadrant. Days Inn and Red Roof Inns were found to fall into the Defector quadrant and the authors suggest the hotel chains in this quadrant may not have evolved as quickly as customer expectations have changed. They conclude that the model provides advance warning of customer dissatisfaction. I think this might be a leap since they were only measuring satisfaction with no apparent measures of dissatisfaction. I have found that satisfaction and dissatisfaction are not opposite of one another. Satisfiers do not necessarily become dissatisfiers when the satisfaction level drops nor do dissatisfiers necessarily become satisfiers for similar reasons. More about this another time.
These principles at Market Metrix certainly have an excellent data set. I wonder how they decide just where to draw the lines which segments the matrix into customer loyalty into low and high and the same with customer satisfaction. I remember a study that was done about 10 years ago and published in an industry trade magazine where a market research company showed plotted data points of customer satisfaction against loyalty and drew a straight line to show the relationship was linear. When I fit a regression line to the data I found an r-square for the line of less than 50%. So much for believing the relationship was linear - at least for that set of data. I believe that as customer satisfaction increases, loyalty tends to increase also. I'll stay there for now.
1. Loyalists - have high levels of customer satisfaction and loyalty
2. Hostages - have high levels of customer loyalty and low levels of satisfaction
3. Mercenaries - have high levels of customer satisfaction and low low levels of loyalty
4. Defectors - have low levels of customer satisfaction and loyalty.
I think each of these groups are relatively easy to understand.
The logic of the model makes sense and I certainly understand that high loyalty and high customer satisfaction would seem to identify a loyalist (one who is truly loyal).
Jonathan Barsky and Lenny Nash are principles of Market Matrix LLC and applied this model to the hospitality industry. They plotted results from 140,000 customer surveys completed in 2006 and found the model provides a robust method of understanding and grouping customers. They found Ritz-Carlton and Four Seasons to be the only two hotels to fall into the Loyalists quadrant. Days Inn and Red Roof Inns were found to fall into the Defector quadrant and the authors suggest the hotel chains in this quadrant may not have evolved as quickly as customer expectations have changed. They conclude that the model provides advance warning of customer dissatisfaction. I think this might be a leap since they were only measuring satisfaction with no apparent measures of dissatisfaction. I have found that satisfaction and dissatisfaction are not opposite of one another. Satisfiers do not necessarily become dissatisfiers when the satisfaction level drops nor do dissatisfiers necessarily become satisfiers for similar reasons. More about this another time.
These principles at Market Metrix certainly have an excellent data set. I wonder how they decide just where to draw the lines which segments the matrix into customer loyalty into low and high and the same with customer satisfaction. I remember a study that was done about 10 years ago and published in an industry trade magazine where a market research company showed plotted data points of customer satisfaction against loyalty and drew a straight line to show the relationship was linear. When I fit a regression line to the data I found an r-square for the line of less than 50%. So much for believing the relationship was linear - at least for that set of data. I believe that as customer satisfaction increases, loyalty tends to increase also. I'll stay there for now.
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