Business Case and Implications for
Consistency in the Customer Experience
In my youth McDonalds used to tout the number of hamburgers served on their signs, just under those iconoclastic Golden Arches. On family vacations passing by these Golden Arches, I’d observe those signs and contemplate the size of that number - hundreds of millions. McDonalds stopped counting at a mind boggling 100 billion hamburgers in 1994.
Think about that that… 100 billion hamburgers of… what one really must admit, are of marginal quality.
How did McDonalds sell so many of them? - Consistency.
McDonalds’ hamburgers are extremely consistent, from location to location, through the years, no matter where we went on our family vacations, we could count on being served a nearly identical hamburger each and every time.
Why We Value Consistency
Modern humans appeared on the Savanna Plain about 200,000 years ago. Life on the Savanna for our ancestors was difficult. Resources such as food, water, shelter were unreliable. Evolution in this unreliable environment produced us modern humans, which in many ways are hard wired to value consistency - seeking security in an insecure world. It is not surprising, therefore, consistency gives us a sense of confidence and security. Even though our modern world offers a far more reliable environment, our brains are still hard wired to value consistency.
The lesson for customer experience managers is obvious - customers value consistency. Consistency fosters a feeling of dependability and trust. Customers want to have confidence that a brand will deliver its brand promise, consistently each time, with little or no variation in quality. We are creatures of habit. Customer loyalty is built on the foundation of dependable, consistent, quality service delivery.
Customers base their expectations on prior experience. The importance of consistency is reinforced by this. Repeat purchases are based on past experiences.
In modern society, mass production not only revolutionized the cost and manner of production, it ushered in an entirely new consumer experience, where product quality was much more consistent than previously available, resulting in consumers’ increased expectations of consistency. Today’s technology not only continues to reinforce our desire for consistency – but layers in an added dimension of customization.
Business Case for Consistency
Consistency is the foundation of customer loyalty. In a bit of research by McKinsey & Company in 2014, the researchers concluded that across the majority of industries surveyed, consistency drives feelings of trust, and trust is the strongest driver of satisfaction and loyalty. 1
My company, Kinēsis, is founded on the proposition that each time a brand and a customer interact, the customer learns something in the process and adjusts their behavior based on what they learn. One of these potential behaviors is repeat purchases or loyalty.
Building loyalty takes time. Consumer confidence and the resulting positive feelings of security are built up with consistent delivery over time. Consumer relationships are built on a solid foundation of trust. Regardless of industry, customers want quality products and services; they want to know that what they experience now, can be counted on in future experiences.
The benefits of customer loyalty are best described by the loyalty effect, which is founded on the proposition that the longer the customer tenure, the more profitable they become. This loyalty effect is best illustrated by the loyalty curve, which depicts profit contribution per customer over time:
At the beginning of the relationship, acquisition cost drives down customer profitability, after initial acquisition customer profitability tends to grow over time as a result of revenue growth, cost savings, referrals and price premiums.
Not only is consistency an important driver of loyalty. Process quality and consistency is also an important driver of reduced costs and efficiency. Consistent customer experiences are less expensive to deliver than inconsistent experiences. 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.
Brands are not defined by taglines and pictures. They are defined by how customers experience them.
Again, research has concluded that across the majority of industries surveyed, feelings of trust are the strongest drivers of satisfaction and loyalty. The same research concluded that consistency is particularly important in fostering trust with customers.2
For example, in the banking industry, institutions in the top quartile of consistency were 30% more likely to be trusted by their customers compared to those in the bottom quartile of consistency. Consistency drives an emotional connection to the brand. Again, in the banking industry, agreement with the statement that my bank is “a brand I feel close to” and “a brand that I can trust” are top drivers of customer experience-based brand differentiation. In an environment where trust in financial brands is very low, building trust is important for customer loyalty and growth.
Brands are not only defined by discrete interactions, but by clusters of interactions which make the individual impact of discrete interactions less important than the cumulative effect of clusters of interactions. To illustrate this, consider that a variety of research in the past (including McKinsey quoted above) has consistently concluded that negative customer experiences are three to four times more powerful on the customer’s relationship with the brand compared to positive experiences.
Now, consider the following example:
Assume on average a customer has five annual service interactions with a brand. Additionally, assume the brand has a respectable 95% satisfaction rate. Under these assumptions, any given customer has a 25% probability of a negative experience; in theory the entire customer base will have a negative experience in four years!
Inter-Channel (Cross-Channel) Consistency
The number of service channels across all industries has expanded considerably over the past few decades, and there is no evidence the pace of channel expansion will decrease. Reflecting this channel expansion, customer empowerment is increasing with customer choice.
Inter-Channel consistency, consistency across channels, has become more and more powerful as delivery channels and customer choice expand. Again, customer relationships with brands are comprised of clusters of interactions which have a cumulative effect that is far more powerful than the individual interactions. These clusters of interactions are not confined to individual channels. The same McKinsey study quoted earlier, concluded lower performing banks had less cross-channel consistency between the branch network and the contact centers.
Customers make no distinction between channels. In their minds, all the channels are the same entity. They don’t care about organization charts or silos. They expect a consistent customer experience across all channels. They expect the contact center agent to have the same ability to answer questions as the in-branch personnel.
For sales and service organizations, a high degree of variation in the customer experience represents a significant threat to customer loyalty. As previously observed, consistent positive customer experiences generate a feeling of trust.
While the causes of cross-channel variation often reside higher up in the organizational chart, the causes of variation within the same channel, often are at the local level. A store, for example, with large variation in customer traffic, which increases the variation of its customer experience, is an example of a local cause.
It is common to see a correlation between intra-channel consistency and performance. In a Gallup study, a study of a retail chain of 1,100 stores revealed the best performing stores were 3.5 stronger than the lowest performing stores. 3
This example illustrates a couple of important intra-channel variation concepts. First, consistency equals quality. The more consistent a branch’s customer satisfaction, the higher the branch’s customer satisfaction. It is extremely rare to see a business unit with consistently poor customer satisfaction. On the contrary, it is much more common to see inconsistency correlated with poor performance. Second, measuring average satisfaction within a channel can be very misleading. The bank in the above example had 93% customer satisfaction with branch transactions. However, this top-line measure of satisfaction can be very misleading, hiding branches with both inconsistent and low satisfaction. Customers experience variation at the local level, not top-line averages. Top-line averages distance managers from the actual customer experience.
The causes of intra-channel variation in contact centers can also be found at the local level – in the case, the agent level. While technology and processes provide a foundation for consistency, at the agent level performance varies from agent to agent.
Another important aspect of consistency is equal treatment of customers based on their demographic profile. The Civil Rights Act of 1964 prohibits discrimination by privately owned places of public accommodation on the basis of race, color, religion or national origin. The term public accommodation is broadly interpreted to include almost all service industries (restaurants, theaters, stores, etc.). Beyond the legal risk, there is significant PR risk. It seems almost weekly major brands suffer the legal and public relations nightmare of accusations of discrimination. Beyond the branding and public relations implications, allegations of discrimination can produce huge customer loyalty and attrition risk.
Discriminatory practices generally fall into three categories: overt discrimination, disparate impact, and disparate treatment. Overt discrimination is very rare. Slightly more common is disparate impact, which is the result of policies or business practices which have an unequal impact. Disparate treatment is the most common type of discrimination and represents the largest threat to the brand. Disparate treatment occurs at the employee level, where an employee consciously or unconsciously treats customers differently based on their demographic class. Shaking a hand or offering promotional material to one group of customers, and not the other would be an example of disparate treatment.
Observed differences in treatment is not necessarily evidence of discrimination. Human behavior is variable, and observed differences in treatment may just be random and not represent a pattern or practice of discrimination. Kinēsis tests for disparate treatment using statistical tests of significance to determine if any observed differences in treatment are random or reflect a pattern or practice.
Customer Experience Managers
Consistency is difficult to get right and requires the attention of senior leadership. The quality control researcher W. Edwards Deming argued inconsistency is prima facie evidence of ineffective management. Inconsistency can be very expensive both in terms of revenue (lost revenue to customer attrition) and in terms of increased operating costs. It’s management’s responsibility to all the stakeholders in the organization to monitor consistency.
Manage Inconsistency at the Cause
Inconsistency needs to be managed at its cause. However, different types of inconsistency have different causes.
Inter-channel consistency requires the attention of senior leadership. The most common cause of inter-channel variation is the effect of siloing customer experience management within each channel. As a result, the cross-channel experience needs to be managed higher up in the organizational chart where lines of authority converge, bringing disparate channels together, or through cross-functional teams charged with uniting the customer experience through all channels. Either approach requires the sponsorship of senior leadership.
Opportunities for improvement are always found not in top-line averages but at the local level. Inconsistency at the local level almost always has local causes. As a result, the best way to reduce variability and improve performance at the local level is to focus on the local level where variability originates.
Inter-channel consistency often finds its causes higher up in the organizational chart, where lines of authority converge bringing different channels together.
Intra-channel consistency needs to be managed at a more 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.
Write a Clear Mission Statement
Customer experience objectives need to be codified in a clear mission statement. This mission statement should clearly communicate how management wants customers to experience the brand, and how you want them to feel as a result of the experience – how you want customers to emotionally connect with the brand. This emotional connection is critical to experience the benefits of customer loyalty.
Align Cross-Channel Behaviors
The first step in aligning behaviors across channels it to define the elements of the experience for the entire organization. Start with the mission statement. Next, define the customer experience in terms of dimensions or attributes which make up the desired experience.
For example, a financial institution may decide they what their customer experience to be comprised of four dimensions:
- Relationship building
- Sales process
- Product knowledge
- Customer knowledge
Now, define these dimensions in terms of specific attributes which support the dimension.
Keeping with the above example, a financial institution may define each dimension in terms of the following set of attributes.
Commitment to customer needs
Perceived as trusted advisor
Referral to appropriate partner
Understanding of a range of products
Understand features and benefits
Explain benefits in ways that are meaningful to customers
Once each dimension is defined in terms of specific attributes, the next step is to identify specific behaviors for each channel that support each attribute, and map these behaviors across channels. Again, staying with the above example, the financial institution may decide that establishing trust is made up of a set of five behaviors mapped across each channel.
|Maintain eye contact||Maintain eye contact|
|Speak clearly||Speak clearly||Speak clearly|
|Maintain smile||Maintain smile||Sound as if they were smiling through the phone|
|Thank for business||Thank for business||Thank for business|
|Ask “What else may we assist you with today?”||Ask “What else may we assist you with today?”||Ask “What else may we assist you with today?”|
|Encourage future business||Encourage future business||Encourage future business|
Note this behavioral map assigns behaviors based on their appropriateness to each channel. So, for example, while the in-personal channel may be expected to maintain eye contact, obviously that would not apply for the contact center. Or the in-person channel may be expected to maintain a smile, while for the contact center this behavior may be modified for the phone channel to sounding as if they are smiling through the phone.
Meet Regularly with Employees
Relationships factor greatly into the consistency equation. The customer experience and relationship with the brand is only as strong as the weakest link. Review your core values and reinforce them regularly to ensure consistent treatment of customers aligned with the mission statement.
Focus on Customer Journey
Consistency is not just about products or services, but the overall customer journey. Customers expect the same level of quality at every step of the customer journey. The customer journey is the sum total of experiences when interacting with the brand.
Consistency in customer journeys is an important predictor of both the overall customer experience and customer loyalty. Research suggests, paying attention to customer journeys increases customer satisfaction by 20% and revenues by more than 15%, all the while lower costs of customer service by 20%!4
Customer journeys by their nature are nearly infinite. Managers should, therefore, focus on the low hanging fruit, the 3 – 5 most common journeys, or journeys which management believes will yield the most return on investment.
Given customer journeys cross touch points, brands need to reorganize from silos and create teams that are responsible for the end-to-end journey across touch points; including, redesigning customer experience measurement to capture journeys across all touch points.
The Balancing Act
Managing the customer experience toward consistently delivering quality is a balancing act between process focusing on consistency of execution and individual autonomy. Where the fulcrum is placed in this balancing act depends on the industry. A fast food franchise typically requires more process than an investment advisor – however, both these examples require a balancing act between consistency of execution and autonomy.
Southwest Airlines is an excellent example of this two pronged approach. Airlines require a great deal of process, however, Southwest employees have the ability to individualize the experience often in fun ways.
Process consistency delivers a consistent experience through a tightly defined set of policies, procedures and check lists designed to anticipate nearly every contingency. Automated systems are an example of execution consistency. Human initiative, talent and experience takes a secondary role under these process oriented systems. Process is a foundation to make the experience more consistent, but without the balancing act, its value is limited. It will help poor performers improve to average, but it will not help good performers excel.
The other side of the balancing act emphasizes the human initiative, talent and experience of the employee. This side of the balancing act builds the emotional connection with the customer, resulting in strong healthy relationships. It selects employees based on talent and abilities to engage customers, and empowers them to solve problems.
The thread that runs through all these quality and consistency issues is the concept of alignment. Customer expectations must be aligned with the customer experience. Aligning the experience to customer expectations allows brands to promote and reinforce measureable changes in customer behaviors, leading to greater retention, increased revenue and lower transaction costs. The concept of "continual alignment" is critical to a customer experience strategy. A truly comprehensive customer experience strategy deals with many interlocking points of alignment.
Among the most important are:
- Company message with customer expectations
- Customer expectations with company standards
- Company standards with training content
- Training content with frontline execution
- Frontline execution with rewards and incentives
Common Cause vs. Special Cause Variation
It is incumbent on managers to understand the causes of variation in the customer experience. The process management discipline Six Sigma groups causes of variation into common cause variation and special cause variation.
Common cause variation is natural variation within any system; it is random, much like the role of dice. It is any variation constantly active within a system, and represents 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
Unlike the roll of the dice, special cause variation is not predictable by laws of probability. As a result, it does not represent statistical “noise” but 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
Prioritize Areas for Attention
All customer experience improvement initiatives must prioritize areas for attention. Remember, negative experiences are 4 to 5 times more impactful than positive experiences, and inconsistency erodes trust and loyalty. Process improvement should, therefore, focus on improving negative experiences.
Identify the most common areas for attention. Voice of the Customer (VOC) tables (which will be discussed later) are an excellent tool to identify areas for attention.
Not all points along the customer journey are equal. Most customers travel through the customer journey in what can be called a state of inertia until they reach critical points in the journey – points which influence their perception of the brand in significant ways. These points in the customer journey are “moments of truth” where customers form or change their opinion of the provider, either positively or negatively, based on their experience. Moments of truth can be quite varied and occur in a skilled sales presentation, when a shop owner stays open late to help a dad buy the perfect gift, or when a hold time is particularly long. Critical Incident Technique (CIT), which will be discussed later, is an excellent tool to identify common moments of truth.
Management should provide a mechanism for customer experience intelligence from frontline employees. Meetings, focus groups, and surveys help managers understand what is going on at the customer-employee interface by leveraging employees as a valuable resource of customer experience information – serving as a means to identify areas to prioritize.
Beyond listening to front-line employees, train them to identify and address common customer issues, and script guidelines toward not only resolving the customer experience issue, but also fostering customer trust – bonding the customer to the brand.
Customer Experience Researchers
As a lifelong market researcher, it is difficult sometimes to admit that statistics can be misleading. This is often the case with top-line average satisfaction measures. Averages can mislead, giving a false sense of security. Customers don’t experience averages, they experience specific interactions and customer journeys with all the variation they entail.
Again, consider a previously mentioned example: assume a brand with a 95% satisfaction rate, with an average of five annual service interactions per customer. A 95% satisfaction rate as a top-line average appears strong, however this variation in satisfaction means any given customer has a 25% probability of a negative experience in a year. In four years, the entire customer base, in theory, will have a negative experience. Again, customers do not experience averages.
To understand the entire customer experience in its totality, researchers must drill deeper into the organization, and investigate variability in the customer experience at its source.
Before we get into specific research methodologies, let’s review the types of variation and their customer experience research implications.
Inter-Channel Sources of Variation
Cross-channel consistency has become more and more important as customer choice expands. Brands must be prepared to meet customers in the channel of their choice, be it in-person, a contact center, online or mobile.
The problem, however, is each different channel requires specialized systems and processes which can cause each channel to be siloed operating independently from each other.
Customers, however, don’t care about organization charts or silos. They draw no distinction between channels - they are the same entity. Customers expect a consistent customer experience across all channels. They expect information available online to be consistent with information received from a contact center agent or in-person at a store.
Inter-channel consistency often finds its causes higher up in the organizational chart where lines of authority converge bringing different channels together.
Intra-Channel Sources of Variation
The causes of variation within the same channel often are at the local level. As we previously observed, a store with large variation in traffic volume is likely to experience customer experience variation.
It is tempting to explain variability at the local level based on factors that cannot be managed – location, size, demographic, etc., but research reveals that even after the effects of these variables have been controlled, inconsistency persists.
At the local level, in spite of technology, tools, processes, and systems variation at the local level – it’s the people. The employees are the predominate cause of variation.
For in-person stores, a Gallup study, a study of a retail chain of 1,100 stores revealed the best performing stores were 3.5 stronger than the lowest performing stores.5
In a study conducted by Kinēsis, mystery shopping bank branches of six national institutions, we determined that the top quartile of branches with consistent mystery shop scores have 15% higher satisfaction with the overall experience and 20% higher purchase intent (both measured on a 5-point scale).
For contact centers, research by Gallup has determined the top 10% of agents have a ratio of positive to negative experiences of 6:1. The bottom 10% of agents have a positive to negative experience ratio of 3:4 – the majority of experiences are negative.
Now, let’s drill into research tools to monitor variation in the customer journey.
A certain amount of variation in customer experience measurement is normal. As discussed earlier, variation comes in two forms: common and special cause variation. Common cause variation is normal random measurement variation. It is the noise within the system. Special cause variation is not random variation. It is the signal within the system.
Customer experience researchers need a means of distinguishing common cause from special cause variation, to determine if any observed variation in the customer experience is the result of actual changes to the customer experience or simply normal variation in measurement. Control charts offer a solution to this need.
Control charts are a statistical tool which tracks measurements within upper and lower quality control limits. Variation within these limits is not statistically significant and may be either common or special cause variation. Variation outside of these quality control limits is statistically significant and represents actual variation in the customer experience, rather than random measurement effects.
To illustrate this concept, consider the following example of mystery shop results:
The general trend of these measurements is increasing, yet from month to month there is a bit of variation.
Customer experience managers need a means of determining if July was a particularly bad month, or if the performance in October and November represents an actual improvement in the customer experience. Defining upper and lower quality control limits will provide answers to these questions.
To define the quality control limits, in addition to the average observations, the customer experience researcher needs to know the count of observations and their standard deviation from month to month.
The following table adds these two 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:
This equation produces quality control limits with a 95% confidence; meaning these is at least a 95% probability that measurements outside of these limits represent special cause variation.
Applying these equations to the data in the above table, produces the following control chart, where the upper and lower quality control limits are depicted in red.
Now we know, not only are mystery shop scores trending upward, but the most recent measurement in November has improved beyond the upper control limit. Furthermore, something happened in July to drive the mystery shop scores below the lower control limit. Some special cause variation is at work here; perhaps employee turnover caused the decrease, or something external such as a weather event was the cause, but we know with a 95% confidence the variation of both July and November is not normal or random variation.
Again, customers experience clusters of interactions over the life of the relationship, not just discrete service interactions. Customer experience researchers should therefore, redesign customer experience measurement to measure journeys, not touch points. McKinsey & Company have determined measuring satisfaction with the customer journey is 30% more predictive of overall satisfaction than measuring satisfaction with individual interactions.
A search for the root cause of variation in customer journeys, must consider how processes cause variation. Voice of the Customer (VOC) Tables are a great tool to translate research into action, identifying specific business processes which are both sources of variation and weigh heavily on the customer journey.
VOC Tables identify action steps by listing customer experience attributes measured in a customer survey on the vertical axis, sorting each attribute by an importance rating. On the horizontal axis, a complete list of business processes is listed.
The business process is matched with the survey attributes and an informed judgment is made regarding the extent to which the business process influences survey attribute. A numeric value is assigned to the influence and placed in each cell.
A value is calculated by multiplying the strength of influence by the importance for each attribute and summing the resulting value for each column (business function) to determine which business functions have the most influence on the customer experience.
Consider the following example in a retail mortgage lending environment.
The satisfaction attributes and their relative importance, as determined in the survey, are listed in the far left column. Specific business processes in the customer journey are listed across the top of the table. For each cell, where satisfaction attributes and business process intersect, the researchers have made a judgment of the strength of the business process’s influence on the satisfaction attribute (represented by a numerical value of 1 – 3). The strength of the influence is multiplied by the importance and summed in each column to determine the relative importance of each business process in influencing overall customer satisfaction.
In this example, the following business processes will yield the highest ROI in terms of driving the customer experience.
Now, let’s look into another research methodology designed to identify areas for attention.
Critical Incident Technique
Not all customer experiences on the customer journey are equal. Some have more importance on the customer's impression of, and subsequent relationship to, the brand. These critical experiences can include such experiences as: a complaint, question, special request, or an employee going the extra mile.
Because they are memorable, critical interactions tend to have a powerful effect on the relationship with the customer, they are “moments of truth” where the brand has an opportunity to solidify the relationship or risk defection.
Critical Incident Technique (CIT) is an excellent research technique to identify and determine the most effective way to respond to them.
There is plenty of room for freedom in study design, but basically customer experience researchers want to identify: what happened, how the customer responded (positive or negative), what recovery strategy was used for negative incidents, and how effective was this recovery strategy.
First, ask the research participant to recall a recent experience in your industry (regardless of provider) that was particularly satisfying or dissatisfying. Next, gather details surrounding the experience by asking open-ended probing questions.
Ask questions like:
- When did the incident happen?
- What caused the incident? What are the specific circumstances that led to the incident or situation?
- Why did you feel the incident was particularly satisfying or dissatisfying?
- How did the provider respond to the incident? How did they correct it?
- What action(s) did you take as a result of the incident?
The analysis of CIT interviews is a process of classifying these incidents into clearly defined, mutually exclusive categories and sub-categories of increasing specificity. For example, the researcher may classify incidents into the following categories:
1) Service Delivery System Failures
- Unavailable Service
- Unreasonably Slow Service
- Other Core Service Failures
2) Customer Needs and Requests
- Special Customer Needs
- Customer Preferences
3) Unprompted and Unsolicited Actions
- Attention Paid to Customer
- Truly Out of the Ordinary Employee Behavior/Performance
- Holistic Evaluation
- Performance Under Adverse Circumstances
A similar classification technique is used to group both recovery strategies and their effectiveness, as well as classifying the attitudinal and behavioral result on the customer. How did the moment of truth impact their relationship to the brand? Did the customer purchase more or less, tell others about the experience, call for support more or less often, use different channels, change providers, etc.?
This analysis will identify the most common moments of truth within your industry. Identify how customers change their relationship to, and their behavior toward, brands in your industry as a result of each moment of truth, as well as evaluate the effectiveness of recovery strategies. This provides an informed position from which to make decisions on how best to train employees to recognize moments of truth and how to respond to each moment of truth to lead to a positive outcome.
Finally, researchers need a tool to test for consistency in the customer experience based on the customer’s demographic class.
Match-pair mystery shopping is an excellent tool to test for disparate treatment based on some demographic protected class. Matched-pair testing matches test and control mystery shoppers and sends them into the same employee or business unit.
The “test” shopper is a member of a protected minority class (race, color, age, religion, gender, national origin, disability or familial status) and is paired with a non-minority “control” shopper.
Kinēsis conducts two levels of statistical tests of significance at 95% and 80% confidence, which means there is at least a 95% or 80% chance any differences observed are the result of actual differences in treatment rather than the result of normal variation. The difference between the two confidence levels is the degree of certainty. An 80% confidence level identifies potential problem areas; 95% confidence identifies a much higher degree of certainty in detecting disparate treatment.
Since our species first appeared on the Savanna Plain 200,000 years ago, we’ve been hard wired to value consistency. Evolving in an unreliable environment produced us modern humans, who seek security in an insecure world.
There is no better example of this than McDonalds, who has sold hundreds of billions of hamburgers based on a value proposition of consistency.
Customer experience managers and researchers need to be aware of the role consistency plays in driving customer loyalty.
1 The three Cs of customer satisfaction: Consistency, consistency, consistency, 2014, mckinsey.com/industries/retail/our-insights/the-three-cs-of-customer-satisfaction-consistency-consistency-consistency. Accessed 1 Nov. 2017.
3 Why Consistency Is the Key to Profitable Customer Service, 2006, gallup.com/businessjournal/23953/why-consistency-key-profitable-customer-service.aspx. Accessed 2 Nov. 2017.
4 The three Cs of customer satisfaction: Consistency, consistency, consistency, 2014, mckinsey.com/industries/retail/our-insights/the-three-cs-of-customer-satisfaction-consistency-consistency-consistency. Accessed 1 Nov. 2017.
5 Why Consistency Is the Key to Profitable Customer Service, 2006, gallup.com/businessjournal/23953/why-consistency-key-profitable-customer-service.aspx. Accessed 2 Nov. 2017.
Eric Larse is co-founder of Seattle-based Kinesis, which helps companies plan and execute their customer experience strategies. Mr. Larse can be reached at firstname.lastname@example.org.
Assume on average a customer has five annual service interactions with a brand. Additionally, assume the brand has a respectable 95% satisfaction rate. Under these assumptions, any given customer has a 25% probability of a negative experience; in theory the entire customer base will have a negative experience in four years!