Tag Archive | ROI

Implications of CX Consistency for Researchers – Part 2 – Intra-Channel Consistency

Previously, we discussed the implications of inter-channel consistency for researchers, and introduced a process for management to define a set of employee behaviors which will support the organization’s customer experience goals across multiple channels.

This post considers the implications of intra-channel consistency for customer experience researchers.

As with cross-channel consistency, intra-channel consistency, or consistency within individual channels requires the researcher to identify the causes of variation in the customer experience.  The causes of intra-channel variation, is more often than not at the local level – the individual stores, branches, employees, etc.  For example, a bank branch with large variation in customer traffic is more likely to experience variation in the customer experience.

Regardless of the source, consistency equals quality.

In our own research, Kinēsis conducted a mystery shop study of six national institutions to evaluate the customer experience at the branch level.  In this research, we observed a similar relationship between consistency and quality.  The branches in the top quartile in terms of consistency delivered customer satisfaction scores 15% higher than branches in the bottom quartile.  But customer satisfaction is a means to an end, not an end goal in and of itself.  In terms of an end business objective, such as loyalty or purchase intent, branches in the top quartile of consistency delivered purchase intent ratings 20% higher than branches in the bottom quartile.

Satisfaction and purchase intent by customer experience consistency

Purchase intent and satisfaction with the experience were both measured on a 5-point scale.

Again, it is incumbent on customer experience researchers to identify the causes of inconsistency.   A search for the root cause of variation in customer journeys must consider processes cause variation.

One tool to measure process cause variation is a Voice of the Customer (VOC) Table. VOC Tables have a two-fold purpose:  First, to identify specific business processes which can cause customer experience variations, and second, to identify which business processes will yield the largest ROI in terms of improving the customer experience.

VOC Tables provide a clear road map to identify action steps using a vertical and horizontal grid.  On the vertical axis, each customer experience attribute within a given channel is listed.  For each of these attributes a judgment is made about the relative importance of each attribute.  This importance is expressed as a numeric value.   On the horizontal axis is a exhaustive list of business processes the customer is likely to encounter, both directly and indirectly, in the customer journey.

This grid design matches each business process on the horizontal axis to each service attribute on the vertical axis.  Each cell created in this grid contains a value which represents the strength of the influence of each business process listed on the horizontal axis to each customer experience attribute.

Finally, a value is calculated at the bottom of each column which sums the values of the strength of influence multiplied by the importance of each customer experience attribute.  This yields a value of the cumulative strength of influence of each business process on the customer experience weighted by its relative importance.

Consider the following example in a retail mortgage lending environment.

VOC Table

In this example, the relative importance of each customer experience attributes was determined by correlating these attributes to a “would recommend” question, which served as a loyalty proxy.  This yields an estimate of importance based on the attribute’s strength of relationship to customer loyalty, and populates the far left column.  Specific business processes for the mortgage process are listed across the top of this table.  Within each cell, an informed judgment has been made regarding the relative strength of the business process’s influence on the customer experience attribute.  This strength of influence has been assigned a value of 1 – 3.  It is multiplied by the importance measure of each customer experience attribute and summed into a weighted strength of influence – weighted by importance, for each business process.

In this example, the business processes which will yield the highest ROI in terms of driving the customer experience are quote of loan terms (weighted strength of influence 23.9), clearance of exemptions (22.0), explanation of loan terms (20.2), loan application (18.9) and document collection (16.3).

Next, we will look into the concepts of common and special cause variation, and another research methodology designed to identify areas for attention. Control charts as just such a tool.

Mystery Shopping Gap Analysis: Identify Service Attributes with Highest Potential for ROI

Research without call to action may be interesting, but in the end, not very useful.

This is particularly true with customer experience research.  It is incumbent on customer experience researchers to give management research tools which will identify clear call to action items –items in which investments will yield the highest return on investment (ROI) in terms of meeting management’s customer experience objectives.   This post introduces a simple intuitive mystery shopping analysis technique that identifies the service behaviors with the highest potential for ROI in terms of achieving these objectives.

Mystery shopping gap analysis is a simple three-step analytical technique.

Step 1: Identify the Key Objective of the Customer Experience

The first step is to identify the key objective of the customer experience.  Ask yourself, “How do we want the customer to think, feel or act as a result of the customer experience?”

For example:

  • Do you want the customer to have increased purchase intent?
  • Do you want the customer to have increased return intent?
  • Do you want the customer to have increased loyalty?

Let’s assume the key objective is increased purchase intent.  At the conclusion of the customer experience you want the customer to have increased purchase intent.

Next draft a research question to serve as a dependent variable measuring the customer’s purchase intent.  Dependent variables are those which are influenced or dependent on the behaviors measured in the mystery shop.

Step 2: Determine Strength of the Relationship of this Key Customer Experience Objective

After fielding the mystery shop study, and collecting a statistically significant number of shops, the next step is to determine the strength of the relationship between this key customer experience measure (the dependent variable) and each behavior or service attribute measured (independent variable).  There are a number of ways to determine the strength of the relationship, perhaps the easiest is a simple cross-tabulation of the results.  Cross tabulation groups all the shops with positive purchase intent and all the shops with negative purchase intent together and makes comparisons between the two groups.  The greater the difference in the frequency of a given behavior or service attribute between shops with positive purchase intent compared to negative, the stronger the relationship to purchase intent.

The result of this cross-tabulation yields a measure of the importance of each behavior or service attribute.  Those with stronger relationships to purchase intent are deemed more important than those with weaker relationships to purchase intent.

Step 3: Plot the Performance of Each Behavior Relative to Its Relationship to the Key Customer Experience Objective

The third and final step in this analysis to plot the importance of each behavior relative to the performance of each behavior together on a 2-dimensional quadrant chart, where one axis is the importance of the behavior and the other is its performance or the frequency with which it is observed.

Interpretation

Interpreting the results of this quadrant analysis is fairly simple.    Behaviors with above average importance and below average performance are the “high potential” behaviors.  These are the behaviors with the highest potential for return on investment (ROI) in terms of driving purchase intent.  These are the behaviors to prioritize investments in training, incentives and rewards.  These are the behaviors which will yield the highest ROI.

The rest of the behaviors are prioritized as follows:

Those with the high importance and high performance are the next priority.  They are the behaviors to maintain.  They are important and employees perform them frequently, so invest to maintain their performance.

Those with low importance are low performance are areas to address if resources are available.

Finally, behaviors or service attributes with low importance yet high performance are in no need of investment.  They are performed with a high degree of frequency, but not very important, and will not yield an ROI in terms of driving purchase intent.

Research without call to action may be interesting, but in the end, not very useful.

This simple, intuitive gap analysis technique will provide a clear call to action in terms of identifying service behaviors and attributes which will yield the most ROI in terms of achieving your key objective of the customer experience.

Mystery_Shopping_Page

Calculating ROI: A Simple Intuitive Customer Experience ROI Calculator

Calculating ROI on the customer experience typically takes the blind faith approach, where ROI on customer service is considered a given, and the sophisticated approach, where predictive models explain the links between service attributes, customer satisfaction and profitability. 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, give the appearance of being far more precise than they actually are, and may be too sophisticated for some audiences.

ROI

Sometime managers need a simpler, more intuitive approach to estimating ROI on customer service.

First let me suggest the proposition that every time a company and service provider interact the customer learns something positive or negative and adjusts their behavior, again positive or negative based on what they learn. This is a behavioral approach to managing the customer experience, that by managing customer behaviors in profitable ways, service providers can maximize return on investment in the customer experience.

Using this behavior approach as a model, it possible to construct a simple intuitive ROI estimate of the customer experience.

List customer behaviors with financial implications

The first step in this methodology is to list all customer behaviors that directly drive revenues or costs. Ask yourself, “What specifically, do we want customers to do more or less of?” Don’t include attitudes, such as satisfaction, or feelings such as delight – only include empirically measureable behaviors such as purchase more, purchase more frequently, call for support less often, use more profitable channels, return merchandise less frequently, etc.

Before moving on to the next step, review this list and eliminate any customer behaviors that cannot be influenced through service interactions.

List service attributes that likely influence customer behaviors

Next, work backwards making a second list of specific , measureable service attributes that likely influence desired customer behaviors. This list should only include attributes for which there is a realistic cause and effect relationship between the service attribute and customer behavior. Ask yourself, “What can we (across all service channels) do more of, less of, or do differently to influence customer behaviors?” If it can’t be measured, if it can’t be trained (or programmed) or if it has no likely effect on measureable customer behaviors that affect profit, it should be removed from the list.

Consider how to influence desired customer behaviors (systems, skills, incentives, measurement & rewards)

Now, consider what specific systems, knowledge and skills are required to provide the service that will influence desired customer behaviors. Consider what employee incentives will be most effective in reinforcing the use of those skills and what measurement tools need to be in place to gather the metrics to trigger appropriate rewards.

Link list of customer behaviors to costs and revenues (incremental change)

Finally, link the first list (customer behaviors) to costs and revenues. To do this, calculate the financial impact 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 the 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.

ROI CALCULATOR: See the attached spreadsheet for an example of a Customer Experience ROI calculator based on this approach.

Thus far you have identified the customer behaviors you want to change, the general influence of each behavior on revenue or cost, and the dollar value of an incremental change in each behavior.
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.

Finally, this method excludes word of mouth customer behavior. In this ad of social media, increasing word of mouth (positive or negative) is an important customer behavior to manage. It’s been excluded from this tool do difficultly empirically measuring its benefits. See the attached post for a description of word of mouth measurement.

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Shareholder Return and the Customer Experience: A Case for Investment in the Customer Experience

I recently came across a very intriguing bit of research that suggests the benefits of investments in the customer experience in terms of shareholder return.

Great customer service processes and people are not built overnight. They take years of investment to cultivate. Unfortunately, for some publically traded companies, the short-term demands of Wall Street make such investment difficult. The demands of investors to meet earnings estimates for the next quarter can make it difficult for managers to invest in the customer experience – the payback is too slow and uncertain.

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.

Given great customer experiences are not built overnight, they are constantly at risk of budget cuts by managers who would boost short term earning at their expense. 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.

Service gurus talk about the need for “investor loyalty” as a counterbalance to customer loyalty, but that requires a visionary, motivated and stable management team who can convince investors to look farther ahead.

Easier said than done, right? How does one make the case for investments in the customer experience in an environment that demands making the next quarters numbers?

Jim Picoult, founder of Watermark Consulting, has an answer. Jim has created a stock index based on Forester’s annual Customer Experience Index (CXI). Jim calculated the returns of two hypothetical portfolios consisting of the top and bottom 10 publicly traded companies in Forester’s CXI for a six year period ending in 2012. Each year he rebalanced the two portfolios based on Forester’s new rankings. The portfolio comprised of companies ranked in Forester’s top 10 yielded a cumulative return of 43%, compared to 14.5% for the S&P 500. The portfolio containing the bottom 10, yielded a cumulative return of negative 33.9% – it lost a third of its value.

Customer Experience Leaders Outperform the Market

Now, correlation is not causation, and there are a lot of factors at play here. But clearly the managers of firms in the portfolio of Forester’s top 10 were able to both deliver shareholder value and invest in the customer experience.

It all comes down to thinking of the customer as an asset in which to invest and realize a return.


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Not All Service Attributes Are Equal: Retail Bank Transaction Drivers of 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.  In one study of the retail banking industry, a 5% increase in customer loyalty translated into an 85% increase in profits.[1]

Customer loyalty is driven by the entire relationship with bank.  Image, positioning, products, price and service all mix together in the customer’s’ value equation as customers make a continual decision to remain loyal.

What customer service attributes drive customer loyalty?

This article summarizes research into specific transaction service attributes with the intent of identifying which transaction attributes drive customer loyalty, and provides an analytical tool to help managers determine which attributes will yield the highest potential for ROI in terms of improving customer loyalty.

In order to determine transaction attributes which drive customer loyalty, Kinesis surveyed bank customers who had recently conducted a transaction at a branch.

With respect to the transaction, customers were asked to rate the following service attributes:

  • Professional dress
  • Branch cleanliness
  • Prompt greeting
  • Greeting made customer feel welcome
  • Dependable and accurate
  • Prompt service
  • Willingness to help
  • Job knowledge
  • Interest in helping
  • Best interests in mind
  • Actively listened to needs
  • Ability of bank personnel to help achieve financial needs
  • Desire of bank personnel to help customers achieve financial goals
  • Commitment to community

The next step in the research is to capture a measurement of loyalty against which to compare these attributes.

Measuring customer loyalty 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 bank.  Survey researchers therefore need to find a proxy measurement to determine customer loyalty.  A researcher 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 bank, or a new customer may be very satisfied and highly loyal.

Measuring customer loyalty 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 bank.  Survey researchers therefore need to find a proxy measurement to determine customer loyalty.  A researcher 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 bank, or a new customer may be very satisfied and highly loyal.

Kinesis proposes 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 bank to friend, relative or colleague.  It stands to reason, if one is going to refer others to the bank, they will remain loyal as well.  Because customers who are promoters of the bank are putting their reputational risk on the line, this willingness to put their reputational risk on the line is 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: “My bank cares about me, not just the bottom line.”  Customers who agree with this statement trust the bank to do right by them, and not subjugate their best interests to profits.  Customers who trust their bank 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 for each customer, where the lower the value, the higher the estimate of loyalty – low values are good.[i]

Trust Promoter Plot

See Using Promoter and Trust Measurements to Calculate a Customer Loyalty Index for a complete description of this methodology.

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.

Comparing the correlation of the above service attributes to this loyalty estimate yields the following Pearson Correlation for each attribute:

Pearson Coefficient

Want to help me achieve financial goals

-0.69

Commitment to community

-0.66

Ability to help achieve financial goals

-0.64

Best interests in mind

-0.60

Greeting made customer feel welcome

-0.56

Interested in helping

-0.56

Willing to help

-0.55

Prompt service

-0.51

Actively listened to needs

-0.50

Prompt greeting

-0.49

Dependable and accurate

-0.45

Professional dress

-0.42

Knew job Job knowledge

-0.41

Branch attractive

-0.39

Branch clean

-0.37

Note the Pearson values are negative; the loyalty estimate is an inverse, where lower values indicate a stronger estimate of loyalty.  As a result the stronger negative correlation translates into a correlation to our estimate of loyalty.

The four attributes with the highest correlation to loyalty are:

  1. Want to help me achieve financial goals,
  2. Commitment to community,
  3. Ability to help achieve financial goals, and
  4. Having my best interests in mind.

Two common themes in the top-four attributes are empathy and competence.  Bank customers value relationships with banks that care about their needs and have the ability to satisfy those needs.  Again, customer loyalty is driven by the entire relationship with bank.  However, in terms of transactional service, customers clearly value empathy and competency and will reward banks who deliver on these two attributes with loyalty.


[i] The mathematical equation for this distance is as follows:

Loyalty Index Equation

Where:

T = Trust rating

P = Promoter rating

ST = Number of points on the Trust scale

SP = Number of points on the Promoter scale

 


[1] Heskett, Sasser, and Schlesinger The Service Profit Chain, 1997, New York: The Free Press, p 21


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Using Promoter and Trust Measurements to Calculate a Customer Loyalty Index

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.[1]

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.   Trust Promoter Plot

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:

 Loyalty Index Equation

Where:

  • T = Trust rating
  • P = Promoter rating
  • ST = Number of points on the Trust scale
  • SP = Number of points on the Promoter scale
 Kinesis’ experience plotting these indices, across a variety of scales, typically yields five zones of loyalty defined as follows:Loyalty Ranges
  1. Strongest Loyalty: The strongest zone of loyalty contains cases where both the Trust and Promoter attributes received the highest possible rating.
  2. Strong Loyalty: The next zone is where the loyalty index lies within 35% of both the Trust and Loyalty axis.
  3. Moderate Loyalty: The zone of moderate loyalty is where the index lies within 60% of the highest possible Trust and Promoter ratings.
  4. Weak Loyalty: The zone of weak loyalty lies within 90% of the highest possible Trust and Promoter ratings.
  5. 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.

Next Article: Using Gap Analysis to Put Loyalty Index into Action


[1] Heskett, Sasser, and Schlesinger The Service Profit Chain, 1997, New York: The Free Press, p 21


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Call to Action: Using Gap Analysis to Put Loyalty Index into Action

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:

Gap Analysis Loyalty

  1. 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.
  2. Quadrant 2: Areas with high correlations to loyalty and high performance.  These are service attributes to maintain.
  3. Quadrant 3: Areas with low correlations to loyalty and low performance.  These are service attributes to address if resources are available.
  4. 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:

Performance

Loyalty Correlation

Appearance/cleanliness of physical facilities

4.9

0.37

Appearance/cleanliness of personnel

4.8

0.42

Perform services as promised/right the first time

4.8

0.62

Perform services on time

4.9

0.54

Show interest in solving problems

4.9

0.61

Willingness to help/answer questions

4.7

0.55

Problems resolved quickly

4.4

0.56

Knowledgeable employees/job knowledge

4.6

0.41

Employees instill confidence in customer

4.7

0.52

Employee efficiency

4.7

0.58

Employee courtesy

4.9

0.56

Employee recommendations

4.8

0.53

Questioning to understand needs

4.9

0.45

Plotted on the above quadrant chart, they yield the following chart:

Gap Example

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.

Related Article: Using Promoter and Trust Measurements to Calculate a Customer Loyalty Index


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