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.
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.
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:
In an earlier post we explored how customers experience all aspects of their relationship with a brand through the lens of their emotional state, and observed that all brands must be prepared to meet each customer in their specific emotional state – be they happy, excited, depressed or angry.
Research has determined that, not surprisingly, people are motivated to maintain positive moods, and mitigate negative affective states. When feeling good we tend to make choices that maintain a positive mood. Customers in a positive mood are more loyal, and more likely to interpret information favoring a current brand. Meanwhile, people in negative affective states make choices that have the potential to change or, in particular, improve their moods.
A key to maintaining positive moods is arousal, or more specifically, the management of arousal. Let’s take a look at how arousal management influences consumer choice. Consumers in a positive mood prefer products congruent with their state of arousal. Excited or happy consumers want to stay excited or happy, while relaxed and calm consumers what to stay relaxed and calm. Consumers in a negative mood prefer products with the potential to change their level of arousal.
In considering the role of customer emotions in their relationship to a brand, it is important to understand the implications of customer emotions on design of the customer experience. It is impossible, of course, to plan every customer experience or to ensure that every experience occurs exactly as intended. However, brands can identify and plan for the types of experiences that impart the desired emotional state on the customer. It is useful to group these experiences into three categories of interaction with the customer: Stabilizing, Critical, and Planned.
Stabilizing interactions promote customer retention, particularly in the early stages of the relationship.
New customers are probably in a positive state of valence, with either a high state of arousal (happy/excited) or a negative state of arousal (relaxed/calm). Remember, people are motivated to maintain positive moods, therefore, the objective of these stabilizing interactions is to maintain this positive mood.
The keys to an effective stabilizing strategy and maintaining these positive moods are education, competence and consistency.
New customers are at the highest risk of defection. As customers become more familiar with a brand they adjust their expectations accordingly. It is important that expectations be set appropriately to eliminate conflict with reality. Conflict between expectations and reality early in the customer relationship runs the risk changing the customer’s mood from positive to negative. They are more likely to experience disappointment, and thus more likely to defect.
Education influences expectations, helping customers develop realistic expectations. It goes beyond simply informing customers about the products and services offered by the company. It systematically informs new customers how to use the brand’s services more effectively and efficiently, how to obtain assistance, how to complain, and what to expect as the relationship progresses. In addition to influencing expectations, systematic education leads to greater efficiency in the way customers interact with the company, thus driving down the cost of customer service and support.
Critical interactions are service encounters that lead to memorable customer experiences. While most service is routine, from time to time a situation arises that is out of the ordinary: a complaint, a question, a special request, a chance for an employee to go the extra mile. We call these critical interactions “moments of truth.” The outcomes of moments of truth can be either positive or negative – they are rarely neutral.
Because they are memorable and unusual, moments of truth tend to have a powerful effect on the customer relationship. We often think of moments of truth as instances when the brand has an opportunity to solidify the relationship – earning a loyal customer, or risk the customer’s defection. Positive outcomes lead to positive states of valence (excited, happy, relaxed, calm) with greater wallet share, loyalty, and positive word word-of-mouth endorsements; while negative outcomes generate negative states (anger, frustration, depression); and result in customer defection, diminished share of wallet and unfavorable word-of-mouth.
We are in an era of automated channels. Automated channels are essential for meeting customer expectations and reducing transaction costs, but technical solutions are not, by themselves, able to drive an emotional connection between customers and the brand – particularly in moments of truth. Employees, emotionally intelligence employees, empowered to resolve the issue are critical in driving an emotional connection. In a future post, we will discuss the concept of Emotional Intelligence of frontline employees in handling moments of truth.
An effective customer experience strategy should include systems for recording critical interactions, analyzing trends and patterns, and feeding that information back to the organization. Employees can then be trained to recognize critical opportunities, and empowered to respond to them in such a way that they will lead to positive outcomes and desired customer behaviors.
Planned interactions are intended to increase customer profitability through up-selling and cross-selling. These interactions are frequently triggered by changes in the customers’ purchasing patterns, account usage, financial situation, family profile, etc. CRM analytics combined with Big Data are becoming quite effective at recognizing such opportunities and prompting action from service and sales personnel.
Customer experience managers should have a process to record and analyze the quality of execution of planned interactions with the objective of evaluating the performance of the brand at the customer brand interface – regardless of the channel.
The key to an effective strategy for planned interactions is appropriateness. Triggered requests for additional purchases must be made in the context of the customers’ needs and permission; otherwise the requests will come off as clumsy and annoying. By aligning information about execution quality (cause) and customer impressions (effect), customer experience managers can build a more effective and appropriate approach to planned interactions.