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Customer Experience Measurement in the Coronavirus Age: Implications for Customer Experience

Earlier in this three-part series we discussed the mechanism of infection and risk of SARS-CoV-2 infection. 

In summary, the most common cause of spread is believed to be airborne by inhaling virus particles exhaled into the environment.  The infectious dose of a virus is the amount of virus a person needs to be exposed to in order to establish an infection.  We currently do not know the infectious dose for SARS-CoV-2.  Estimates range from a few hundred to a few thousand virus particles.[1]  One virus particle will not cause an infection.  To be infected one must exceed the infectious dose by either being exposed to a cough or a sneeze.  Absent coughs or sneezes, under normal activity one must be exposed to the virus over time to exceed the infectious dose.

This post draws ocorn the foundation of the first to discuss the implications of the pandemic on the customer experience.

Modern day customer experiences exist in a finely tuned ecosystem, where the dramatic changes as a result of the pandemic have off set the delicate balance, causing problems from supply chain disruptions to an immediate shift away from in-person channels.

Furthermore, the pandemic represents what I call a moment of truth regarding the relationship with customers.  Moments of truth are specific experiences of high importance, where a customer either forms or changes their opinion of a brand in meaningful and lasting ways.  How brands respond to moments of truth, particularly in this time of global crisis, will strengthen or weaken the customers’ relationship to the brand.

Moments of truth are specific experiences of high

importance, where a customer either forms or changes

their opinion of a brand in meaningful or lasting ways.

Customers are stressed.  They feel uncertainty, fear and, frankly, exhaustion.   Ongoing concern for personal safety, education of children, and the well being of loved ones is exhausting.  This uncertainty and fear drives customers to seek shelter from resources they trust.  Brands which become a trusted resource, which provide comfort, true comfort, in the face of this crisis have an opportunity to not only do the right thing, but cement their customers’ relationship with the brand.  On the other hand, brands which fail to do so, risk destruction of their customer relationships.

Care for all Stakeholders

Perhaps the most important way brands can respond to the moment of truth presented by this crisis is showing true care for stakeholders in the brand: customers, employees, and the community.

Care for Customers

Brands must communicate care for customers.  Drawing on a personal example, March of 2020 was a particularly worrisome time for me.   At that time, the Seattle area was considered one of the epicenters of the outbreak, mandatory stay at home orders where being introduced – fear ruled – fear driven by uncertainty; uncertainty with respect to the safety of myself and loved ones; uncertainty with respect to the financial future; uncertainty with respect to the state of the entire globe.

Amidst all this uncertainty and fear I received an email from Citigroup entitled “Covid-19.  Let us know if we can help.”  It communicated personal care for me, encouraged alternative channel use: online, mobile and 24/7 contact center assistance, and contained links to CDC guidance.

A week later the campaign continued with an update on the actions Citigroup was implementing based on the pandemic; again, educating me to digital tools available, offering personal assistance if needed.

Two and a half months later, in June, I received an email expressing “heartfelt thanks” for adapting to changes and remaining loyal.  It described ways Citigroup was assisting with a variety of COVID-19 relief, specifically introducing a partnership with celebrity chef Jose Anres’ World Central Kitchen Campaign distributing meals in low-income neighborhoods in big cities like New York, and monitoring the globe for food shortages elsewhere. This not only demonstrated care for me personally, but care for the community.

Care for Communities

Citigroup’s donations to the World Central Kitchen campaign is one example of care for our communities.   There are countless examples of brands offering community support. 

  • A beer brewery, Brewdog, shifted production away from beer to hand sanitizer.
  • A Spanish sports retailer donated scuba masks to hospitals.
  • EBay offered free services to small business forced to switch from brick-and-mortar to ecommerce to keep their small business afloat – pledging $100 million in support of this endeavor.

Care for Employees

Employees are important.  They animate the brand and drive customer loyalty – particularly in moments of truth like these.  Research has determined that in many retail and service environments, there is a positive correlation between employee satisfaction and employee retention as well as customer loyalty.  They are not immune from the fear and the stress of this crisis.  Additionally, frontline employees spend all their time in the brand-customer interface.  They are the personal representatives of the brand.

Additionally, given these front-line employees spend the majority of their time in the brand-customer interface, they tend to have a level of understanding about the customer experience that management often misses.

As a result, it is incumbent on brands to attend to the stresses employees are under, demonstrate concern, and develop communication channels for employees to feed customer experience intelligence to management.

Delivery Channels

I’ve always been an advocate of meeting customers in their preferred channel; meeting them where they are today and delivering a seamless experience.   Obviously, over the recent decades there has been a migration from in-person channels to increasing self-directed, alternative channels.  The pandemic has immediately accelerated this shift.  Be it telehealth, online banking, in-home instruction of our children, or a restaurant delivering through UberEats, providers of all types now face increasing pressure to bring their business to their customers’ homes.

Emotional Well Being

As observed earlier, this pandemic is a moment of truth between many brands and their customers.  In our experience, customers primarily want three things from a provider: 1) empathy, 2) care/concern for their needs, and 3) competence.  We see this constantly.  Customers want to do business with brands that empathize with them, care about their needs, and are capable of satisfying those needs in a competent manner.  Brands that seek to attend to the emotional needs of their customers during this moment of truth will earn the loyalty and positive word-of-mouth of their customers.

In-Person Precautions and Mitigation Strategies

While the pandemic has accelerated an ongoing transition to alternative channels, some industries require an in-person experience.  Based on current science, in-person interactions can be relatively safe if followed within CDC and public health guidance outlined in the first part of this series:

  • Physical Distancing:  Estimates of exposure time all assume close personal contact.  Physical distancing decreases the likelihood of receiving an infectious dose by putting space between ourselves and others – current recommendations are 6 feet.

Furthermore, many in-person transactions can now be done touch free.  I recently had to rent a car, and was pleased to meet the rental attendant outside holding a tablet.  The attendant took down all my information, I never had to touch or sign anything.  In a different transaction, requiring a signature, I was offered a single use pen to keep.

  • Masks:  Masks are a core tool to provide physical distancing between individuals. Masks do not primarily act as a filter for the wearer, but suppress the amount of droplets an infected person can spread into the space around them. This reduces the risk that others will exceed the infectious dose of the virus.
  • Ventilation:  Well ventilated areas disperse virus particles making it less likely a dose exceeds the infectious limits.  Like my car rental agency, brands should endeavor to provide well ventilated spaces for employees and customers to interact – not only to protect customers but employees as well.
  • Length of Exposure:  Finally, brands should design service encounters to be as time efficient as possible.  Again, the CDC advises a 15-minute exposure limit for close personal contact.  Social distancing through physical distance, masks, and ventilation should increase this safe exposure limit.  However, strategies should be implemented to make service encounters as brief as possible.  For example, if you require information from your customers as part of the service interaction, collect this required information online or over the phone prior to an appointment.  This could help to make customers and employees safer and more comfortable.
  • Hand Washing & Sanitizer:  Hand washing and sanitization is the primary defense against transfer infections.

Putting it All Together

Putting all this together, let’s look at an industry Kinesis has the most experience with.  Kinesis’ largest practice is in the banking and financial services industry.  Recently the American Bankers Association (ABA) released the results of an industry survey regarding publically announced responses of US banks to the pandemic. [2] 

Many banks are applying some of the concepts discussed above in creative ways.  A review of a random selection of banks reveals the following responses ranked from most common to least common:

  1. Enhanced deep cleaning and disinfecting of work spaces;
  2. Implementing social distancing in work spaces, including branches;
  3. Encouraging use of alternative delivery channels, such as mobile and internet banking;
  4. Personalized assistance to customers negatively impacted by the pandemic;
  5. Increased donations to charity/ partnering with the local community to mitigate the effects of the pandemic;
  6. Allowing employees to work remotely if possible;
  7. Limiting access to branches (closing branch lobbies, limiting hours, appointment only banking);
  8. Paid time off for employees to self-quarantine or to care of school age children;
  9. Rotating schedules of customer-facing staff to reduce risk (one institution has applied a 10 days on 10 days off policy); and
  10. Educating customers of pandemic related fraud/scams.

In the next post, we will build off the foundation of the previous two posts to address the implications of the pandemic on customer experience measurement.


[1] Geddes, Linda. “Does a high viral load or infectious dose make covid-19 worse?”  newscientist.com, March 27, 2020.  Web May 14, 2020.

[2] “America’s Banks Are Here to Help: The Industry Responds to the Coronavirus.”  ABA.com, April 29, 2020.  Web.  May 19 2020.

Customer Experience Measurement in the Coronavirus Age: The Mechanism and Risk of Infection

Introduction

From Zoom happy hours, canceled events, concerns over how best to educate our children,  economic disruption, and caring for the victims, the SARS-CoV-2 pandemic, and the resulting public heath requirements are changing our lives in ways both big and small, superficial and tragic.  The customer experience is certainly no exception.  Writing about effects of the pandemic, while it unfolds, is a unique challenge – as we are learning more about the virus, its health effects, mitigation strategies, and overall effects on society in real time.  Things change daily and we are all learning on the fly here.  This series of blog posts is an early attempt to discuss the effects of the pandemic on customer experience research.

Before we begin, let me stress one thing.  I am a market researcher who specializes in evaluating the customer experience.  I am not an epidemiologist or doctor, and I have no training or experience in public health.  As a result, I will refrain from expressing scientific or medical theories or opinions of my own.  Any virus related conclusions or opinions expressed in this series of posts will be from credible sources and cited in footnotes.  If at any point it appears I am drawing medical or scientific conclusions of my own, it is unintentional, and should not be regarded as such. 

The need for managers of the customer experience to understand the implications of post-SARS-CoV-2 environment will most likely survive the immediate pandemic.  Changes in customer experience management will probably assume a more permanent nature.  First, this novel coronavirus may never go completely away, but rather become endemic in our society, meaning it could be a constant presence.[1] Second, recent history suggests SARS-CoV-2 is not the only novel-corona virus we are going to face in the coming decades.  Currently there are seven know coronaviruses that infect humans – prior to 2003 there were only four.  In a relatively short period of time, three new coronaviruses have jumped from animals to humans.[2]  The number of known coronaviruses to which humans are susceptible has nearly doubled in 17 years, so it does not require a great leap of the imagination to conclude this is not the last novel virus we will need to deal with.

This pandemic and its predicted aftermath represent a moment of truth for customers and their relationship to the brand.  In an uncertain and risky environment, customers will be even more likely to build relationships with brands they trust.  Forward thinking managers of the customer experience will respond by building more mechanisms to monitor customer perceptions of safety within the in-person channel and fulfillment via expanded alternative channels.

Mechanism of Infection

What we know now is the virus appears to spread primarily through person-to-person contact, via people in close contact with each other or to a lesser extent secondary transfer off contaminated surfaces.

SARS-CoV-2 survives on most surfaces.  Touching an infected surface and touching your eye, nose or mouth represents a risk of infection by transfer.[3]  Although, recent guidance from the CDC suggests transfer is not a significance mode of transmission.[4]  That being said, high touch surfaces such as door handles, elevator buttons, POS machines, and bathroom surfaces, should still be considered a potential risk for transfer infection.  However, the main mechanism of infection is via close personal contact.

When an infected person coughs, sneezes, talks or performs any other activity exhaling air, respiratory droplets are produced.  These droplets can land on the mouths or noses of people nearby, or in some circumstances, hang in the air in an aerosol form and be inhaled into the lungs.[5]  Current evidence suggests most individuals with mild to moderate symptoms can be infectious up to 10 days after symptom onset.  Further complicating this picture, it appears individuals without symptoms can be infectious even without knowing they are infected themselves.[6]

In order for customer experience managers to make informed choices about the customer experience in the post-Covid age, it is important to understand the mechanism of infection.  The infectious dose of a virus is the amount of virus a person needs to be exposed to in order to establish an infection.  The infectious dose varies depending on the virus  (the flu can cause infection after exposure to as few as 10 virus particles, others require exposure to thousands of particles to establish an infection).  Currently, the infectious dose of SARS-CoV-2 is not understood with any precision; however, some experts estimate it at a few hundred to a few thousand virus particles.[7] 

Like fire needs three things to burn (oxygen, fuel and heat), in my layman’s expression, three factors dictate Covid-19 transmission: activity, duration and proximity.

Different activities release different amounts of virus particles into the environment.  On the far end of the spectrum, a cough or sneeze releases about 200-million virus particles.  Furthermore, the force of a cough or sneeze can aerosolize these particles (thus allowing them to hang in the air for a long time), or travel across a room in an instant.  On the other end of the spectrum, breathing normally releases about 20 virus particles per minute, but with less force than a cough or sneeze.  As a result, the particles expelled by breathing will tend to be expelled at a slower speed and travel a shorter distance.  Speaking releases about 200 viral particles per minute.[8]

These rates of exposure are important in terms of understanding the time required to exceed the infectious dose threshold.  Consider the following formula:

The time required to be infected, assuming close proximity with no precautions, is the infectious dose divided by the rate the virus particles are expelled.

Assuming an infectious dose of 1,000 virus particles, very close proximity to someone speaking (close enough to inhale all the particles released by the speaker) would require 5 minutes to exceed the infectious dose:

Similarly, very close proximity to someone breathing normally would require a ten-fold increase in exposure (50 minutes):

Obviously, a single cough or sneeze with 200-million virus particles will instantly exceed the 1,000 particle threshold.

Again, currently, we do not know the infectious dose – estimates range from a few hundred to a few thousand virus particles.  Therefore, the data is insufficient to determine the exact duration of time to acquire an infection.  However, public health authorities do provide guidance.

Risk of Infection

The Centers for Disease Control and Prevention (CDC) advises, that for close contact with an individual in a non-healthcare setting, 15 minutes can be used as a threshold for the time to acquire an infectious dose (note: subsequent to the date of this blog post, the CDC’s guidance has been updated from 15 consecutive minutes to 15 non-consecutive minutes in total over a 24-hour period).[9]

Since currently we do not know SAR-CoV-2’s infectious dose, the key take away is an individual is not going to be infected by a single virus particle.  However, we are not free from risk.  We, as a society, are going to need to weigh the risks.  This will take the form of everyday people making everyday decisions about the risks they are willing to accept – both to themselves personally, and to society as a whole.  “Nothing is without risk, but you can weigh the risks. . . . It’s going to be a series of judgment calls people will make every day,” as  Dr. William Petri a professor of infectious disease at the University of Virginia Medical School, told the Washington Post. [10]

Forward-thinking customer experience brands will consider how individuals and society as a whole weigh these risks and build customer experiences around both customer expectations and responsible civic commitment.  The pandemic represents a moment of truth between brands and their customers.    Building responsible and safe customer experiences will become a core driver of trust in the brand.

Some factors individual consumers and customer experience managers will need to consider as we weigh these risks include: [11]

Distance:  At a minimum the environment and activity should allow for 6 feet separation to be maintained.

Duration:  The duration of the activity should be short enough to minimize infection risk, considering the specific activity (breathing, talking, singing, etc) and other mitigation efforts (distance, masks, ventilation, etc).

Ventilation:  Indoor venues should be well ventilated.  Outdoor venues are naturally well ventilated and, therefore, safer.

Masks:  Mask wearing by individuals will inhibit the spread of virus particles in the air. The CDC recommends wearing cloth face coverings in public settings where other social distancing measures are difficult to maintain (e.g., grocery stores and pharmacies).  Masks are less of a filter to protect the wearer, but they inhibit the spread of virus droplets in the air by the wearer – masks protect others.[12]

Transfer Risk:  Customers and employees should avoid unnecessary contact with high touch objects or surfaces, disinfecting surfaces and hands with hand sanitizer.

In the next post, we expand on this discussion of infection risk and mitigation strategies and look at the implications of these on the customer experience and customer experience management.


[1] “Nothing Like SARS: Researchers Warn The Coronavirus Will Not Fade Away Anytime Soon”  npr.org,  June 11, 202.  Web.  August 13, 2020.

[2] Fred Hutchinson Cancer Research Center. Dr. Amitabha Gupta “Fred Hutch and Covid-19.” August 4, 2020. Video, 10:15. https://www.youtube.com/watch?v=iaa40DflvOk&feature=youtu.be.

[3] Skinner, Michael.  “expert reaction to questions about COVID-19 and viral load”  sciencemediacentre.org, March 26, 2020.  Web. May 13, 2020.

[4] “How COVID-19 Spreads.”  CDC.gov, May 21, 2020.  Web.  May 21, 2020.

[5] “How COVID-19 Spreads.”  CDC.gov, May 21, 2020.  Web.  May 21, 2020.

[6] “Transmission of SARS-CoV-2: implications for infection prevention precautions.”  who.int, July 9, 2020.  Web.  August 13, 2020.

[7] Geddes, Linda. “Does a high viral load or infectious dose make covid-19 worse?”  newscientist.com, March 27, 2020.  Web May 14, 2020.

[8] Bromage, Eric.  “The Risks – Know Them – Avoid Them.” Erinbromage.com, May 6, 2020.  Web. May 13 2020.

[9] “Public Health Recommendations for Community-Related Exposure.”  CDC.gov, March 30, 2020.  Web.  May 15 2020.

[10] Shaver, Katherine.  “Wondering what’s safe as states start to reopen? Here’s what some public health experts say.”  Washingtonpost.com, May 15, 2020.  Web. May 15, 2020.

[11] Shaver, Katherine.  “Wondering what’s safe as states start to reopen? Here’s what some public health experts say.”  Washingtonpost.com, May 15, 2020.  Web. May 15, 2020.

[12] “About Masks.”  CDC.gov, August 6, 2020.  Web.  August 14 2020.

Implications of CX Consistency for Researchers – Part 3 – Common Cause v Special Cause Variation

Previously, we discussed the implications of intra-channel consistency for researchers.

This post considers two types of variation in the customer experience: common and special cause variation, and their implications for customer researchers.

The concepts of common and special cause variation are derived from the process management discipline Six Sigma.

Common cause variation is normal or random variation within the system.  It is 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

Special cause variation, on the other hand, is not random.  It conforms to laws of probability.   It 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

What are the implications of common and special cause variation for customer experience researchers?

Given the differences between common cause and special cause variation, researchers need a tool to help them distinguish between the two.  Researchers need a means of determining if any observed variation in the customer experience is statistical noise or a signal within the system.  Control charts are a statistical tool to make a determination if variation is noise or a signal.

Control charts track measurements within upper and lower quality control limits.  These quality control limits define statistically significant variation overtime (typically at a 95% confidence), which means there is a 95% probability that the variation is the result of an actual change in the customer experience (special cause variation) not just normal common cause variation.  Observed variation within these quality control limits are common cause variation.  Variation which migrates outside these quality control limits is special cause variation.

To illustrate this concept, consider the following example of mystery shop results:

Mystery Shop Scores

This chart depicts a set of mystery shop scores which both vary from month to month and generally appear to trend upward.

Customer experience researchers need to provide managers a means of determining if the month to month variation is statistical noise or some meaningful signal within the system.  Turning this chart into a control chart by adding statistically defined upper and lower quality control limits will determine if the monthly variation is common or special cause.

To define quality control limits, the customer experience researcher needs to determine the count of observations for each month, the monthly standard deviation, and the average count of shops across all months.

The following table adds these three additional pieces of information into our example:

 

Month

Count of Mystery Shops Average Mystery Shop Scores Standard Deviation of Mystery Shop Scores

May

510 83% 18%

June

496 84% 18%

July

495 82% 20%

Aug

513 83%

15%

Sept 504 83%

15%

Oct 489 85%

14%

Nov 494 85%

15%

Averages 500 83.6%

16.4%

To define the upper and lower quality control limits (UCL and LCL, respectively), apply the following formula:

Where:

x = Grand Mean of the score

n = Mean sample size (number of shops)

SD = Mean standard deviation

 

These equations yield quality control limits at 95% confidence, which means there is a 95% probability any variation observed outside these limits is special cause variation, rather than normal common cause variation within the system

Calculating these quality control limits and applying them to the above chart produces the following control chart, with upper and lower quality control limits depicted in red:

Control Chart

This control chart now answers the question, what variation is common cause and what variation is special cause.  The general trend upward appears to be statistically significant with the most recent month above the upper quality control limit.  Additionally, this control chart identifies a period of special cause variation in July.  With 95% confidence we know some special cause drove the scores below the lower control limit.  Perhaps this special cause was employee turnover, perhaps a new system rollout, or perhaps a weather event that impacted the customer experience.

 

 

Implications of CX Consistency for Researchers – Part 2 – Intra-Channel Consistency

Previously, we discussed the implications of inter-channel consistency for researchers, and introduced a process for management to define a set of employee behaviors which will support the organization’s customer experience goals across multiple channels.

This post considers the implications of intra-channel consistency for customer experience researchers.

As with cross-channel consistency, intra-channel consistency, or consistency within individual channels requires the researcher to identify the causes of variation in the customer experience.  The causes of intra-channel variation, is more often than not at the local level – the individual stores, branches, employees, etc.  For example, a bank branch with large variation in customer traffic is more likely to experience variation in the customer experience.

Regardless of the source, consistency equals quality.

In our own research, Kinēsis conducted a mystery shop study of six national institutions to evaluate the customer experience at the branch level.  In this research, we observed a similar relationship between consistency and quality.  The branches in the top quartile in terms of consistency delivered customer satisfaction scores 15% higher than branches in the bottom quartile.  But customer satisfaction is a means to an end, not an end goal in and of itself.  In terms of an end business objective, such as loyalty or purchase intent, branches in the top quartile of consistency delivered purchase intent ratings 20% higher than branches in the bottom quartile.

Satisfaction and purchase intent by customer experience consistency

Purchase intent and satisfaction with the experience were both measured on a 5-point scale.

Again, it is incumbent on customer experience researchers to identify the causes of inconsistency.   A search for the root cause of variation in customer journeys must consider processes cause variation.

One tool to measure process cause variation is a Voice of the Customer (VOC) Table. VOC Tables have a two-fold purpose:  First, to identify specific business processes which can cause customer experience variations, and second, to identify which business processes will yield the largest ROI in terms of improving the customer experience.

VOC Tables provide a clear road map to identify action steps using a vertical and horizontal grid.  On the vertical axis, each customer experience attribute within a given channel is listed.  For each of these attributes a judgment is made about the relative importance of each attribute.  This importance is expressed as a numeric value.   On the horizontal axis is a exhaustive list of business processes the customer is likely to encounter, both directly and indirectly, in the customer journey.

This grid design matches each business process on the horizontal axis to each service attribute on the vertical axis.  Each cell created in this grid contains a value which represents the strength of the influence of each business process listed on the horizontal axis to each customer experience attribute.

Finally, a value is calculated at the bottom of each column which sums the values of the strength of influence multiplied by the importance of each customer experience attribute.  This yields a value of the cumulative strength of influence of each business process on the customer experience weighted by its relative importance.

Consider the following example in a retail mortgage lending environment.

VOC Table

In this example, the relative importance of each customer experience attributes was determined by correlating these attributes to a “would recommend” question, which served as a loyalty proxy.  This yields an estimate of importance based on the attribute’s strength of relationship to customer loyalty, and populates the far left column.  Specific business processes for the mortgage process are listed across the top of this table.  Within each cell, an informed judgment has been made regarding the relative strength of the business process’s influence on the customer experience attribute.  This strength of influence has been assigned a value of 1 – 3.  It is multiplied by the importance measure of each customer experience attribute and summed into a weighted strength of influence – weighted by importance, for each business process.

In this example, the business processes which will yield the highest ROI in terms of driving the customer experience are quote of loan terms (weighted strength of influence 23.9), clearance of exemptions (22.0), explanation of loan terms (20.2), loan application (18.9) and document collection (16.3).

Next, we will look into the concepts of common and special cause variation, and another research methodology designed to identify areas for attention. Control charts as just such a tool.

Business Case and Implications for Consistency – Part 7 – Disparate Treatment of Protected Classes

Previously we explored the business case for consistency both within individual channels and across multiple channels.  In this post, we will explore consistency of treatment in a demographic context.

Inconsistent treatment based on certain demographic characteristics is illegal.  The Civil Rights Act of 1964 prohibits discrimination in almost all privately owned service industries based on race, color, religion, gender, or national origin.  Other industries, such as retail banking, have additional regulatory requirements.

Beyond this legal risk, managers must be aware of the significant risk to the reputation of the brand posed by discriminatory practices.

Managers may seek comfort in the knowledge that their company’s policies and procedures are not to refuse service to anyone.  However, this overt discrimination is just a small part of the risk associated with discrimination.  Beyond overt discrimination, which is extremely rare, there are two other categories of discriminatory practices: disparate impact and disparate treatment.

Disparate impact is the result of policies or business practices which have an unequal impact.  A restaurant with a policy to require prepayment for meals from one demographic group and not another is an example of disparate impact.

Disparate treatment is differences in treatment that originate at the customer-employee interface.  Disparate treatment does not necessarily need to be a conscious act.  It can be an unconscious pattern or practice of different treatment that the employee is not even aware of.  The use of name, offering promotional material to a customer of one group as opposed to a customer on another group are all examples of disparate treatment.

Now, observing differences is treatment is not necessarily proof of discrimination.  Human behavior, after all, is variable.  There is a certain amount of normal variation in all service encounters.  The trick is to determine if disparate treatment observed represents a pattern or practice of discrimination.  Fortunately statistics has the answer, we use statistical tests of significance to determine both if observed differences in treatment are the result of actual discriminatory practices and the likelihood that any one member of a protected class will be treated differently than a member of another protected class.  It should be noted, however, that regulatory agencies set the bar much higher.  Many do not necessarily rely on statistical testing.  In their view, any single case of disparate treatment is evidence of discrimination.

In a future post we will discuss the implications for customer experience researchers in testing for disparate treatment.

Business Case and Implications for Consistency – Part 6 – Intra-Channel Consistency

Previously we explored inter-channel consistency and its implication for customer experience managers.

Inconsistent customer experiences are a significant threat to customer loyalty.  In a previous post, we observed the casual relationship between consistency in the customer experience and feelings of trust and loyalty.

Consistency drives satisfaction.  It is extremely common to see a correlation between intra-channel consistency and performance.  Consider the following scatter plot from Kinesis’ research, which plots bank branch customer satisfaction by the variation in branch customer satisfaction:

Branch Satisfaction by VariationAs this plot demonstrates, consistency correlates with quality.  Branches with higher customer satisfaction ratings are also the most consistent.  In our customer experience research proactive we see this time and time again.

Additionally, this plot also demonstrates that top-line averages of customer satisfaction can be misleading.  The bank in this plot had an average customer satisfaction rating of 93%.  However, many branches fall well below this top-line average, resulting in an incomplete picture of the customer experience.  Customers do not experience top-line averages; they experience the customer experience one interaction at a time at the local business unit level.

What are the implications for managers of the customer experience?

The first implication for managers is the above observation that top-line averages can mislead.  Top-line averages hide individual business units with both low and inconsistent customer satisfaction.  Top-line averages come between management and customers, distancing managers from how customers actually experience the brand.

Secondly, variation must be managed at the cause.  Intra-channel variation is almost always at the local business unit level.  For example, a store with a high degree of variation in customer traffic will experience a high degree of variation in the customer experience if management does not mitigate the effects of the variation in traffic.

How to manage for consistency:

  1. Manage inconsistency at the cause
  2. Write a clear mission statement
  3. Use appropriate analytics
  4. Don’t silo analytics by channel
  5. Meet regularly with employees to share problems and potential solutions
  6. Focus on customer journey

Intra-channel consistency needs to be managed at the local level – individual stores and agents.  Tools need to be available deep into the organization to allow managers at the lowest level of each channel to deliver a consistent experience.

In the next post we will explore demographic consistency, treating all customers the same regardless of their demographic profile.

Business Case and Implications for Consistency – Part 5 – Inter-Channel Consistency

Previously we explored the business case for consistency by considering the influence of poor experiences.

The modern customer experience environment is constituted of an ever expanding variety of delivery channels, with no evidence of the slowing of the pace of channel expansion.  As channel expansion continues, customer empowerment is increasing with customer choice.  Customer relationships with brands are not derived from individuals’ discrete interactions.  Rather, customer relationships are defined by clusters of interactions, clusters of interactions across the entire life cycle of the relationships, and across all channels.  Inter-channel consistency defines the customer relationship.

McKinsey and Company concluded in their 2014 report, The Three Cs of Customer Satisfaction: Consistency, Consistency, Consistency, demonstrated, in a retail banking context, a link between cross-channel consistency and bank performance.

In customers’ minds, all channels belong to the same brand.  Customers do not consider management silos or organizational charts – to them all channels are the same.  Customers expect consistent experiences regardless of channel.  In their minds, an agent at a call center should have the same information and training as in-person agents.

What are the implications for managers of the customer experience?

The primary management issue in aligning disparate channels is to manage inconsistency at its cause.  The most common cause of inconsistencies across channels is the result of siloed management, where managers’ jurisdiction is limited to their channel. Inter-channel consistency is increasingly important as advances in technology expand customer choice.  Brands need to serve customers in the channel of their choice.   Therefore, the cause of inter-channel inconsistency must be managed higher up in the organization at the lowest level where lines of authority across channels converge, or through some kind of cross-functional authority.

The implications for management are not limited to senior management and cross-functional teams. Customer experience managers should be aware that top-line averages can mislead.  Improvement opportunities are rarely found in top-line averages, but at the local level.  Again, the key is to manage inconsistency at the cause.  Inconsistency at the local level almost always has a local cause; as a result, variability in performance must be managed at the local level as well.

In a previous post from 2014, we discussed aligning cross channel service behaviors and attributes.

In the next blog post in this series, we will explore intra-channel consistency.

Business Case and Implications for Consistency – Part 4 – Consistency and the Outsized Influence of Poor Experiences

In earlier posts we discussed the business case for consistency, primarily because consistency drives customer loyalty and the causal chain from consistency to customer loyalty.

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.

In 4 Years: 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.

Negative Experiences Outweigh Positive Experiences

 

In our next post we will discuss inter-channel consistency.

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.

In our next post we will continue to explore the business case for consistency by considering the influence of poor experiences.

 

Business Case and Implications for Consistency – Part 2: Business Case for Consistency

In a previous post we considered why humans value consistency.

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.