Research without a call to action may be informative, but not very useful. One way to build a call to action element into your customer experience research is to add a measure of customer loyalty. Loyalty can serve as a basis for evaluating which elements of the service mix are most important in terms of driving customer loyalty, and as result, have more potential ROI.
Measuring customer loyalty, however, in the context of a survey is difficult. Surveys best measure attitudes and perceptions. Loyalty is a behavior. Kinesis has had success with a model for estimating customer loyalty based on two measurements:
- Promoter: This is measured with the likelihood of referral to a friend relative or colleague, using a numeric scale.
- Trust: Trust is measured by capturing agreement with the statement, “the company cares about me, not just the bottom line.” Again answered in a numeric scale.
These two measures are combined together to calculate a loyalty index, which visually is the linear distance of the plot of these two measurements from the highest possible value for each scale (cases where promoter and trust received the highest possible rating).
Mathematically, this index can be calculated with the following equation:
T = Trust rating
P = Promoter rating
ST = Number of points on the Trust scale
SP = Number of points on the Promoter scale
Note this index measures the distance from the ideal or most loyal state. Lower values estimate stronger loyalty.
Calculating a loyalty index has value, but limited utility. A loyalty index alone does not give management much direction upon which to take action. One strategy to increase the actionably of the research is to use this index as a means to identify the service attributes that drive customer loyalty. Not all service attributes are equal; some play a larger role than others in driving customer loyalty.
So…how does the research determine an attribute’s role or relationship to customer loyalty? One tool is to capture satisfaction ratings of specific service attributes and determine their correlation to the loyalty statistic. The Pearson correlation coefficient is a measure of the strength of a linear association between two variables.
The following table contains a hypothetical list of service attributes and their correlation to the loyalty index. Note lower values of the loyalty index indicate stronger loyalty, so the Pearson correlations to the attribute satisfaction ratings are negative. The closer the correlation is to -1 equates to a stronger relationship to loyalty.
|Pearson Correlation to Loyalty Index|
|Perform services as promised/right the first time||-0.62|
|Show interest in solving problems||-0.61|
|Problems resolved quickly||-0.56|
|Willingness to help/answer questions||-0.55|
|Perform services on time||-0.54|
|Employees instill confidence in customer||-0.52|
|Questioning to understand needs||-0.45|
|Appearance/cleanliness of personnel||-0.42|
|Knowledgeable employees/job knowledge||-0.41|
|Appearance/cleanliness of physical facilities||-0.37|
As this table illustrates, the service attributes with the strongest correlation to the loyalty index are: perform services as promised/right the first time (-0.62), show interest in solving problems (-0.61), and employee efficiency (-0.58). Under this hypothetical example, the hypothetical managers can conclude that of the attributes measured, these three are the strongest drivers of customer loyalty. They now can use this research to make informed judgments as to where investments in the service mix will yield the most ROI.
Correlating service attributes to loyalty is not the end of the analysis; the next step is to further put this research to action by layering in the overall performance of each attribute relative to its relationship to loyalty.
Research has determined the business attribute with the highest correlation to profitability is customer loyalty. Customer loyalty lowers sales and acquisition costs per customer by amortizing these costs across a longer lifetime – leading to some extraordinary financial results. Depending on the industry, a small increase in customer loyalty (5%) translates into a 25% – 85% increase in profits.
Customer loyalty is driven by the entire relationship with the company. Image, positioning, products, price, cost of switching, and service all form a value equation each customer applies in their continuous decision to remain loyal.
Measuring customer loyalty, however, in the context of a survey is difficult. Surveys best measure attitudes and perceptions. Loyalty is a behavior based on rational decisions customers make continually through the lifecycle of their relationship with the company. Customer experience researchers therefore need to find a proxy measurement to determine customer loyalty. One might measure customer tenure under the assumption that length of relationship predicts loyalty. However, customer tenure is a poor proxy. A customer with a long tenure may leave the firm, or a new customer may be very satisfied and highly loyal.
Kinesis has had success with a model for estimating customer loyalty based on two measurements: likelihood of referral and customer advocacy. Likelihood of referral captures a measurement of the customer’s likelihood to refer the company to a friend, relative or colleague. It stands to reason, if one is going to refer others to the bank, they will remain loyal as well. These promoters are putting their reputational risk on the line founded on a feeling of loyalty and trust. This concept of trust is perhaps more evident in the second measurement: customer advocacy. Customer advocacy is captured by measuring agreement with the following statement: “The Company cares about me, not just the bottom line.” Customers who agree with this statement trust the firm to do right by them, and will not subjugate their best interests to profits. Customers who trust the company to do the right thing are more likely to remain loyal.
Kinesis uses likelihood of referral, hereafter labeled “Promoter” and customer advocacy, hereafter labeled “Trust” to calculate an estimate of the customer’s loyalty. Imagine a plot where each customer’s promoter score is plotted along one axis and the trust score plotted along the other. Using this plot we can calculate the linear distance between the perfect state of the highest possible trust and promoter ratings. This distance yields a loyalty estimate, where the lower the value, the higher the estimate of loyalty – low values are good. The mathematical equation for this distance is as follows:
- T = Trust rating
- P = Promoter rating
- ST = Number of points on the Trust scale
- SP = Number of points on the Promoter scale
- Strongest Loyalty: The strongest zone of loyalty contains cases where both the Trust and Promoter attributes received the highest possible rating.
- Strong Loyalty: The next zone is where the loyalty index lies within 35% of both the Trust and Loyalty axis.
- Moderate Loyalty: The zone of moderate loyalty is where the index lies within 60% of the highest possible Trust and Promoter ratings.
- Weak Loyalty: The zone of weak loyalty lies within 90% of the highest possible Trust and Promoter ratings.
- Weakest Loyalty: The zone with the weakest loyalty are cases where one or both of the Trust and Promoter scores are less than 90% of the highest possible for Trust and Promoter..
Given that for many industries the business attribute with the highest correlation to profitability is customer loyalty; it is incumbent upon survey researchers to gather a measure of customer loyalty as part of their customer experience measurement. Kinesis’ approach of calculating a loyalty index based on “would recommend” and “customer advocacy” ratings has proven to be a useful tool for segmenting customers by an estimate of their loyalty. The next step in this analysis is to put this segmentation to work identifying which service attributes will yield the most ROI in term of driving customer loyalty.
 Heskett, Sasser, and Schlesinger The Service Profit Chain, 1997, New York: The Free Press, p 21
For most service industries the business attribute with the highest correlation to profitability is customer loyalty. It is, therefore, very important to gather a measurement of customer loyalty. However, simply calculating a loyalty index is not enough. Estimating customer loyalty is important, and an obvious first step; however, alone – without any context – is not very useful.
What’s needed is a methodology to transition research into action, and identify clear paths to maximize return on investments in the customer experience. What managers need is a tool to help them prioritize the service behaviors on which to focus improvement efforts. One such tool is an analytical technique called Gap Analysis.
Gap Analysis compares performance of individual service attributes relative to their importance, providing a frame of reference for prioritizing which areas require attention and resources.
To perform Gap Analysis, each service attribute measured is plotted across two axes. The first axis is the performance axis. On this axis the performance of each attribute is plotted. The second axis is the importance axis. Each attribute is assigned an importance rating based on its correlation to the loyalty index. Service attributes with strong correlations to loyalty are deemed more important and service attributes with low correlations are deemed less important.
This two-axis plot creates four quadrants:
- Quadrant 1: Areas with high correlations to loyalty and low performance. These service attributes are where there is high potential of realizing return on investments in improving performance.
- Quadrant 2: Areas with high correlations to loyalty and high performance. These are service attributes to maintain.
- Quadrant 3: Areas with low correlations to loyalty and low performance. These are service attributes to address if resources are available.
- Quadrant 4: Areas with low correlations to loyalty and high performance. These are service attributes which require no real attention as their performance exceeds their importance.
To illustrate this analysis methodology, consider the example below with the following service attributes:
|Appearance/cleanliness of physical facilities||
|Appearance/cleanliness of personnel||
|Perform services as promised/right the first time||
|Perform services on time||
|Show interest in solving problems||
|Willingness to help/answer questions||
|Problems resolved quickly||
|Knowledgeable employees/job knowledge||
|Employees instill confidence in customer||
|Questioning to understand needs||
Plotted on the above quadrant chart, they yield the following chart:
In this example, problems resolved quickly, employee efficiency, willingness to help, employees instill confidence are the four behaviors with relatively high correlations to the loyalty index and relatively low performance As a result, improvements in these attributes will yield the highest potential for ROI in terms of improving customer loyalty.
Using gap analysis, managers now have a valuable indicator to identify service attributes to focus improvement efforts on. Directing attention to the attributes in Quadrant I should have the highest likelihood realizing ROI in terms of the customer experience improving purchase intent.
What impresses customers positively as a result of a visit to your branch?
To answer this and other questions, Kinesis conducted research into the efficacy of the branch sales process and identified several service and sales attributes that drive purchase intent. (See the insert below of a description of the methodology).
In our observational research of 100 retail banking presentations, mystery shoppers were asked to describe what impressed them positively as a result of the visit to the branch. Excluding the branch atmosphere, the five most common themes contained in these open-ended comments were:
- Attentive to Needs/ Interest in Helping/ Personalized Service,
- Professional/ Courteous/ Not Pushy, Positive Greeting,
- Friendly Employees, and
- Rep. Product Knowledge/ Informative/ Confidence in Rep.
In an effort to understand the relative importance of these behaviors in driving purchase intent, shoppers were asked to rate their purchase intent, as a result of the presentation, as if they had been an actual customer. Shops were then grouped into those with positive and negative purchase intent and compared to each other.
Of these drivers of a positive impression, three have positive relationships to purchase intent – they tend to be present with greater frequency in shops with positive purchase intent compared to those with negative purchase intent.
Reason for Positive Purchase Intent
Relative Frequency Positive to Negative Purchase Intent
|Rep. Product Knowledge/ Informative/ Confidence in Rep.||
|Attentive to Needs/ Interest in Helping/ Personalized Service||
The banker’s product knowledge was present 2.7 times more frequent in shops with positive purchase intent relative to shops with negative purchase intent. Similarly, attention to needs and personalized service was present 2.5 times more in shops with positive purchase intent compared to those with negative purchase intent. Finally, shoppers were 2.3 times more likely to cite the friendliness of the bankers in shops with positive purchase intent relative to negative.
The observations contained within this research are not rocket science. What customers want, what drives purchase intent, is personal: attention to needs, interest in helping, personalized service, professional, courteous and friendly encounters.
To evaluate the state of the in-branch sales process, Kinesis mystery shopped 100 branches among five banks with significant North American footprints. Among the objectives of the study were to:
1) Define the sales process among different institutions.
2) Evaluate the effectiveness of specific sales behaviors.
Shoppers were asked a mixture of closed-ended questions to evaluate the presence or frequency of specific behaviors, and open-ended questions to gather the qualitative impressions of these behaviors on the shoppers – in short the how and why behind how the shopper felt. Finally, to provide a basis to evaluate the effectiveness of each sales behavior, shoppers were asked to rate their purchase intent as a result of the visit. This purchase intent rating was then used as a means of evaluating what behaviors tend to be present when positive purchase intent is reported as opposed to negative purchase intent.
Return On Investment: Quantify the return when forming a service strategy. Is good service a good thing?
Author: Peter Gurney
Reprinted from the Puget Sound Business Journal
October 5, 2001
Most business people would say it is. It keeps customers coming back, they will claim. And it could lead to more frequent purchases, fewer complaints, positive word-of-mouth and other activities that affect profitability. But if you ask how much they should invest in good service and how much return they can expect from their investment, the answers start to get fuzzy.
Most business people don’t know what their return on investment, or ROI, in customer service is, even what it should be. Nevertheless, they spend money – sometimes an astonishing amount – on employee training, customer satisfaction research, mystery shopping and manager incentives with the belief that it will pay off in the end.
This is a good time to evaluate customer service expenditures and determine how to maximize investments.
So often business professionals give little scrutiny to customer service expenditures, which tend to be based not on calculated benefit, but on blind faith.
This approach begins with the unchallenged belief that good service always leads to higher profits. Companies launch service crusades, making grand promises to their customers as they whip their staff into a frenzy of friendly service activity. They intone ritual phrases, like, “We’re dedicated to excellence,” and “The customer is No. 1.” They proclaim they will become the Nordstrom of their industry. And they contribute a substantial amount of money to the effort, confident it is going to a good cause.
In the end, the miracle they had hoped for seldom appears. Customers may be more satisfied, but the desired rise in profitability rarely occurs. There may be profit changes, up or down, but it is devilishly difficult to figure out how much effect service quality had on the change.
At this point many companies experience a crisis in faith and revert to their old practices: cost cutting, reductions in staff and new ad campaigns. Poorer but wiser, they look back at their crusade and wonder how they could have been so naive.
Despite efforts of so many companies to improve service, customer satisfaction levels have been dropping nationwide for years. In fact, the American Customer Satisfaction Index, a cross-industry national economic indicator of customer satisfaction, reports that 38 industries polled show a steady decline since 1994.
Perhaps it is time to take a different approach, beginning by redefining what makes service good or bad.
Here is a suggestion. Good service should begin and end with profit. If there is no predictable, measurable ROI, it isn’t good for anybody in the long run. Investors get a suboptimal return, employees suffer through service crusades doomed to failure, and customers are set up with unrealistic expectations that companies cannot meet.
The newest approach to customer service is a holistic one – Customer Experience Management – better known as CEM. It’s a profit-based approach to service beginning with companies asking the question, “What do we want our customers to do more of or less of?”
Do we want them to spend more with each purchase? To complain less frequently? To recruit new customers through word-of-mouth? In making this list, attitudes (such as satisfaction) and feelings (such as delight) are not included – only measurable, observable customer behaviors that can plausibly be influenced through service interactions.
The next step in this exercise is to calculate the financial effect of an incremental change in each customer behavior. What would be the effect on revenue of increasing the average customer purchase by one dollar, or reducing the volume of complaints to call centers by five percentage points? It quickly becomes clear even a small change in some customer behaviors can have a substantial financial impact.
This process next moves to the subject of employee training. What specific knowledge and skills are needed to influence desired customer behaviors? Then, you need to ask what rewards will be most effective at reinforcing the use of those skills? What metrics need to be gathered to trigger rewards?
With this process, there is always a clear path to making money. All of those fuzzy, feel-good terms that so many businesses base their service initiatives on, like “customer loyalty” and “customer delight,” are left to the public relations companies. This doesn’t mean there is no benefit to customers. On the contrary, the types of behaviors desired of customers will only come about if they are satisfied, loyal and occasionally delighted. But companies cannot make the world a better place for customers unless they show a profit. By defining good service in terms of its effect on the bottom line, everybody wins.
Probably the most common problem facing the customer experience researcher is the actionability or usefulness of the research. All too often, while there may be lots of data available, managers lack methodologies to transition research into action, and identify clear paths to maximize return on investments in the customer experience.
This is particularly true with mystery shopping. When done correctly, mystery shopping can be a valuable tool. However, often managers collect data about the service behaviors of their employees, but lack a clear means of identifying which behaviors to focus improvement efforts on, or identifying service attributes have the most potential for ROI.
What managers need is a tool to help them prioritize the service behaviors on which to focus improvement efforts. One such tool is an analytical technique called Gap Analysis.
Gap Analysis compares performance of individual service attributes relative to their importance, providing a frame of reference for prioritizing which areas require attention and resources.
To perform Gap Analysis, each service attribute measured is plotted across two axes. The first axis is the performance axis. On this axis the performance of each attribute is plotted. The second axis is the importance axis. Each attribute is assigned an importance rating based on its correlation to purchase intent. Service attributes with strong correlations to purchase intent are deemed more important and service attributes with low correlations to purchase intent are deemed less important.
This two-axis plot creates four quadrants:
- Quadrant 1: Areas of high importance and low performance (where there is high potential of realizing return on investments in improving performance).
- Quadrant 2: Areas of high importance and high performance. These are service attributes to maintain.
- Quadrant 3: Areas of low importance and low performance. These are service attributes to address if resources are available.
- Quadrant 4: Areas of low importance and high performance, these are service attributes which requ
ire no real attention as their performance exceeds their importance.
To illustrate this concept, consider the following example quadrant chart where seven service quality attributes are plotted according to their performance and importance. The “cross-hairs” defining the quadrants are the mid-point (or average) of both the importance and performance measures. In this case the mid-point of the performance measures is 74%, and the mid-point of the importance axis is 2.9.
According to this example, two service attributes reside in the first quadrant (high importance and low performance). These attributes are introduce product or service by using targeted question and mention any other product or service. These two attributes, therefore, are the two that should be focused on first, as improvements in these should yield the most ROI in terms of improving purchase intent.
No attributes are in the second quadrant (high importance and high performance), and one attribute, offer further assistance, resides in quadrant three (an area to address if resources are available). The remaining four attributes reside in the fourth quadrant, where performance exceeds importance, and therefore do not require any immediate attention.
In this example, the manager now has a valuable indicator regarding which service attributes they should focus their improvement efforts. Directing attention to the attributes in Quadrant 1 should have the highest likelihood realizing ROI in terms of the customer experience improving purchase intent.
Most business people would agree that there is value in good service. An abundance of literature exists supporting the notion that service can affect retention, spending, word-of-mouth endorsements and other customer activities that make a company more profitable. However, there are also many examples of companies with excellent service that chronically suffer from poor financial performance.
Good service is expensive. It requires research, training, measurement and the payout of incentives to managers and employees. Because it costs so much, companies struggle with the question of what their return on investment should be. Some even ask whether the investment is worth making at all. Could the money dedicated to improving service be more profitably spent in some other way?
The question is a fair one. Simply assuming that good service is a good investment is not very businesslike. Investment opportunities should be weighed against each other, with expected risks and returns assessed to determine the best choices. Unfortunately, few companies have had success calculating the ROI of customer service, making it difficult for them to determine whether their money will be, or has been, well spent.
Approaches to calculating service ROI appear to fall into two major camps: “Blind Faith” and “Rube Goldberg”. Rube Goldberg was a Pulitzer Prize winning cartoonist, famous for his outrageous “inventions”, which performed simple tasks in complicated ways. He produced cartoons similar to this:
The Blind Faith approach begins with the unchallenged belief that good service always leads to higher profits. Companies launch service crusades, making grand promises to their customers as they whip their staff into a frenzy of friendly service activity. They intone ritual phrases, like, “We’re dedicated to excellence,” and “The customer is number one.” And they contribute a substantial amount of money to the effort, confident that it is all going to a good cause.
In the end, the miracle they had hoped for seldom appears. Customers may be more satisfied, but the expected rise in profitability rarely occurs. There may be profit changes, up or down, but it is devilishly difficult to figure out how much effect service quality had on the change.
At this point many companies experience a crisis in faith and revert to their old practices: cost-cutting, reductions in staff, new ad campaigns. Poorer but wiser, they look back at their crusade and wonder how they could have been so naive.
The Service Machine
The Rube Goldberg camp takes a more mechanistic approach. These folks don their white lab coats and attempt to build predictive models that explain the links between service attributes, customer satisfaction and profitability. They use statistical techniques to uncover correlations and coefficients and co-variation, revealing that a twelve-second reduction in average wait times will result in a one-point rise in customer satisfaction, which will turn into a half-cent increase in per-transaction revenue at a cost of a quarter of a penny, etc., etc.
Such models can, in fact, be valuable as a means for understanding the associations among different service and profit factors. They can also provide insight into how service attributes interact with each other to influence customer perceptions. A major drawback, however, is that these models tend to have too many moving parts to function as a practical, day-to-day business tool. In addition, they tend to give the appearance of being far more precise than they actually are. Many companies have spent considerable effort and money constructing such models, only to find that their applicability is marginal and their useful lifespan limited.
The Third Way
There is another approach – a third way that is simple, practical and intuitive. It does not purport to be as precise as the Rube Goldberg predictive models, nor does it treat service as sacrosanct, as with the Blind Faith followers. Rather, the Third Way takes the view that service isn’t profitable because it’s good, it’s good because it’s profitable.
The Third Way begins with the company making a list of customer behaviors that directly affect revenues or costs. The company asks itself, “What, specifically, do we want customers to do more of or less of?” Attitudes (such as satisfaction) and feelings (such as delight) aren’t included – only measurable, observable behaviors, such as, “use our service more often,” “call our support line less often,” “purchase more items on an average visit to the store,” and “return merchandise less frequently.”
The next step is to reduce the list by eliminating any items that cannot plausibly be influenced through service interactions. (Note that service interactions do not necessarily involve employees. ATMs, web sites and unmanned kiosks are all, from a customer’s point of view, service providers.) Working backwards, the company next makes a second list composed of specific, measurable service activities that are likely to affect desired customer behaviors. This list should only include items for which a realistic, cause-and-effect scenario between service behavior and customer behavior can be articulated. Again, attitudes and feelings are not included. The company asks itself, “What can employees (or machines or web sites) do more of or less of, or do differently, to influence how customers act?” If it can’t be measured, if it can’t be trained (or programmed) or if it has no likely effect on measurable customer behaviors that effect profit, it is removed it from the list.
This process of deconstruction next moves to the subject of training: What specific knowledge and skills are needed to provide the service that will affect desired customer behaviors? Then, incentives and measurement: What rewards will be most effective at reinforcing the use of those skills? What metrics need to be gathered to trigger rewards?
Each list is winnowed to ensure that it applies only to the items on the previous list. In this way, the picture is never cluttered with irrelevant or ambiguous elements. Because every item on every list is concrete and measurable, the people who are accountable for delivering service and making it pay will know precisely what is expected of them.
The next step is to link the first list (customer behaviors) to costs and revenues. To do this the company calculates the financial effect of an incremental change in each item. For example, what would be the effect on revenue of increasing the average customer purchase by one dollar? What would be effect on costs if the volume of complaints to call centers were reduced by five percentage points? It quickly becomes clear that even a small change in some customer behaviors can have a substantial financial impact. It also becomes clear which service changes will have the biggest effect.
The company has thus far identified the customer behaviors it wants to change, the general effect of each behavior on revenue or cost, and the dollar value of an incremental change in each behavior. In addition, it has identified the service activities that are likely to influence changes in customer behaviors, and a strategy for promoting those activities through training, measurement and rewards. All of these steps can be accomplished in a day with a few managers and pot of strong coffee. But from this point on, the process gets a bit trickier.
The major element missing from the formula is magnitude. How much change can the company expect to create? Can complaints be reduced by 1%, 5%, 10%? Will average purchase amounts increase by 50 cents? Ten dollars?
Also missing is the interaction among different variables. For example, aggressive up-selling may lead to a 10% increase in the average transaction amount, but it could also lead to a 2% increase in customer turnover, which might counteract the benefit.
The only way to answer these questions is to experiment. The company must identify the most promising service investments and test them on a small scale. Service units (stores, call centers, etc.) should be compared, using test and control groups. The groups should be small enough to keep the experiment manageable, but large enough to wash out the influence of temporary, outside factors, such as bad weather or a blow-out sale by a competing store.
The experimentation does not stop with one test. The process is iterative, allowing the company to fine-tune its tactics and find the optimal mix of service activities that result in the highest return on investment. With patience, a reliable formula for ROI will emerge and the company can decide which service improvements it should invest in – or whether it should invest in service improvements at all.
To return to an earlier statement: The Third Way takes the view that service isn’t profitable because it’s good, it’s good because it’s profitable. This doesn’t mean there is no benefit to customers. On the contrary, the types of behaviors desired of customers will only come about if they are satisfied, loyal and occasionally delighted. The point is, companies cannot make the world a better place for customers unless they show a profit. By defining good service in terms of its effect on the bottom line, everybody wins.
Baseball is an interesting game. It is our nation’s pastime, the subject to many books and movies, and, at the major league level, a patently unfair game.
What is the source of this unfairness? – In a word, payroll. This year the Yankees enter the season with a payroll of $149 million compared to the Tampa Bay Devil Rays who enter this season with a payroll of just under $20 million.
Yet, in spite of this inequity, one team with one of the lower payrolls seems to win consistently. In the last four years, the Oakland A’s have won fifty-nine percent of their games, and finished no worse than second in the American League. Last season with a payroll of $40 million Oakland won 103 games – as many games as the Yankees with their 2002 payroll of $126 million. On average, the A’s spent $390,000 for each win compared to the Yankees’ $1.22 million.
How do they do this? Have they been lucky? – Doubtful, not four years in a row. Rather the A’s front office did something very unique for tradition bound major league baseball – they hired a Harvard MBA as assistant GM and conducted an objective analysis to determine which performance measures are key drivers of success in baseball. The result of this analysis has influenced everything from player selection to game strategy. Historically, player selection has been dominated by major league scouts who subjectively evaluate players based on their potential to run, throw, field, hit and hit with power. What the A’s did was conduct research to determine the relative value of each of these attributes in order to maximize the return on their payroll.
Among the conclusions they reached, they found that teams like the Yankees overpaid for speed – that on-base percentage (the percent of the time a batter reaches a base either via a hit or a walk) and slugging percentage (the number of bases achieved per at bat) both correlated closer to winning percentage than any other factor, including speed and batting average. Intuitively this makes sense, the higher the on-base percentage, the lower the probability of being called out. The result of this research? The A’s seek players with skills other teams undervalue, players with less speed, but who don’t swing at bad pitches, control the strike zone and force walks – yet also have the ability to hit home runs. In any given inning, two walks and a home run is their recipe for success.
So, what’s the lesson for business?
There are many. Among them: the importance of objective research over subjective opinions, the value of seeking and exploiting inefficiencies in the market place, and the competitive advantage that can be gained by challenging conventional wisdom.
Conventional marketing efforts have historically centered around the four P’s of marketing: product, price, promotion and place – with the primary objective of building market share. In the 70’s a body of research entitled PIMS (Profit Impact of Market Strategy) concluded that market share was determinate of profitability.
Like the A’s, another group of Harvard researchers in the early 90’s started questioning conventional wisdom. The problem they noticed, was that the PIMS studies focused largely on the manufacturing sector and failed to differentiate between manufacturing and service industries. In studying service industries they concluded there was no correlation between profitability and market share. Rather, the attribute with the highest correlation to profitability is customer loyalty. Depending on the industry, a small increase in customer loyalty (5%) translated into a 25% – 85% increase in profits. As with baseball’s on-base percentage, this result is intuitive – customer loyalty lowers sales and acquisition cost per customer by amortizing it across a longer lifetime – leading to some extraordinary financial results.
Like baseball, conventional wisdom gives way to a new paradigm. The importance of the four P’s gives way to the importance of the three R’s: retention, related sales, and referrals.
So the question is – what is the cause of customer loyalty? Or even what is customer loyalty? Is customer loyalty delighting customers? Is customer loyalty dependent on fostering customers who will stick with the firm through thick and thin? – I don’t believe so, any more than I believe the Oakland A’s are lucky.
Customer loyalty is a rational decision each customer makes every time they patronize a firm – be it the first time or the millionth time. The mental equation behind each customer’s rational decision to buy is the Customer Value Equation, which is the ratio of results and process quality to price and all other costs of acquiring the service (tangible and intangible).
The Customer Value Equation is the rational underpinning behind customer loyalty. The idea of customer
loyalty is certainly not new, yet few firms do a very good job of loyalty-based management. Like the Oakland A’s, any firm which practices customer values based management and manages according to the Customer Value Equation, with the objective of maximizing loyalty and the three R’s, will gain an advantage in realizing return on investment relative to their competition.
Author: Peter Gurney
Reprinted from the Puget Sound Business Journal
CEOs know, or should know, the “secrets” to great customer service. In fact, there really are no secrets. Those who practice customer service successfully are generally happy to share what they know, and there is no shortage of books, articles and studies on the subject.
Nevertheless, most service in this country is mediocre, an observation made by Howard Schultz, Chairman of Starbucks, at a recent Professional Speaker Series on the topic of “Customer Service: Exceeding Expectations.”
A variety of reasons have been offered to explain why it’s a challenge for companies to raise the bar on service quality. Among the more familiar: increased demand for customer service personnel, due to an economic shift from manufacturing to service; higher turnover and loss of loyalty, as a result of the layoffs, mergers and acquisitions of the ’80s and ’90s; and the migration of traditional customer service workers to higher paying professions.
A less frequently mentioned factor, however, is the long-term investment needed to create a culture of service excellence.
It takes years to develop and successfully implement a service initiative, but our society and business systems mitigate against investing in such long-term initiatives. The payback is too slow and uncertain for many companies to support and for those companies that are publicly traded, mangers must plan within the time horizons of the investors for whom they act as agents.
Stockholders have little patience nowadays with investments that do not show a clear and quick return. To ensure that managers are acting in the owners’ interests, management incentives are more frequently tied to quarterly financial performance than to difficult-to-measure variables like customer loyalty.
In such an environment service-related programs are sitting ducks when the budget ax comes out. Service initiatives have a tendency to come and go in large companies before they have a chance to prove their worth, resulting in customer frustration, employee cynicism and widespread service mediocrity.
Author: Peter Gurney
Reprinted from Northwest Entrepreneur Network
Think about the organizations that stand out as leaders in customer service – companies like Southwest Airlines, Federal Express and Nordstrom. Now, contrast these companies with the parade of mediocre service providers we encounter every day as customers: the restaurant chains and retail stores, and the telecommunications companies that perpetually annoy us with long waits, uninformed personnel and self-serving policies (can you spell “Qwest”?). What the first group of companies has in common is that they planned for service excellence from the beginning. Good service is not something they tried to introduce after they were up and running; it is part of their DNA, an essential component of their business model.
These service leaders offer a valuable lesson for entrepreneurs. Business plans typically include details about how to attract customers, but little information about how to sustain and build relationships after acquisition. They often include platitudes like, “We are a customer-centric organization,” and “We create value for all of our stakeholders,” but seldom present a clear strategy that shows how customers will be retained, nurtured and made progressively more profitable over time.
There are many financial benefits to articulating and following a strategy of service excellence: lower customer and employee turnover, fewer complaints and returns, greater share of wallet, positive word-of-mouth endorsements. But good service costs money, and it is easy to invest in service standards and programs that do not provide a measurable return. The key is to form a clear strategy that links service investments with profit-based outcomes, and to make sure the service strategy aligns with other components of the business model.
Young companies with no formal service strategy frequently give the appearance of being “customer-centric” – first, because there aren’t many customers, and second, because the internal service providers tend to be owners or others who are heavily invested in the organization’s success. Over time, however, the responsibility for customer relationships will devolve to employees who are less invested and less empowered to take risks, solve problems and spend money on solutions. At this point a formal service strategy acts as a roadmap to keep the company true to its vision.
At the least, the service strategy should indicate how customers will be retained after acquisition. Many of the now-defunct dot-coms failed in part because they neglected to think through a retention approach after spending millions on attracting customers. Other companies suffer from chronic customer churn and low profitability because they have not aligned customer expectations with service delivery.
A retention strategy begins with an understanding of the typical customer’s lifecycle with the company. It identifies the points at which the customer and company interact, when and how often those interactions are likely to occur, and how they can best be used to advance the customer relationship.
In the book “The Loyalty Effect”, author Frederick Reichheld makes the case that the longer customers remain with a company, the more profitable they become. The loyalty effect works because long-term customers have more opportunities to learn about the company (and vice versa), allowing the relationship to become increasingly efficient and productive. But the benefits of loyalty do not occur simply because customers have more experiences with the company over time. To move up the loyalty/profit curve they need to have the types of experiences that will add to their knowledge and influence their behavior. Understanding the customer lifecycle allows the company to plan the right types of customer experiences at the right time.
Implementation decisions follow logically from the service strategy. By identifying when and how interactions occur, as well as what they should accomplish (both for the customer and the company), one can work backwards to design a service-focused organization. The service strategy helps define appropriate standards and policies, and also suggests how standards and policies can be supported through hiring practices, training content, research and measurement, business tools, etc.
Consider research and measurement. Many companies make a substantial investment in customer surveys, call monitoring, mystery shopping and other service-related research that generates reams of data reports — which sit, unread and unused, on the desks of overworked managers. But with a clearly articulated service strategy that is incorporated into the business model, companies can make much better decisions from the first about what data they should collect and how they should use it. For example, by mapping out the points at which service failures are most likely to occur, the company can create feedback channels that will identify at-risk customers, and service recovery mechanisms to prevent turnover. The service strategy will also suggest what data should be captured to ensure that standards are executed properly and that employees and managers are rewarded for the right outcomes.
Choices of business tools and systems also follow from the service strategy. Consider that in the past few years billions of dollars have been spent on Customer Relationship Management (CRM) systems that have under-performed or failed to produce a positive return. One of the primary reasons for this failure is that many systems were installed with no logical context: companies invested in improving customer relationships without defining what an ideal customer relationship should look like. A clear service strategy fills that void, allowing the company to invest in tools and systems that support a well-defined vision.
All of these details do not necessarily go into the business plan – they may go into the plan behind the plan, or emerge over time as the company evolves. What does go into the business plan is a declaration of the role that service will play in the overall offering of the company, and a description of how the service strategy will align with other components of the organization. Also included is a picture of what the relationship between the company and customer should look like as it advances through the customer lifecycle. Armed with this information, new companies can make service work for them from the outset.