Inconsistent treatment based on certain demographic characteristics is illegal. The Civil Rights Act of 1964 prohibits discrimination in almost all privately owned service industries based on race, color, religion, gender, or national origin. Other industries, such as retail banking, have additional regulatory requirements.
Beyond this legal risk, managers must be aware of the significant risk to the reputation of the brand posed by discriminatory practices.
Managers may seek comfort in the knowledge that their company’s policies and procedures are not to refuse service to anyone. However, this overt discrimination is just a small part of the risk associated with discrimination. Beyond overt discrimination, which is extremely rare, there are two other categories of discriminatory practices: disparate impact and disparate treatment.
Disparate impact is the result of policies or business practices which have an unequal impact. A restaurant with a policy to require prepayment for meals from one demographic group and not another is an example of disparate impact.
Disparate treatment is differences in treatment that originate at the customer-employee interface. Disparate treatment does not necessarily need to be a conscious act. It can be an unconscious pattern or practice of different treatment that the employee is not even aware of. The use of name, offering promotional material to a customer of one group as opposed to a customer on another group are all examples of disparate treatment.
Now, observing differences is treatment is not necessarily proof of discrimination. Human behavior, after all, is variable. There is a certain amount of normal variation in all service encounters. The trick is to determine if disparate treatment observed represents a pattern or practice of discrimination. Fortunately statistics has the answer, we use statistical tests of significance to determine both if observed differences in treatment are the result of actual discriminatory practices and the likelihood that any one member of a protected class will be treated differently than a member of another protected class. It should be noted, however, that regulatory agencies set the bar much higher. Many do not necessarily rely on statistical testing. In their view, any single case of disparate treatment is evidence of discrimination.
In a future post we will discuss the implications for customer experience researchers in testing for disparate treatment.
Inconsistent customer experiences are a significant threat to customer loyalty. In a previous post, we observed the casual relationship between consistency in the customer experience and feelings of trust and loyalty.
Consistency drives satisfaction. It is extremely common to see a correlation between intra-channel consistency and performance. Consider the following scatter plot from Kinesis’ research, which plots bank branch customer satisfaction by the variation in branch customer satisfaction:
As this plot demonstrates, consistency correlates with quality. Branches with higher customer satisfaction ratings are also the most consistent. In our customer experience research proactive we see this time and time again.
Additionally, this plot also demonstrates that top-line averages of customer satisfaction can be misleading. The bank in this plot had an average customer satisfaction rating of 93%. However, many branches fall well below this top-line average, resulting in an incomplete picture of the customer experience. Customers do not experience top-line averages; they experience the customer experience one interaction at a time at the local business unit level.
What are the implications for managers of the customer experience?
The first implication for managers is the above observation that top-line averages can mislead. Top-line averages hide individual business units with both low and inconsistent customer satisfaction. Top-line averages come between management and customers, distancing managers from how customers actually experience the brand.
Secondly, variation must be managed at the cause. Intra-channel variation is almost always at the local business unit level. For example, a store with a high degree of variation in customer traffic will experience a high degree of variation in the customer experience if management does not mitigate the effects of the variation in traffic.
How to manage for consistency:
- Manage inconsistency at the cause
- Write a clear mission statement
- Use appropriate analytics
- Don’t silo analytics by channel
- Meet regularly with employees to share problems and potential solutions
- Focus on customer journey
Intra-channel consistency needs to be managed at the local level – individual stores and agents. Tools need to be available deep into the organization to allow managers at the lowest level of each channel to deliver a consistent experience.
The modern customer experience environment is constituted of an ever expanding variety of delivery channels, with no evidence of the slowing of the pace of channel expansion. As channel expansion continues, customer empowerment is increasing with customer choice. Customer relationships with brands are not derived from individuals’ discrete interactions. Rather, customer relationships are defined by clusters of interactions, clusters of interactions across the entire life cycle of the relationships, and across all channels. Inter-channel consistency defines the customer relationship.
McKinsey and Company concluded in their 2014 report, The Three Cs of Customer Satisfaction: Consistency, Consistency, Consistency, demonstrated, in a retail banking context, a link between cross-channel consistency and bank performance.
In customers’ minds, all channels belong to the same brand. Customers do not consider management silos or organizational charts – to them all channels are the same. Customers expect consistent experiences regardless of channel. In their minds, an agent at a call center should have the same information and training as in-person agents.
What are the implications for managers of the customer experience?
The primary management issue in aligning disparate channels is to manage inconsistency at its cause. The most common cause of inconsistencies across channels is the result of siloed management, where managers’ jurisdiction is limited to their channel. Inter-channel consistency is increasingly important as advances in technology expand customer choice. Brands need to serve customers in the channel of their choice. Therefore, the cause of inter-channel inconsistency must be managed higher up in the organization at the lowest level where lines of authority across channels converge, or through some kind of cross-functional authority.
The implications for management are not limited to senior management and cross-functional teams. Customer experience managers should be aware that top-line averages can mislead. Improvement opportunities are rarely found in top-line averages, but at the local level. Again, the key is to manage inconsistency at the cause. Inconsistency at the local level almost always has a local cause; as a result, variability in performance must be managed at the local level as well.
Business Case and Implications for Consistency – Part 4 – Consistency and the Outsized Influence of Poor Experiences
This post continues to explore the business case for consistency by considering the influence of poor experiences.
To start, let’s consider the following case study:
Assume a brand’s typical customer has 5 service interactions per year. Also assume, the brand has a relatively strong 95% satisfaction rate. Given these assumptions, the typical customer has a 25% probability each year of having a negative experience, and in four years, in theory, every customer will have a negative experience.
As this case study illustrates, customer relationships with brands are not defined by individual, discrete customer experiences but by clusters of interactions across the lifecycle of the customer relationship. The influence of individual experiences is far less important than the cumulative effect of these clusters of customer experiences.
Consistency reduces the likelihood of negative experiences contaminating the clusters of experiences which make up the whole of the customer relationship. Negative experiences, regardless of how infrequent, have a particularly caustic effect on the customer relationship. A variety of research, including McKiney’s The Three Cs of Customer Satisfaction: Consistency, Consistency, Consistency, has concluded that negative experiences have three to four times the influence on the customer as positive experiences – three to four times the influence on the customer’s emotional reaction to the brand – three to four times the influence on loyalty, purchase intent and social sharing within their network.
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.
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.
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.
These days, post-transaction surveys are ubiquitous. Brands large and small take advantage of internet-based survey technology to evaluate the customer experience at almost every touch point. Similarly, loyalty proxy methodologies such as Net Promoter (NPS) are very much in vogue. However, many NPS surveys are fielded in a post-transaction context (potentially exposing the research to sampling bias as a result of only hearing from customers who have recently conducted a transaction), and are not designed in a manner that will give managers appropriate information upon which to take action on the research.
At their core, loyalty proxies are brand perception research – not transactional. We believe it is a best practice to define the sample frame as the entire customer base, as opposed to customers who have recently interacted with the brand. Ultimately, these surveys are image and perception research of the brand across the entire customer base.
Happily, this perception research offers an excellent opportunity to gather customer perceptions of the brand, compare them to your desired brand image, as well as measure engagement or wallet share. An excellent survey instrument to accomplish this is a survey divided into three parts:
- Loyalty Proxy: Consisting of the NPS rating or some other appropriate measure and 1 or 2 follow up questions to explore why the customer gave the NPS rating they did.
- Image perception: consisting of 3 or 4 questions to determine how customers perceive the brand.
- Engagement/Wallet Share: consisting of 3 or 4 questions to determine if the customer considers the brand their primary provider, and to gauge share of wallet of various financial products & services across the brand and its competitors.
This research plan will not only yield an NPS, but it will provide insight into why the customers assigned the NPS they did, evaluate the extent to which the entire customer base’s impressions of the brand matches your desired brand image, as well as identify how the brand is perceived by promoters and detractors. This plan will also yield valuable insight into share of wallet, and how wallet share differs for promoters and detractors.
Such a survey need not be long, the above objectives can be accomplished with 10 – 12 questions and will probably take less than 5 minutes for the customer to complete.
In a subsequent posts, we will explore each of these 3-parts of the survey in more detail: