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.
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.
The business attribute with the highest correlation to profitability is loyalty. Loyalty lowers sales and acquisition costs per guest by amortizing these costs across a longer lifetime – leading to some extraordinary financial results. However, the question remains, what service attributes drive guest loyalty?
To answer this question from a behavioral standpoint Kinesis conducted 400 restaurant mystery shops with the purpose of determining which service attributes/behaviors drive guest return intent. Forty-six service attributes were observed across five dimensions of the guest experience: environment, food & beverage quality, greeting, personal attention and timing of food and beverage delivery.
The attributes measured grouped into these five dimensions as follows:
- Table maintained appropriately throughout the meal
- Dining room clean, organized and well maintained
- Exterior building, parking lot, walkways and planters clean
- Silverware, china, glassware and your table clean
- Men’s restroom clean and stocked with supplies
- Lighting fixtures clean and working
- Lobby area clean and organized
- Menus clean and in good condition
- Women’s restroom clean and stocked with supplies
- Bar clean, organized and well maintained
- Room temperature level comfortable
Food & Beverage Quality
- Entrees presented attractively, and tasted good
- Appetizer presented attractively, and tasted good
- Drinks attractively presented, and tasted good
- Dessert presented attractively, and tasted good
- Greeting made feel welcome
- Prompt greeting
- Staff members greet with a friendly smile as being seated
- Thanked and encouraged to visit again
- Ask specific questions about your experience upon leaving
Service: Personal Attention
- Server attentive and prompt throughout the meal
- Server discuss the beverage menu, suggest an item or ask about your preferences
- Server discuss the appetizer menu, suggest an item or ask about your preferences
- Server promote daily specials
- Host carry on a conversation as being seated
- Server discuss the beverage menu or ask about preferences
- Receive appetizer in a timely manner
- Manager engage guests in conversation
- Server smiling and enjoying time with all the guests
- Acknowledged by a server in a timely manner
- Attentive to needs while in the bar area
- Server discuss the dessert menu, suggest an item or ask about preferences
- Server knowledgeable and confident when responding to questions
- Manager present
- Server try and entice you to order their favorite appetizer(s)
- Resolve any service, food or beverage issues
- Food and beverage service timed well
- Receive entrees in a timely manner
- Receive starter soup/ salad in a timely manner
- Receive appetizer in a timely manner
- Manager engage guests in conversation
- Receive drink orders in timely manner
- Receive dessert in a timely manner
- Cashed out in a timely manner
- Acknowledge and get order in a timely manner
- Drinks arrive in a timely manner
In order to determine the relationship of these attributes to return intent, Kinesis asked mystery shoppers if, based on the guest experience, they intended to return to the restaurant. This independent variable was then used as a basis for cross-tabulation to determine the frequency with which the behaviors were observed in shops with positive return intent and negative return intent.
The results of this cross tabulation is as follows:
|Environment||Shops with …|
|Positive Return Intent||Negative Return Intent|
|Table maintained appropriately throughout the meal||96%||73%|
|Dining room clean, organized and well maintained||100%||90%|
|Exterior building, parking lot, walkways and planters clean||100%||94%|
|Silverware, china, glassware and your table clean||98%||94%|
|Men’s restroom clean and stocked with supplies||96%||91%|
|Lighting fixtures clean and working||98%||95%|
|Lobby area clean and organized||100%||98%|
|Menus clean and in good condition||99%||97%|
|Women’s restroom clean and stocked with supplies||93%||92%|
|Bar clean, organized and well maintained||99%||98%|
|Room temperature level comfortable||95%||94%|
|Food & Beverage Quality||Shops with …|
|Positive Return Intent||Negative Return Intent|
|Entrees presented attractively, and tasted good||98%||58%|
|Appetizer presented attractively, and tasted good||97%||88%|
|Drinks attractively presented, and tasted good||97%||88%|
|Dessert presented attractively, and tasted good||97%||97%|
|Greeting||Positive Return Intent||Negative Return Intent|
|Thanked and encouraged to visit again||95%||63%|
|Ask specific questions about your experience upon leaving||35%||8%|
|Greeting made feel welcome||93%||70%|
|Staff members greet with a friendly smile as being seated||60%||44%|
|Service: Personal Attention||Positive Return Intent||Negative Return Intent|
|Server attentive and prompt throughout the meal||93%||45%|
|Server discuss the beverage menu, suggest an item or ask about your preferences||80%||43%|
|Server discuss the appetizer menu, suggest an item or ask about your preferences||68%||33%|
|Server promote daily specials||64%||33%|
|Host carry on a conversation as being seated||70%||41%|
|Server discuss the beverage menu or ask about preferences||63%||35%|
|Manager engage guests in conversation||73%||47%|
|Server smiling and enjoying time with all the guests||97%||73%|
|Acknowledged by a server in a timely manner||96%||73%|
|Attentive to needs while in the bar area||92%||72%|
|Server discuss the dessert menu, suggest an item or ask about preferences||81%||65%|
|Acknowledge and get order in a timely manner||94%||80%|
|Server knowledgeable and confident when responding to questions||98%||86%|
|Server try and entice you to order their favorite appetizer(s)||64%||57%|
|Resolve any service, food or beverage issues||53%||67%|
|Service: Timing||Positive Return Intent||Negative Return Intent|
|Food and beverage service timed well||92%||51%|
|Receive entrees in a timely manner||92%||59%|
|Server promote daily specials||64%||33%|
|Receive starter soup/ salad in a timely manner||91%||60%|
|Receive appetizer in a timely manner||93%||65%|
|Receive drink orders in timely manner||96%||73%|
|Receive dessert in a timely manner||95%||77%|
|Cashed out in a timely manner||97%||81%|
|Acknowledge and get order in a timely manner||94%||80%|
|Drinks arrive in a timely manner||98%||85%|
Putting all this together, the ten attributes with the largest difference between shops with positive and negative return intent are:
|Top 10 Attributes|
|Service: Personal Attention||Server attentive and prompt throughout the meal||48%|
|Service: Timing||Food and beverage service timed well||41%|
|Food||Entrees presented attractively, and tasted good||40%|
|Service: Personal Attention||Server discuss the beverage menu, suggest an item or ask about your preferences||37%|
|Service: Personal Attention||Server discuss the appetizer menu, suggest an item or ask about your preferences||35%|
|Service: Timing||Receive entrees in a timely manner||33%|
|Service: Personal Attention||Server promote daily specials||31%|
|Greeting||Thanked and encouraged to visit again||31%|
|Service: Timing||Receive starter soup/ salad in a timely manner||30%|
|Service: Personal Attention||Host carry on a conversation as being seated||29%|
Of the ten attributed with the strongest relationship to return intent, five belong to the personal attention dimension, three belong to the timing dimension, the food & beverage quality and greeting dimensions round out the top ten with one attribute each.
Directing our attention from specific attributes to broader dimensions, the following chart shows the average difference in shops with positive return intent to shops with negative return intent:
Outside of the timing of food and beverage delivery, the dimensions of the customer experience with the strongest correlation to return intent are the greeting and personal attention, followed by food and beverage quality and the physical environment.
Customer loyalty is the business attribute with the strongest correlation to profitability. Loyalty lowers sales and acquisition costs per customer by amortizing these costs across a longer lifecycle, leading to extraordinary financial results. A 5% increase in customer loyalty can translate, depending on the industry, into a 25% to 85% increase in profits.
Many customer experience managers want to include a measure of loyalty in their customer experience research. Indeed loyalty and how brand perception drives loyalty is the foundation of any brand perception research. However, loyalty is a behavior measured longitudinally over time, and surveys best measure customer attitudes. As a result, researchers typically use attitudinal proxies for customer loyalty. Generally the two most common proxies are either a “would recommend” or a “customer advocacy” question.
- Would Recommend: A “would recommend” question is typically Net Promoter (NPS) or some other measure of the customer’s likelihood of referring to a friend, relative or colleague. It stands to reason, if one is going to refer others to a brand, they will remain loyal as well. Promoters’ willingness to put their reputational risk on the line is founded on a feeling of loyalty and trust.
- Customer Advocacy: A customer advocacy question asks if the customer agrees with the following statement, “the brand cares about me, not just the bottom line.” The concept of trust is perhaps more evident in customer advocacy. Customers who agree with this statement trust the brand to do right by them, and not subjugate their best interests to profits. Customers who trust the brand to do the right thing are more likely to remain loyal.
We’ve seen some loyalty surveys (particular those employing the NPS methodology), which only ask the loyalty proxy with little or no other areas of investigation. We believe this is a bad practice for a number of reasons:
- Customer Experience: Customers who have affirmatively taken the action of clicking on the survey want to give you their opinion (they want to participate in the survey), and based on their experience are expecting a multiple question survey. Presenting them with just one rating scale risks alienating them as they may feel they didn’t get an appropriate opportunity to share their opinion, and ultimately feel it was not worth their time to participate. Secondly, some customers may conclude the survey system is broken in some way as it only presented them with one question, resulting in customer confusion.
- Actionable Research Results: A survey consisting of one NPS rating is not going to yield any information from which to draw conclusions about how customers feel about the brand. It will produce an average rating and frequency of promoters and detractors, but no context in which to interpret the results.
Establishing and measuring loyalty proxies are an important first step in evaluating brand perception. Additional areas of investigation should include indentifying and comparing customer impressions of the brand to your desired brand personality, and evaluate customer engagement or wallet share.
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:
Loyalty. There is almost universal agreement that it is an objective – if not the objective – of customer experience management. It is highly correlated to profitably. It lowers sales and acquisition costs per customer by amortizing these costs across a longer lifetime – leading to extraordinary financial results. In retail banking a 5% increase in loyalty translates to an 85% increase in profits.
Loyalty is Emotion Driven
Banks often see themselves as transaction driven; delivery channels are evaluated on their cost per transaction. As a result, there is a lot of attention given to and investment in automated channels which reduce transaction costs and at the same time offer more convenience to customers. Win-win, right? The bank drives costs out of the transaction and customers get the convenience of performing a variety of transactions untethered by time or space. However, while transaction costs and convenience are important, loyalty is often driven by an emotional connection with the institution. An emotional connection fostered by interaction with actual employees at moments of need for the customers –moments with a high level of emotional importance to the customer – moments of truth.
Moments of truth are atypical events, where customers experience a high emotional energy in the outcome (such a lost credit card, loan application, or investment advice). In one study published in McKinsey Quarterly, positive experiences during moments of truth led to more than 85% of customers increasing wallet share by purchasing more products or investing more of their assets (Beaujean et al 06)
Impersonal alternative channels lack the ability to bind the customer to the institution. It’s the people. Effective handling of moments of truth requires frontline staff with the emotional tools or intelligence to recognize the emotional needs of the customer and bind them to the institution.
Customers experience all aspects of their relationship with a brand through the lens of their emotional state. Be they happy, excited, depressed or angry all brands must be prepared to meet each customer in their specific emotional state. It’s a challenge – but also an opportunity. Ultimately, loyalty is emotionally driven. Brands that can react to and manage customer emotions stand to reap the rewards of customer loyalty.
To understand the role of the customer’s mood in managing the customer experience, it is instructive to consider how two affective states work together to define mood. The following model tracks mood across valence (the extent to which the emotional state is positive or negative) and arousal (the extent to which the energy mobilization of the emotional state is experienced on a scale of active to passive or aroused to calm).
Together, these affective states of valence and arousal can define all human emotions. States of positive valence and high arousal are excited or happy; negative valence and low arousal are bored or depressed. States of positive valence and low arousal are calm and relaxed, and negative valence and high arousal are angry or frustrated.
Here is a detailed map of a variety of emotions across these two dimensions.
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. For example, researchers have demonstrated a preference for TV shows that held the greatest promise of providing relieve from negative affective states. People in a sad mood want to be comforted, anxious people want to feel control and safety.
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. For example, in an experiment, participants were offered the choice of an energy drink or iced tea. The following chart illustrates participant’s preference by the state of arousal and valence:
Participants in a positive mood, preferred the drink congruent with their level of arousal, those in a positive low-arousal state preferred iced tea, and those in a positive high-arousal state preferred an energy drink. On the other hand, those in a negative mood preferred a drink incongruent with their energy state, those in a negative low-arousal state preferred an energy drink, and those in a negative high-arousal state preferred iced tea.
Understanding the role of arousal management in customers’ innate desire to maintain positive moods and mitigate negative moods has far reaching implications for just about every element of the customer experience from sales, to problem resolution, to customer experience design, hiring, training and customer experience measurement. In future posts we will explore these implications for each of these elements of the customer experience.
As we explored in an earlier post, 3 Types of Customer Interactions Every Customer Experience Manager Must Understand, there are three types of customer interactions: Stabilizing, Critical, and Planned.
The third of these, “planned” interactions, are intended to increase customer profitability through up-selling and cross-selling.
These interactions are frequently triggered by changes in the customer’s 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 increased spending must be made in the context of the customer’s 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.
Research Plan for Planned Interactions
The first step in designing a research plan to test the efficacy of these planned interactions is to define the campaign. Ask yourself, what customer interactions are planned based on customer behavior? Mapping the process will define your research objectives, allowing an informed judgment of what to measure and how to measure it.
For example, after acquisition and onboarding, assume a brand has a campaign to trigger planned interactions based on triggers from tenure, recency, frequency, share of wallet, and monetary value of transactions. These planned interactions are segmented into the following phases of the customer lifecycle: engagement, growth, and retention.
Often it is instructive to think of customer experience research in terms of the brand-customer interface, employing different research tools to study the customer experience from both sides of this interface.
In our example above, management may measure the effectiveness of planned experiences in the engagement phase with the following research tools:
|Customer Side||Brand Side|
Post-transaction surveys are event-driven, where a transaction or service interaction determines if the customer is selected for a survey, targeting specific customers shortly after a service interaction. As the name implies, the purpose of this type of survey is to measure satisfaction with a specific transaction.
|Transactional Mystery Shopping
Mystery shopping is about alignment. It is an excellent tool to align sales and service behaviors to the brand. Mystery shopping focuses on the behavioral side of the equation, answering the question: are our employees exhibiting the sales and service behaviors that will engage customers to the brand?
|Overall Satisfaction Surveys
Overall satisfaction surveys measure customer satisfaction among the general population of customers, regardless of whether or not they recently conducted a transaction. These surveys give managers a feel for satisfaction, engagement, image and positioning across the entire customer base, not just active customers.
|Alternative Delivery Channel Shopping
Website mystery shopping allows managers of these channels to test ease of use, navigation and the overall customer experience of these additional channels.
Employee surveys often measure employee satisfaction and engagement. However, they can also be employed to understand what is going on at the customer-employee interface by leveraging employees as a valuable and inexpensive resource of customer experience information.They not only provide intelligence into the customer experience, but also evaluate the level of support within the organization, and identifies perceptual gaps between management and frontline personnel.
In the growth phase, one may measure the effectiveness of planned experiences on both sides of the customer interface with the following research tools:
|Customer Side||Brand Side|
Awareness of the brand, its products and services, is central planned service interactions. Managers need to know how awareness and attitudes change as a result of these planned experiences.
|Cross-Sell Mystery Shopping
In these unique mystery shops, mystery shoppers are seeded into the lead/referral process. The sales behaviors and their effectiveness are then evaluated in an outbound sales interaction.
|Wallet Share Surveys
These surveys are used to evaluate customer engagement with and loyalty to the brand. Specifically, to determine if customers consider the brand their primary provider, and identify potential road blocks to wallet share growth.
Finally, planned experiences within the retention phase of the customer lifecycle may be monitored with the following tools:
|Customer Side||Brand Side|
|Lost Customer Surveys
Lost customer surveys identify sources of run-off or churn to provide insight into improving customer retention.
|Life Cycle Mystery Shopping
Shoppers interact with the company over a period of time, across multiple touch points, providing broad and deep observations about sales and service alignment to the brand and performance throughout the customer lifecycle across multiple channels.
Comment tools are not new, but with modern Internet-based technology they can be used as a valuable feedback tool to identify at risk customers and mitigate the causes of their dissatisfaction.
Call to Action – Make the Most of the Research
Research without call to action may be interesting, but not very useful. Regardless of the research choices you make, be sure to build call to action elements into research design.
For mystery shopping, we find linking observations to a dependent variable, such as purchase intent, identifies which sales and service behaviors drive purchase intent – informing decisions with respect to training and incentives to reinforce the sales activities which drive purchase intent.
For surveys of customers, we recommend testing the effectiveness of the onboarding process by benchmarking three loyalty attitudes:
- Would Recommend: The likelihood of the customer recommending the brand to a friend relative or colleague.
- Customer Advocacy: The extent to which the customer agrees with the statement, “you care about me, not just the bottom line?”
- Primary Provider: Does the customer consider the brand their primary provider for similar services?
As you contemplate campaigns to build planned experiences into your customer experience, it doesn’t matter what specific model you use. The above model is simply for illustrative purposes. As you build your own model, be sure to design customer experience research into the planned experiences to monitor both the presence and effectiveness of these planned experiences.