Business Case and Implications for Consistency – Part 3: The Causal Chain from Consistency to Customer Loyalty
In an earlier post we discussed the business case for consistency, primarily because consistency drives customer loyalty. This post describes the causal chain from consistency to customer loyalty.
Brands are defined by how customers experience them, and they will have both an emotional and behavioral reaction to what they experience. It is these reactions to the customer experience which drive satisfaction, loyalty and profitability.
There is a causal chain from consistency to customer loyalty. McKinsey and Company concluded in their 2014 report, The Three Cs of Customer Satisfaction: Consistency, Consistency, Consistency, that feelings of trust are the strongest drivers of customer satisfaction and loyalty, and consistency is central to building customer trust.
For example, in our experience in the banking industry, institutions in the top quartile of consistent delivery are 30% more likely to be trusted by their customers compared to the bottom quartile. Furthermore, agreement with the statements: my bank is “a brand I feel close to” and “a brand that I can trust” are significant drivers of brand differentiation as a result of the customer experience. Again, brands are defined by how customers experience them. In today’s environment where consumer trust in financial institutions is extremely low, fostering trust is critical for driving customer loyalty. Consistency fosters trust. Trust drives loyalty.
Loyalty is the holy grail of managing the customer experience.
The foundation of customer loyalty is consistency. In a 2014 research paper entitled, The Three Cs of Customer Satisfaction: Consistency, Consistency, Consistency, McKinsey & Company concluded that trust, trust driven by consistent experiences, is the strongest drivers of customer loyalty and satisfaction.
Kinēsis, believes that each time a brand and a customer interact, the customer learns something about the brand, and they adjust their behavior based on what they learn. There is real power in understanding this proposition. In it is the power to influence the customer into profitable behaviors and away from unprofitable behaviors. One of these behaviors is repeat purchases or loyalty.
Customer loyalty takes time to build. Feelings of security and confidence in a brand are built up by consistent customer experiences over a sustained period of time. Across all industries, customers want a good, consistent experience with the products and services they use.
The value of customer loyalty is obvious. Kinēsis has found the concept of the “loyalty effect” to be an excellent framework for illustrating the value of loyalty. The loyalty effect is a proposition that states that customer profitability increases with customer tenure. Consider the following chart of customer profit contribution to customer tenure:
This curve of profit contribution per customer over time is called the loyalty curve. At customer acquisition, the profit contribution is initially negative as a result of the cost of customer acquisition. After acquisition, customer profit contribution increase with time as a result of revenue growth, cost savings, referrals and price premiums. Loyal customers and consistent customer experiences require less customer education, generate fewer complaints, reduce the number of phone calls, handle time and are more efficient across the board.
In the next post we will explore the causal chain from consistency to customer loyalty.
Humans value consistency – we are hard wired to do so – it’s in our DNA.
It is generally believed that modern humans originated on the Savanna Plain. Life was difficult for our distant forefathers. Sources of water, food, shelter were unreliable. Dangers existed at every turn. Evolving in this unreliable and hostile environment, evolutionary forces selected in modern humans a value for consistency – in effect hard wiring us to value consistency. We seek security in an insecure world.
In this context, it is not surprising we evolved to value consistency. While our modern world is a far more reliable environment, our brains are still hard wired to value consistency.
The implication for managers of the customer experience is obvious – customers want and value consistency in the customer experience. We’ve all felt it. When a car fails to start, when the power goes out, when software crashes we all feel uncomfortable. A lack of reliability and consistency creates confusion and frustration. We want to have confidence that reliable events like starting the car, turning on the lights or using software will work consistently. In the customer experience realm, we want to have confidence that the brands we have relationships with will deliver consistently on their brand promise each time without variation in quality.
Customers expect consistent delivery on the brand promise. They base their expectations on prior experience. Thus customers are in a self-reinforcing cycle where expectations are set based on prior experiences continually reinforcing the importance of consistency. This is the foundation of customer loyalty. We are creates of habit. The foundation of customer loyalty is built on the foundation of dependable, consistent, quality service delivery.
While we evolved in a difficult and unreliable environment, our modern society is much more reliable. Our modern society offers a much more consistent existent. Again, it’s a self-reinforcing cycle. Product quality and consistency of our mass production economy has reinforced our expectations of consistency.
Today’s information technology continues to reinforce our desire for consistency. However, it adds an additional element of customization. Henry Ford, the father of mass production, famously said of the Model-T, “You can have any color you want as long as it’s black.” Those days are gone. Today, we expect both consistency and customization.
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.
Mystery shopping not in pursuit of an overall customer experience objective may be interesting, it may be successful in motivating certain service behaviors, but ultimately will fail in maximizing return on investment.
Consider the following proposition:
“Every time a customer interacts with a brand, the customer learns something about the brand, and based on what they learn, adjust their behavior in either profitable or unprofitable ways.”
These behavioral adjustments could be profitable: positive word of mouth, complain less, less expensive channel use, increased wallet share, loyalty, or purchase intent, etc.. Or…these adjustments could be unprofitable: negative word of mouth, more complaints, decreased wallet share, purchase intent or loyalty, etc.
There is power in this proposition. Understanding it is the key to managing the customer experience in a profitable way. Unlocking this power gives managers a clear objective for the customer experience in terms of what you want the customer to learn from it and react to it. Ultimately, it becomes a guidepost for all aspects of customer experience management – including customer experience measurement.
In designing customer experience measurement tools, ask yourself:
- What is the overall objective of the customer experience?
- How do you want the customer to feel as a result of the experience?
- How do you want the customer to act as a result of the 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?
The answer to the above series of questions will become the guideposts for designing a customer experience which will achieve your objectives.
The answers to the above questions will serve as a basis for evaluating the customer experience against your objectives. In research terms, the answer to this question or questions will become the dependent variable(s) of your customer experience research – the variables influenced or dependent on the specific attributes of the customer experience.
For example, let’s assume your objective of the customer experience is increased return intent. As part of a mystery shopping program, ask a question designed to capture return intent – a question like, “Had this been an actual visit, how did the experience during this shop influence your intent to return for another transaction?” This is the dependent variable.
The next step is to determine the relationship between every service behavior or attribute and the dependent variable (return intent). The strength of this relationship is a measure of the importance of each behavior or attribute in terms of driving return intent. It provides a basis from which to make informed decisions as to which behaviors or attributes deserve more investment in terms of training, incentives, and rewards.
This is what Kinesis calls Key Driver Analysis, an analysis technique designed to identify service behaviors and attributes which are key drivers of your key objectives of the customer experience. In the end, providing an informed basis for which to make decisions about investments in the customer experience.
Net Promoter Score (NPS) burst on the customer experience scene 15 years ago in a Harvard Business Review article with the confident (some might say over confident) title “The One Number You Need to Grow.” NPS was introduced as the one survey question you need to ask in a customer survey.
Unfortunately, I’ve seen many customer experience managers include NPS in their mystery shopping programs, which is frankly a poor research practice.
The NPS methodology is relatively simple. Ask customers a “would recommend” question, “How likely are you to recommend us to a friend, relative or colleague?” on an 11-point scale from 0-10.
Next, segment respondents according to their responses to this would recommend question. Respondents who answered “9” or “10” are labeled “promoters”, those who answered “7” or “8” are identified as “passive referrers”, and finally, those who answered 0-6 are labeled “detractors”. Once this segmentation is complete, the Net Promoter Score (NPS) is calculated by subtracting the proportion of “detractors” from the proportion of “promoters.” This yields the net promoters, the proportion of promoters after the detractors have been subtracted out.
The theory behind NPS is simple. It is used as a proxy for customer loyalty. Loyalty is a behavior, surveys best measure attitudes, not behaviors. Therefore customer experience researchers need a proxy measurement for loyalty. NPS is considered an excellent proxy for loyalty under the theory that if one is likely to put their reputation at risk by referring a brand to others, they are more likely to be loyal to the brand. In contrast, to those who are not willing to put their reputation at risk are less likely to be loyal.
Fads in customer experience measurement come and go. The NPS fad has been particularly stubborn. Mostly because the theory behind it is intuitive, it is a solution to the problem of measuring loyalty within a survey, and it is simple. I personally think it was oversold as the “one number you need to grow.” Overselling it as the one number you need to grow doesn’t do justice to the complexities of managing the customer experience, nor does one NPS number give any direction in terms of how to improve your NPS score. An NPS score alone is just not very actionable.
While NPS is an excellent loyalty proxy and has a lot of utility is a customer experience survey, it is not an appropriate tool to use in a mystery shopping context. Mystery shopping is a snapshot of one experience in time, where a mystery shopper interacts with the representative of the brand. NPS is a measure of one’s likelihood to refer the brand to others. The problem is the likelihood to refer the brand to others is almost never the result of a snapshot in time. Rather, it is a holistic measure of the health of the entire relationship with the brand, and as such does not work well in a mystery shop context where the measurement is of a single interaction. As such, NPS is a measure of things unrelated to the specific experience measured in the mystery shop; things like: past-experiences, overall branding, alignment of the brand to customer expectations, etc.
Now, I understand the intent of inserting NPS in the mystery shop. It is to identify a dependent variable from which to evaluate the efficacy of the experience. NPS is just the wrong solution for this objective.
There is a better way.
Instead of blindly using NPS in the wrong research context, focus on your business objectives. Ask yourself:
- What are our business objectives with respect to the experience mystery shopped?
- What do we want to accomplish?
- How do we want the customer to feel as a result of the experience?
- What do we want the customer to do as a result of the experience shopped?
Once you have determined what business objectives you want to achieve as a result of the customer experience, design a specific question to measure the influence of the customer experience on this business objective.
For example, assume your objective of the customer experience is purchase intent. You want the customer to be more motivated to purchase after the experience than before. Ask a purchase intent question, designed to capture the shopper’s change in purchase intent as a result of the shop.
Now, you have a true dependent variable from which to evaluate the behaviors measured in the mystery shop. This is what we call Key Driver Analysis – identifying the behaviors which are key drivers of the desired business objective. In the example above we want to identify key drivers of purchase intent.
I like to think of different question types and analytical techniques as tools in a tool box. Each is important for its specific purpose, but few are universal tools which work in every context. NPS may be a useful tool for customer experience surveys. It is not, however, an appropriate tool for mystery shopping.
Mystery shop programs measure human interactions; interactions with other humans and increasingly human interactions with automated machines. Given that humans are on one or both sides of the equation, it is not surprising that variation in the customer experience exists.
When designing a mystery shop program, a central decision is the number of shops to deploy. This decision is dependent on a number of issues including: desired reliability, number of customer interactions, and the budgetary resources available for the program. However, one additional and very important consideration, which frankly doesn’t get much attention, is the amount of variation expected in the customer experience to be measured.
The level of variation in the customer experience is an important consideration. Consistent customer experience processes require less mystery shops than those with a high degree of variation. To illustrate this, consider the following:
Assume a customer experience process is 100% consistent with zero variation from experience to experience. Such a process would require only one shop to accurately describe the experience as a whole. Now, consider a customer experience process with an infinite level of variation in the experience. Such a process would require far more than one shop. In fact, assuming an infinite level of variation, 400 shops would be required to achieve a margin of error of plus or minus five percent.
Obviously, the variation of most customer experience processes reside somewhere between perfect consistency and infinite variation. So how do managers determine the level of variation in their process? The answer to this question will probably be more qualitative than quantitative. Ask yourself:
- Do you have a set of standardized customer experience expectations?
- Are these expectations clearly communicated to employees?
- Other than mystery shopping, do you have any processes in place to monitor the customer experience? If so, are the results of these monitoring tools consistent from month-to-month or quarter-to-quarter?
To make it easy, I always ask new clients to give a qualitative estimate of the level of variation in their customer experience from: high, medium to low. The answer to this question will also be considered along with the level of statistical reliability desired and budgetary resources available for the program in determining the appropriate number of shops.
So – ask yourself; how much variation can we expect in our customer experience?
Self-help resources typically take the form of a webpage housed on the mystery shop provider’s website or on an internal resource page. These resources provide a tutorial in the form of either a PowerPoint or video, reinforcing to stakeholders many of the subjects already discussed: definition of the brand, behavioral service expectations, and a copy of the questionnaire.
These self-help resources are also an excellent opportunity to introduce the mystery shop reports and how to read them (both on an individual shop basis and on an analytical level), and introduce concepts designed to identify the relative importance of specific sales and service behaviors which drive desired outcomes like purchase intent and customer loyalty.
Shop Results E-Mail
Upon distribution of the first shop, it is a best practice in launching a mystery shop program to send an e-mail to the supervisor of the employee shopped advising them of a completed shop, and containing either a PDF shop report or access to the shop via an online reporting tool.
The content of this e-mail should be dependent on the performance of the individuals shopped. If a shop is perfect, the e-mail should congratulate the employees on a perfect shop. If a shop is below expectations, it should inform the employees, in as positive way as possible, that their performance was below expectations and set the stage for coaching. It should remind employees that it is not the performance of this first shop that counts, but subsequent improvement as a result of the shops.
Depending on the timing of shop e-mails, some clients prefer the shop to be sent as soon as it clears the provider’s quality control process, while others prefer shops be held and released in mass at the end of a given shopping period (typically monthly). If the e-mail is sent at the end of a given period, this is an excellent opportunity to identify top performers who received perfect shops as a means of both recognizing superior performance, and motivating other employees to seek similar achievement.
Finally, this e-mail should reinforce superior shop performance by reminding front-line employees and managers of the rewards earned by successful shop performance.
This e-mail should be modified for all subsequent waves of shopping and be used as a cover letter for distribution of all future shops.
Additional e-mails may be sent to notify employees and their managers of specific events, such as: perfect shops, failed shops, shops within a specific score range, or shops which identify a specific behavior of an employee like a cross-sell effort.
Post Shop Call/ Presentation
Similar to the kickoff presentation, after the first wave of shopping, it is a best practice to conduct a post shop presentation, again by conference call or WebEx. The purpose of this presentation is to present the reports available, discuss how to read them, and – most importantly – take action on the results through coaching and interpreting call to action elements built into the program. Call to action elements designed to identify which behaviors are most important in terms of driving purchase intent or loyalty.
There should be no surprises in mystery shopping. A key to keeping all stakeholders informed of the mystery shop process is pre-shop communication.
The first communication tool is the kickoff letter. This letter is most often in the form of an e-mail. Sent prior to shopping, its purpose is to introduce employees to the program, explain its purpose in a positive way, make sure employees are aware of what is expected of them, and link shopping to their best interests, by reinforcing it is designed to make them more successful.
The kickoff e-mail should:
- Define the brand and emphasize that frontline employees are the personification of the brand. They are the physical embodiment of the brand.
- Explain that certain behaviors are expected from them in their role as the physical embodiment of the brand.
- List the specific sales and service behaviors that shoppers are asked to observe. Stress that management wants every representative to score well. Management has no interest in setting employees up for failure. If they perform these behaviors, they will receive a perfect shop score.
- Detail the incentive and reward structures in place as a result of the mystery shop program.
A presentation, conference call, or WebEx is an excellent tool to kick off a mystery shop program. All stakeholders in the process should understand their role and what is expected of them.
As with the kickoff letter or e-mail, the presentation should define the brand, stress that employees are the physical embodiment of the brand, and identify the specific sales and service behaviors expected from employees.
It should identify the internal administrator of the program, communicate the dispute process, discuss incentives and rewards earned through positive mystery shops, as well as introduce the concept of coaching as a result of the shop – making sure that managers and customer-facing personnel understand their role in the coaching process.
Finally, this presentation should introduce employees to self-help resources available for taking positive action as a result of the shop.
Research without a clear call-to-action may be interesting but not very useful. Best-in-class mystery shop programs build call-to-action elements into their design to ensure the results provide a clear direction.
Reading and interpreting research reports is a specialized skill. To maximize the value of the mystery shopping, employees should be given instruction into the basic skills of interpreting a research report. Some interpretation skills are easy, such as reading a data table which compares results across units of hierarchy (such as one store compared to another), or over specific time periods (such as one quarter to another). Others may be a little more complicated. such as a cross-tabulation of purchase intent which is a comparison of shops where the shopper reported positive purchase intent and those where the shopper reported negative purchase intent as a result of the shop. However reading them is the same as the comparisons of stores or quarters. All that is needed is just an understanding of how shops are grouped for the comparisons.
Beyond instructing the frontline how to read reports, they should also be given a primer on how to take action on the results.
The most common way managers of frontline employees take action on the results is coaching. Best-in-class mystery shop programs identify employees in need of coaching as a result of the shop, and managers should be instructed on how to use the results to coach employees.
For each such coaching opportunity, frontline managers should be given guidance on how to coach improvement, as well as online tools to log coaching, making both the employee and the manager accountable for coaching.
Additionally, managers should understand the analytical framework for maximizing the value of the program. Best-in-class mystery shop programs build in call-to-action components designed to identify key sales and service behaviors which correlate to a desired customer experience outcome. We call this Key Driver Analysis.
Key Driver Analysis identifies the relationship between specific sales and service behaviors and a desired outcome identifying which behaviors are key drivers of this desired outcome. For most brands, the desired outcomes are purchase intent or return or loyalty. Building these call-to-action elements into the program helps brands identify and motivate the sales and service behaviors with the most potential for return on investment in terms of driving loyalty or purchase intent.