This post considers two types of variation in the customer experience: common and special cause variation, and their implications for customer researchers.
The concepts of common and special cause variation are derived from the process management discipline Six Sigma.
Common cause variation is normal or random variation within the system. It is statistical noise within the system. Examples of common cause variation in the customer experience are:
- Poorly defined, poorly designed, inappropriate policies or procedures
- Poor design or maintenance of computer systems
- Inappropriate hiring practices
- Insufficient training
- Measurement error
Special cause variation, on the other hand, is not random. It conforms to laws of probability. It is the signal within the system. Examples of special cause variation include:
- High demand/ high traffic
- Poor adjustment of equipment
- Just having a bad day
What are the implications of common and special cause variation for customer experience researchers?
Given the differences between common cause and special cause variation, researchers need a tool to help them distinguish between the two. Researchers need a means of determining if any observed variation in the customer experience is statistical noise or a signal within the system. Control charts are a statistical tool to make a determination if variation is noise or a signal.
Control charts track measurements within upper and lower quality control limits. These quality control limits define statistically significant variation overtime (typically at a 95% confidence), which means there is a 95% probability that the variation is the result of an actual change in the customer experience (special cause variation) not just normal common cause variation. Observed variation within these quality control limits are common cause variation. Variation which migrates outside these quality control limits is special cause variation.
To illustrate this concept, consider the following example of mystery shop results:
This chart depicts a set of mystery shop scores which both vary from month to month and generally appear to trend upward.
Customer experience researchers need to provide managers a means of determining if the month to month variation is statistical noise or some meaningful signal within the system. Turning this chart into a control chart by adding statistically defined upper and lower quality control limits will determine if the monthly variation is common or special cause.
To define quality control limits, the customer experience researcher needs to determine the count of observations for each month, the monthly standard deviation, and the average count of shops across all months.
The following table adds these three additional pieces of information into our example:
|Count of Mystery Shops||Average Mystery Shop Scores||Standard Deviation of Mystery Shop Scores|
To define the upper and lower quality control limits (UCL and LCL, respectively), apply the following formula:
x = Grand Mean of the score
n = Mean sample size (number of shops)
SD = Mean standard deviation
These equations yield quality control limits at 95% confidence, which means there is a 95% probability any variation observed outside these limits is special cause variation, rather than normal common cause variation within the system
Calculating these quality control limits and applying them to the above chart produces the following control chart, with upper and lower quality control limits depicted in red:
This control chart now answers the question, what variation is common cause and what variation is special cause. The general trend upward appears to be statistically significant with the most recent month above the upper quality control limit. Additionally, this control chart identifies a period of special cause variation in July. With 95% confidence we know some special cause drove the scores below the lower control limit. Perhaps this special cause was employee turnover, perhaps a new system rollout, or perhaps a weather event that impacted the customer experience.
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.
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.
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).
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?”
- 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.
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.
Customer experience researchers are constantly looking for ways to make their observations relevant, to turn observations into insight. Observing a behavior or service attribute is one thing, linking observations to insight that will maximize return on customer experience investments is another. One way to link customer experience observations to insights that will drive ROI is to explore the influence of customer experience attributes to key business outcomes such as loyalty and wallet share.
The first step is to gather impressions of a broad array of customer experience attributes, such as: accuracy, cycle time, willingness to help, etc. Make this list as long as you reasonably can without making the survey instrument too long.
For additional thoughts on survey length and research design, see the following blog posts:
The next step is to explore the relationship of these service attributes to loyalty and share of wallet.
Two Questions – Lots of Insight
In our experience, two questions: a “would recommend” and primary provider question, yield valuable insight into the relative importance of specific service attributes. Together, these two questions form the foundation of a two-dimensional analytical framework to determine the relative importance of specific service attributes in driving loyalty and wallet share.
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.
Measuring customer loyalty in the context of a survey is difficult. Surveys best measure attitudes and perceptions. Loyalty is a behavior not an attitude. 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, or a new customer may be very satisfied and highly loyal.
Likelihood of referral captures a measurement of the customer’s likelihood to refer a brand to a friend, relative or colleague. It stands to reason, if one is going to refer others to a brand, they will remain loyal as well, because customers who are promoters of a brand 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.
Any likelihood of referral question can be used, depending on the specifics of your objectives. Kinesis has had success with both a “yes/no” question, “Would you refer us to a friend, relative or colleague?” and the Net Promoter methodology. The Net Promoter methodology asks for a rating of the likelihood of referral to a friend, relative or colleague on an 11-point (0-10) scale. Customers with a likelihood of 0-6 are labeled “detractors,” those with ratings of 7 and 8 and identified as “passive referrers,” while those who assign a rating of 9 and 10 are labeled “promoters.”
In our experience asking the “yes/no” question: “Would you refer us to a friend, relative or colleague?” produces starker differences in this two-dimensional analysis making it easier to identify which service attributes have a stronger relationship to both loyalty and engagement.
Similar to loyalty, customer engagement or wallet share can lead to some extraordinary financial results. Wallet share is the percentage of what a customer spends with a given brand over a specific period of time.
Also similar to loyalty, measuring engagement or wallet share in a survey is difficult. There are several ways to measure engagement: one methodology is to use some formula such as the Wallet Allocation Rule which uses customer responses to rank brands in the same product category and employs this rank to estimate wallet share, or to use a simple yes/no primary provider question.
Using these loyalty and engagement measures together, we can now cross tabulate the array of service attribute ratings by these two measures. This cross tabulation groups the responses into four segments: 1) Engaged & Loyal, 2) Disengaged yet Loyal, 3) Engaged yet Disloyal, 4) Disengaged & Disloyal. We can now make comparisons of the responses by these four segments to gain insight into how each of these four segments experience their relationship with the brand.
These four segments represent: the ideal, opportunity, recovery and attrition.
Ideal – Engaged Promoters: This is the ideal customer segment. These customers rely on the brand for the majority of their in category purchases and represent lower attrition risk. In short, they are perfectly positioned to provide the financial benefits of customer loyalty. Comparing attribute ratings for customers in this segment to the others will identify both areas of strength, but at the same time, identify attributes which are less important in terms of driving this ideal state, informing future decisions on investment in these attributes.
Opportunity – Disengaged Promoter: This customer segment represents an opportunity. These customers like the brand and are willing to put their reputation at risk for it. However, there is an opportunity for cross-sell to improve share of wallet. Comparing attribute ratings of the opportunity segment to the ideal will identify service attributes with the highest potential for ROI in terms of driving wallet share.
Recovery – Engaged Detractor: This segment represents significant risk. The combination of above average share of wallet, and low commitment to put their reputational risk on the line is flat out dangerous as it puts profitable share of wallet at risk. Comparing attribute ratings of customers in the recovery segment to both the ideal and the opportunity segments will identify the service attributes with the highest potential for ROI in terms of improving loyalty.
Attrition – Disengaged Detractor: This segment represents the greatest risk of attrition. With no willingness to put reputational risk on the line, and little commitment to placing share of wallet with the brand, retention strategies may be too late for them. Additionally, they most likely are unprofitable. Comparing the service attributes of customers in this segment to the others will identify elements of the customer experience which drive attrition and may warrant increased investment, as well as, elements that do not appear to matter very much in terms driving runoff, and may not warrant investment.
By making comparisons across each of these segments, researchers give managers a basis to make informed decisions about which service attributes have the strongest relationship to loyalty and engagement. Thus identifying which behaviors have the highest potential for ROI in terms of driving customer loyalty and engagement. This two-dimensional analysis is one way to turn customer experience observations into insight.
Best in class mystery shop programs provide managers a means of applying coaching, training, incentives, and other motivational tools directly on the sales and service behaviors that matter most in terms of driving the desired customer experience outcome. One tool to identify which sales and service behaviors are most important is Key Driver Analysis.
Key Driver Analysis determines the relationship between specific behaviors and a desired outcome. For most brands and industries, the desired outcomes are purchase intent or return intent (customer loyalty). This analytical tool helps mangers identify and reinforce sales and service behaviors which drive sales or loyalty – behaviors that matter.
As with all research, it is a best practice to anticipate the analysis when designing a mystery shop program. In anticipating the analytical needs of Key Driver Analysis identify what specific desired outcome you want from the customer as a result of the experience.
- Do you want the customer to purchase something?
- Do you want them return for another purchase?
The answer to these questions will anticipate the analysis and build in mechanisms for Key Driver Analysis to identify which behaviors are more important in driving this desired outcome – which behaviors matter most.
Next, ask shoppers if they had been an actual customer, how the experience influenced their return intent. Group shops by positive and negative return intent to identify how mystery shops with positive return intent differ from those with negative. This yields a ranking of the importance of each behavior by the strength of its relationship to return intent.
Additionally, pair the return intent rating with a follow-up question asking, why the shopper rated their return intent as they did. The responses to this question should be grouped and classified into similar themes, and grouped by the return intent rating described above. The result of this analysis produces a qualitative determination of what sales and service practices drive return intent.
Finally, Key Driver Analysis produces a means to identify which behaviors have the highest potential for return on investment in terms of driving return intent. This is achieved by comparing the importance of each behavior (as defined above) and its performance (the frequency in which it is observed). Mapping this comparison in a quadrant chart, provides a means for identifying behaviors with relatively high importance and low performance – behaviors which will yield the highest potential for return on investment in terms of driving return intent.
Behaviors with the highest potential for return on investment can then be inserted into a feedback loop into the mystery shop scoring methodology by informing decisions with respect to weighting specific mystery shop questions, assigning more weight to behaviors with the highest potential for return on investment.
Employing Key Driver Analysis gives managers a means of focusing training, coaching, incentives, and other motivational tools directly on the sales and service behaviors that will produce the largest return on investment. See the attached post for further discussion of mystery shop scoring.
Establishing and measuring loyalty proxies is important, but your brand perception research should not end there. Brand perception research should produce insight beyond loyalty. It should determine the extent to which customers impressions of the brand are aligned with your desired brand image. Additionally, perceptions of the brand among the most loyal and engaged customers should be compared to those who are deemed less loyal or engaged to identify opportunities to improve perceptions of the brand among customers at either risk of defection, or not fully engaged
In a subsequent post, we will address ways to measure engagement/wallet share.
The first step in measuring your brand perception is to define your desired brand. Ask yourself: if your brand were a person, what personality characteristics would you like your customers to describe you with? What adjectives would you want used to describe your brand?
In addition to describing your brand personality with adjectives, come up with a list of statements that describe your desired personality. For example, you may include statements such as:
- We are easy to do business with.
- We are knowledgeable.
- We are like a trusted friend.
- We are interested in customers as people, not just the bottom line.
- We are committed to the community.
So, we defined the brand in terms of personality adjectives and statements. Both will be used in designing the survey instrument.
The Survey Instrument
Unaided Top-of Mind
The first step in the survey instrument, is asking customers for their unaided top-of-mind perceptions of the brand. This will uncover the first thing that comes to customers’ minds about your brand prior to the effects of any bias introduced by the research instrument itself. There are many ways to capture unaided top-of-mind impressions. We like a simple approach, where you ask the customer for the one word that they would use to describe the company. This research question will yield a list adjectives that can be quantified by frequency and used to determine the extent to which customers top-of-mind impressions match the desired brand image.
After we have defined top of mind impressions of the brand, we recommend comparing brand perception to your desired brand identified in the brand definition exercise described above. This is a fairly simple process of presenting the customers with your list of brand personality adjectives and asking the customer which of these adjectives would the customer use to describe the company.
The next step in comparing the reality of brand perception to your branding goals is to ask the customers to what extent do they agree with each of the brand personality statements described above. As with the list of adjectives, this holds a mirror up to your desired image and measures the extent to which customers agree that you are perceived in the manner that you want to be.
Identifying Attributes with the Most ROI Potential
The value of these brand perception statements goes beyond just evaluating if you live up to your brand. Used in conjunction with the loyalty proxies discussed in the previous post, they become tools to determine which of these brand personality attributes will yield the most ROI in terms of improving customer loyalty. This is achieved with a simple cross-tabulation of agreement with these statements by customer loyalty segment. For example, if NPS is used as the loyalty proxy, then we simply compare agreement to these statements from promoters to detractors to determine which attributes have the largest gaps between promoters and detractors. Those with the largest gaps have the most ROI potential in terms of customer loyalty.
Customer loyalty is the business attribute with the strongest correlation to profitability. Loyalty lowers sales and acquisition costs per customer by amortizing these costs across a longer lifecycle, leading to extraordinary financial results. A 5% increase in customer loyalty can translate, depending on the industry, into a 25% to 85% increase in profits.
Many customer experience managers want to include a measure of loyalty in their customer experience research. Indeed loyalty and how brand perception drives loyalty is the foundation of any brand perception research. However, loyalty is a behavior measured longitudinally over time, and surveys best measure customer attitudes. As a result, researchers typically use attitudinal proxies for customer loyalty. Generally the two most common proxies are either a “would recommend” or a “customer advocacy” question.
- Would Recommend: A “would recommend” question is typically Net Promoter (NPS) or some other measure of the customer’s likelihood of referring to a friend, relative or colleague. It stands to reason, if one is going to refer others to a brand, they will remain loyal as well. Promoters’ willingness to put their reputational risk on the line is founded on a feeling of loyalty and trust.
- Customer Advocacy: A customer advocacy question asks if the customer agrees with the following statement, “the brand cares about me, not just the bottom line.” The concept of trust is perhaps more evident in customer advocacy. Customers who agree with this statement trust the brand to do right by them, and not subjugate their best interests to profits. Customers who trust the brand to do the right thing are more likely to remain loyal.
We’ve seen some loyalty surveys (particular those employing the NPS methodology), which only ask the loyalty proxy with little or no other areas of investigation. We believe this is a bad practice for a number of reasons:
- Customer Experience: Customers who have affirmatively taken the action of clicking on the survey want to give you their opinion (they want to participate in the survey), and based on their experience are expecting a multiple question survey. Presenting them with just one rating scale risks alienating them as they may feel they didn’t get an appropriate opportunity to share their opinion, and ultimately feel it was not worth their time to participate. Secondly, some customers may conclude the survey system is broken in some way as it only presented them with one question, resulting in customer confusion.
- Actionable Research Results: A survey consisting of one NPS rating is not going to yield any information from which to draw conclusions about how customers feel about the brand. It will produce an average rating and frequency of promoters and detractors, but no context in which to interpret the results.
Establishing and measuring loyalty proxies are an important first step in evaluating brand perception. Additional areas of investigation should include indentifying and comparing customer impressions of the brand to your desired brand personality, and evaluate customer engagement or wallet share.