Disloyalty

RightNow Technologies has recently sponsored a survey that was performed by Harris Interactive and which provides some interesting statistics. The source I used did not discuss the methodology of the survey but did note they sampled 2,000 adults. Some of the more interesting findings are:
1. 80% of the respondents vowed never to buy from the same company after a negative experience. This value is up from 68% that was noted in a 2006 survey.
2. About 28% said they cursed as they wrangled with the customer reps during their phone call.
3. About 19% admitted shouting at the customer rep.
4. Harris Interactive provided some further detail by area of the country.
4a. Northeastern customers are unlikely to get emotional - they just take their wallet to another company.
4b. Midwestern customers are most likely to swear (34%).
4c. Southern customers are least likely to swear but 12% fantasized about picketing or defacing the company's headquarters.
4d. Western customers are more likely that the average customer to turn to the web and vent on a blog. 83% said they would never do business again with the offending company.

These statistics are not unreasonable. The customer today is much more sophisticated and more demanding. It is showing up more and more in the research. The companies that maintain a strong customer focus will outperform financially those companies that do not have a customer focus. I demonstrated this effect that improved financial performance coincides with a customer focus with a colleague in a recent article "Customer Focus: One key to Financial Success."

These statistics also support my notion that dissatisfiers are much more important to manage than satisfiers. Customer surveys should focus on the dissatisfiers rather than the "feel good" satisfiers. Managing dissatisfiers is where the action is.

Customer Loyalty Metrics by Accenture

I just read the latest issue of CRM magazine, September, 2007 and read a comment by Woody Driggs who is the global managing partner for the CRM serivce line at Accenture. If I read his column correctly, Accenture has a loyalty metric that has the following components:

1. Involvement with the Product or Service Category - this metric looks into how interested customers are with products and services. Apparently Accenture looks into the degree of involvement by customers with either the product or service or both.
2. Commitment to the Brand - this metric looks at the level of passion that customers have about the brands they buy. They are measuring the degree to which customers are willing to pay a premium for a brand. They also want to know if the customer is an advocate for a brand.
3. Likelihood to reevaluate - this metric measures how prone customers are to reevaluate their buying choices. Apparently they are trying to find the points at which customers are willing to switch.

I have simplified the definitions provided by Mr. Driggs and hope that in my simplification I have not distorted their meanings. If so, I apologize. These three components generally follow the three components of my loyalty model which are product, service and relationship. The component that they include that is not included in my model is brand loyalty.

This brings up an interesting dimension that needs to be evaluated further. The question I must answer is whether or not brand loyalty is just a composite of the three components in my loyalty model or is it really a new variable (component of loyalty). The question that comes to my mind is whether or not you can have brand loyalty without having product, service or relationship loyalty or is this a summary variable that is some function of some of them or all three. Is the brand loyalty variable an independent variable or does it have a high correlation to the three in my model and hence not really a separate variable?

Accenture believes this model will increase a company's ability to improve segmentation and improve their market focus and help them retain the most profitable customers. My previous blog demonstrates that a company with a customer focus will out perform those companies who do not have a customer focus. This suggests that Accenture will improve their client company's performance whether their model is correct or not.

More on the brand loyalty variable issue later.

Customer Satisfaction and Financial Success

I have seen a number of blogs that downplay the impact of customer satisfaction on the financial performance of the firm. I have just published (with my co-author) a refereed article that shows that there is a statistical relationship between companies with a customer focus and the financial performance of the company. The article is "Customer Focus: One Key to Financial Success". It is published in Volume 2, Issue 2 of the Business Renaissance Quarterly (Summer 2007), pages 41-49.

In the article we analyzed 30 companies in twelve industries and used the ACSI scores from the University of Michigan as the basis for their customer focus. We then tested the companies that had high ACSI scores against those who had low ACSI scores within each industry. All companies had to be listed in the Value Line Investment Survey and we used the financial data from the March 27, 2007 issue. We used 7 different financial measures including Beta, growth in sales, growth in cash flow, profit margin, price stability, price growth consistency, and earnings predictability. Four of the financial statistics were statistically significantly higher for those companies with high ACSI scores. The three that were not statistically significant had very large variances. There was no measure in which the worst companies had financial performance better than the best companies.

CFiq versus NPS

It looks like the IBM Global Businesss Services group has developed a new measure for customer advocacy that may be intended to compete with NPS. the IBM measue has the acronym CFiq which stands for Customer Focused Insight Quotient. In fact, they have Trademanrked the CFiq acronym.

IBM has published two reports using this measure; one is for retail banking and the other is for the property and casualty industry. The three questions IBM appears to be using for the property and casualty industry are:
1. I would recommend my insurance agent/carrier to friends and family.
2. I would buy my next product from my agent/carrier.
3. If another insurance carrier offered me a competitive insurance product I would remain with my insurance agent/carrier.

For the retail banking study IBM apparently used the following questions:
1. I would recommend my bank to friends and family.
2. I would go to my bank first for future financial services needs.
3. I would stick with my bank if offered a competitively priced product.

The two sets of questions are suffiently similar to conclude that CFiq will probably apply to many industries.

Their analysis allocates customers into three groups; namely
1. advocates, those who have a high likelihood to recommend, high purchase intent and low switching intent
2. apathetics, those in the middle tier
3. antagonistic, those customers in the lowest tier who probably have characteristics opposite the advocates.

This metric appears to be well researched and according to their reports was created by surveying over 18,000 consumers across multiple industries.

IBM has characterized a customer focused enterprise (those that would be users of CFiq) with the following;
1. the company understands customer authority.
2. the company maintains a customer dialog.
3. the company integrates its functions to provide a consistent experience for their customers.
4. the company seeks solution experiences that address broader customer needs and desires.
5. the company focuses on a human performance approach that allow employees to better meet their personal and organizational objectives.
6. the company focuses on transforming its own organization to fullfill customer-centric strategies and objectives.

I believe IBM is making a statement that CFiq is to be preferred over NPS when they make the statement in their report "Unlike other satisfaction or advocacy measures, the CFiq goes beyond a single measure of satisfaction or a likelihood to recommend."

Measurement is Important

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.

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.

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.