Tag Archive | Actionable Research Design

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.

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Implications of CX Consistency for Researchers – Part 1 – Inter-Channel Consistency

Previously, we discussed the business case and implications for customer experience mangers for inter-channel consistency.

This post considers the implications of cross-channel consistency for customer experience researchers.  The first research implication of inter-channel consistency is to understand that researchers must investigate service delivery consistency at its cause.

The range of choices available to customers here in the 21-st century is incredible.  Gone are the Henry Ford days when you, as he put it, “could have any color you want as long as it’s black.”  Modern customers have an array of choices available to them not only in the brands but in delivery channels.   Modern brands must serve channels in the channel of the customer’s choice, be it on-line, mobile, contact center, or in-person.   As customer choice expands cross-channel consistency has become more and more important.

The problem for customer experience researchers is that this channel expansion requires a broad tool box of research techniques, as different channels require unique systems and processes appropriate to the channel.  Systems and processes for on-line channels are different than those for in-person channels.  These different systems and processes often lead to the siloing of channels, which may help make individual channels more efficient, but run the risk in inconsistencies in the customer experience from one channel to the other.

Customers, however, don’t look at a brand as a collection of siloed channels.  Customers do not care about organizational charts.  They expect a consistent customer experience regardless of channels.  Customers expect cross-channel consistency.

If senior management has defined the customer experience organization-wide, the researcher’s role in coordinating research tools is much easier.  If management has not defined the customer experience organization-wide, the researcher’s role is nearly impossible.

The first step in defining the customer experience organization-wide is writing a clear customer experience mission statement which clearly communicates how customers should experience the brand, and how management wants customers to feel as a result of the experience.  Next, the customer experience should be defined in terms of broad dimensions and specific attributes which constitute the desired customer experience and emotional reaction to the brand.

For illustration, let’s consider the following example:

A bank may define their customer experience with four broad dimensions, which can be described as:

  1. Relationship Building
  2. Sales Process
  3. Product Knowledge
  4. Customer Knowledge

Next, the customer experience leadership of this bank must define each of these broad dimensions in terms of specific attributes which combine to make up the dimensions.  For example, each of the above four dimensions may be defined by the following attributes:

Dimension Attributes
Relationship Building Establish trust

Commitment to customer needs

Perceived as trusted advisor

Sales Process Referral to appropriate partner
Product Knowledge Understanding of a range of products

Understand features and benefits

Explain benefits in ways that are meaningful to customers

Customer Knowledge Needs analysis

Once each of the above dimensions has been defined in terms of specific attributes, the next step in translating the customer experience definition to action is to define a set of empirical behaviors which support each attribute.

For example, establishing trust is an attribute of relationship building.

Relationship Building –> Establish Trust

Under this example, a set of behaviors is defined which are designed to establish trust.  For example, these behaviors may be:

  • Maintain eye contact
  • Speak clearly
  • Maintain smile
  • Thank for business
  • Ask “What else may we assist you with today?”
  • Encourage future business

Now, each of these six behaviors is mapped across each channel.  So, for example, this bank may map these behaviors across channels as follows:

Behaviors Which Support Establishing Trust:

New Accounts Teller Contact Center
Maintain eye contact Maintain eye contact
Speak clearly Speak clearly Speak clearly
Maintain smile Maintain smile Sound as if they were smiling through the phone
Thank for business Thank for business Thank for business
Ask “What else may we assist you with today?” Ask “What else may we assist you with today?” Ask “What else they could do to assist you today?”
Encourage future business Encourage future business Encourage future business

Repeating this process of mapping behaviors to each of the attributes will produce a complete list of employee behaviors appropriate to each channel in support of management’s broader customer experience objectives.

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Mystery Shopping Gap Analysis: Identify Service Attributes with Highest Potential for ROI

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?”

For example:

  • 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.

Interpretation

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.

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Key Driver Analysis: Drive Your Core Customer Experience Objectives

Mystery shopping not in pursuit of an overall customer experience objective may be interesting, it may be successful in motivating certain service behaviors, but ultimately will fail in maximizing return on investment.

Consider the following proposition:

“Every time a customer interacts with a brand, the customer learns something about the brand, and based on what they learn, adjust their behavior in either profitable or unprofitable ways.”

These behavioral adjustments could be profitable: positive word of mouth, complain less, less expensive channel use, increased wallet share, loyalty, or purchase intent, etc..  Or…these adjustments could be unprofitable: negative word of mouth, more complaints, decreased wallet share, purchase intent or loyalty, etc.

There is power in this proposition.  Understanding it is the key to managing the customer experience in a profitable way.  Unlocking this power gives managers a clear objective for the customer experience in terms of what you want the customer to learn from it and react to it.  Ultimately, it becomes a guidepost for all aspects of customer experience management – including customer experience measurement.

In designing customer experience measurement tools, ask yourself:

  • What is the overall objective of the customer experience?
  • How do you want the customer to feel as a result of the experience?
  • How do you want the customer to act as a result of the experience?

For example:

  • 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?

The answer to the above series of questions will become the guideposts for designing a customer experience which will achieve your objectives.

The answers to the above questions will serve as a basis for evaluating the customer experience against your objectives.   In research terms, the answer to this question or questions will become the dependent variable(s) of your customer experience research – the variables influenced or dependent on the specific attributes of the customer experience.

For example, let’s assume your objective of the customer experience is increased return intent.  As part of a mystery shopping program, ask a question designed to capture return intent – a question like, “Had this been an actual visit, how did the experience during this shop influence your intent to return for another transaction?”  This is the dependent variable.

The next step is to determine the relationship between every service behavior or attribute and the dependent variable (return intent).  The strength of this relationship is a measure of the importance of each behavior or attribute in terms of driving return intent.  It provides a basis from which to make informed decisions as to which behaviors or attributes deserve more investment in terms of training, incentives, and rewards.

This is what Kinesis calls Key Driver Analysis, an analysis technique designed to identify service behaviors and attributes which are key drivers of your key objectives of the customer experience.   In the end, providing an informed basis for which to make decisions about investments in the customer experience.
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Mystery Shop Sample Size and Customer Experience Variation

Mystery shop programs measure human interactions; interactions with other humans and increasingly human interactions with automated machines.  Given that humans are on one or both sides of the equation, it is not surprising that variation in the customer experience exists.

When designing a mystery shop program, a central decision is the number of shops to deploy.  This decision is dependent on a number of issues including: desired reliability, number of customer interactions, and the budgetary resources available for the program.  However, one additional and very important consideration, which frankly doesn’t get much attention, is the amount of variation expected in the customer experience to be measured.

The level of variation in the customer experience is an important consideration.  Consistent customer experience processes require less mystery shops than those with a high degree of variation.  To illustrate this, consider the following:

Assume a customer experience process is 100% consistent with zero variation from experience to experience.  Such a process would require only one shop to accurately describe the experience as a whole.  Now, consider a customer experience process with an infinite level of variation in the experience.  Such a process would require far more than one shop.   In fact, assuming an infinite level of variation, 400 shops would be required to achieve a margin of error of plus or minus five percent.

Obviously, the variation of most customer experience processes reside somewhere between perfect consistency and infinite variation. So how do managers determine the level of variation in their process?  The answer to this question will probably be more qualitative than quantitative.   Ask yourself:

  • Do you have a set of standardized customer experience expectations?
  • Are these expectations clearly communicated to employees?
  • Other than mystery shopping, do you have any processes in place to monitor the customer experience? If so, are the results of these monitoring tools consistent from month-to-month or quarter-to-quarter?

To make it easy, I always ask new clients to give a qualitative estimate of the level of variation in their customer experience from: high, medium to low.  The answer to this question will also be considered along with the level of statistical reliability desired and budgetary resources available for the program in determining the appropriate number of shops.

So – ask yourself; how much variation can we expect in our customer experience?

 

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Two Questions….Lots of Insights: Turn Customer Experience Observations into Valuable Insight

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:

Click Here: Maximizing Response Rates: Get Respondents to Complete the Survey

Click Here: Keys to Customer Experience Research Success – Start with the Objectives

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.

Loyalty Question

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.

Engagement Question

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.

Methodology

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.

Loyalty Engagement_2

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.

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Maximizing Response Rates: Get Respondents to Complete the Survey

Previously we discussed ways researchers can increase the likelihood of respondents opening an email survey invitation. Additionally, in a subsequent post we discussed how to get respondents to actually click on the survey link and participate in the survey.

This post is a discussion of ways to keep respondents motivated to complete the entire survey once they have entered it.

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At its core, the key to completion rates is an easy to complete and credible survey that delivers on all promises offered in the invitation email.

Survey Length

From time to time various service providers of mine send me a survey invite, and I’m often surprised how many of them impose upon me, their customer, to complete a 30 or 40 minute survey.  First of all, they never disclose the survey length in advance, which communicates a complete lack of respect for my time.  In addition to just plain being an imposition, it is also a bad research practice.  Ten minutes into the survey I’m either pressed for time, frustrated, or just plain bored, and either exit the survey or frivolously complete the remaining questions without any real consideration of my opinions on the questions they are asking – completely undermining the reliability of my responses.  This is just simply a bad research practice, in addition to being inconsiderate of the end customer’s time.

We recommend keeping survey length short, no more than 10 to 12 minutes – in some cases such as a post-transaction survey – 5 minutes.

If research objectives require a long survey, rather than impose a ridiculously long survey on your customers producing frivolous results, break a 30 – 40 minute survey into two, or better yet, three parts fielding each part to a portion of your targeted sample frame.

Additionally, skip logic should be employed to avoid asking questions that are not applicable to a given respondent, thus decreasing the volume of questions you present to the end customer.

Finally, include a progress bar to keep respondents informed of how far along they are on the survey.

Ease of Completion

The last thing you want respondents feeling when they complete your survey is frustration.  First of all, if the sample frame is made up of your customers, the primary thing you are accomplishing is upsetting your customers and damaging your brand.  And also, creating bad research results because frustrated respondents are not in the proper mindset to give you well considered answers.

Frustration can come from awkward design, question wording, poor programming, and insufficient response choices.  Survey wording and vocabulary should be simple and jargon free, response choices should be comprehensive, and of course the survey programming should be thoroughly proofed and pretested.

Pretesting is a process where the survey is prefielded to a portion of the sample frame to test how they respond to the survey, significant portions of the questionnaire unanswered or a high volume of “other” or “none of the above” responses could signal trouble with survey design.

Convenience

Survey completion should be easy.  Survey entry should work across a variety platforms, browsers and devices.

Additionally, respondents should be allowed to take the survey on their own time, even leaving the survey while saving their answers to date and allowing reentry when it is more convenient for them.

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Maximizing Response Rates: Get Respondents to Start the Survey

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It is incumbent on researchers fielding self-administered surveys to maximize response rates.  This reduces the potential for response bias, where the survey results may not accurately reflect the opinions of the entire population of targeted respondents. Previously we discussed ways researchers can increase the likelihood of respondents opening an email survey invitation.  This post addresses how to get respondents to actually click on the survey link and participate in the survey.

Make the Invite Easy to Read

Don’t bury the lead.   The opening sentence must capture the respondent’s attention and make the investment in effort to read the invitation.   Keep in mind most people skim emails.  Keep text of the invitation short, paying close attention to paragraph length.  The email should be easy to skim.

Give a Reward

Offering respondents a reward for participation is an excellent way to motivate participation.  Tangible incentives like a drawing, coupon, or gift card, if appropriate and within the budget, are excellent tools to maximize response rates.   However, rewards do not necessarily need to be tangible.  Intangible rewards can also prove to be excellent motivators.  People, particularly customers who they have a relationship with the brand, want to be helpful.  Expressing the importance of their option, and communicating how the brand will use the survey to improve its offering to customers like the respondent is an excellent avenue to leverage intangible rewards to motivate participation.

Survey Length

Intangible rewards are often sufficient if the respondent’s cost to participate in the survey is minimal.  Perhaps the largest cost to a potential respondent is the time required to complete the survey.  Give them an accurate estimate of the time it takes to complete the survey – and keep it short.  We recommend no more than 10 minutes, more preferably five to six.   If the research objectives require a longer survey instrument, break the survey into two or three shorter surveys and deliver them separately to different targeted respondents.  Do not field excessively long surveys or mis-quote the estimated time to complete the survey – it is rude to impose on your respondents not to mention disastrous to your participation rates – and it’s unethical to mis-quote the survey length.  As with getting the participants to open the email – creditability plays a critical role in getting them to click on the survey.

Credibility

One of the best ways to garner credibility with the survey invite is to assure the participant confidentiality.  This is particularly important for customer surveys, where the customers interact commonly with employees.  For example, a community bank where customers may interact with bank employees not only in the context of banking but broadly in the community, must ensure customers that their survey response will be kept strictly confidential.

Personalizing the survey with appropriate merge fields is also an excellent way to garner credibility.

Survey Entry

Make it as easy as possible for the participant to enter the survey.  Program a link to the survey, and make sure it is both visible and presented early in the survey.  Again, most people skim the contents of emails, so place the link in the top 1/3 of the email and make it clear that it is a link to enter the survey.

In designing survey invitations, remember to write short, concise, easy to read emails that both leverage respondent’s reward centers (tangible or intangible), and credibly estimate the short time required to complete the survey.  This approach will help maximize response rates and avoid some of the pitfalls of response bias. Click here for the next post in this series in prompting respondents to complete the survey.

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Mystery Shop Key Driver Analysis

Best in class mystery shop programs provide managers a means of applying coaching, training, incentives, and other motivational tools directly on the sales and service behaviors that matter most in terms of driving the desired customer experience outcome.  One tool to identify which sales and service behaviors are most important is Key Driver Analysis.

Key Driver Analysis determines the relationship between specific behaviors and a desired outcome.  For most brands and industries, the desired outcomes are purchase intent or return intent (customer loyalty).  This analytical tool helps mangers identify and reinforce sales and service behaviors which drive sales or loyalty – behaviors that matter.

As with all research, it is a best practice to anticipate the analysis when designing a mystery shop program.  In anticipating the analytical needs of Key Driver Analysis identify what specific desired outcome you want from the customer as a result of the experience.

  • Do you want the customer to purchase something?
  • Do you want them return for another purchase?

The answer to these questions will anticipate the analysis and build in mechanisms for Key Driver Analysis to identify which behaviors are more important in driving this desired outcome – which behaviors matter most.

Next, ask shoppers if they had been an actual customer, how the experience influenced their return intent.  Group shops by positive and negative return intent to identify how mystery shops with positive return intent differ from those with negative.  This yields a ranking of the importance of each behavior by the strength of its relationship to return intent.

Additionally, pair the return intent rating with a follow-up question asking, why the shopper rated their return intent as they did.  The responses to this question should be grouped and classified into similar themes, and grouped by the return intent rating described above.  The result of this analysis produces a qualitative determination of what sales and service practices drive return intent.

Finally, Key Driver Analysis produces a means to identify which behaviors have the highest potential for return on investment in terms of driving return intent.  This is achieved by comparing the importance of each behavior (as defined above) and its performance (the frequency in which it is observed).  Mapping this comparison in a quadrant chart, provides a means for identifying behaviors with relatively high importance and low performance – behaviors which will yield the highest potential for return on investment in terms of driving return intent.

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Behaviors with the highest potential for return on investment can then be inserted into a feedback loop into the mystery shop scoring methodology by informing decisions with respect to weighting specific mystery shop questions, assigning more weight to behaviors with the highest potential for return on investment.

Employing Key Driver Analysis gives managers a means of focusing training, coaching, incentives, and other motivational tools directly on the sales and service behaviors that will produce the largest return on investment. See the attached post for further discussion of mystery shop scoring.

Click Here for Mystery Shopping Best Practices

 

 

Click Here for Mystery Shopping Best Practices

 

 

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Image/Perception: A Mirror to Your Brand

In an earlier post we discussed including a loyalty proxy as part of your brand perception research.

Establishing and measuring loyalty proxies is important, but your brand perception research should not end there.  Brand perception research should produce insight beyond loyalty.  It should determine the extent to which customers impressions of the brand are aligned with your desired brand image.  Additionally, perceptions of the brand among the most loyal and engaged customers should be compared to those who are deemed less loyal or engaged to identify opportunities to improve perceptions of the brand among customers at either risk of defection, or not fully engaged

In a subsequent post, we will address ways to measure engagement/wallet share.

Brand Definition

The first step in measuring your brand perception is to define your desired brand.  Ask yourself: if your brand were a person, what personality characteristics would you like your customers to describe you with?  What adjectives would you want used to describe your brand?

In addition to describing your brand personality with adjectives, come up with a list of statements that describe your desired personality.  For example, you may include statements such as:

  • We are easy to do business with.
  • We are knowledgeable.
  • We are like a trusted friend.
  • We are interested in customers as people, not just the bottom line.
  • We are committed to the community.

So, we defined the brand in terms of personality adjectives and statements.  Both will be used in designing the survey instrument.

The Survey Instrument

Unaided Top-of Mind

The first step in the survey instrument, is asking customers for their unaided top-of-mind perceptions of the brand.  This will uncover the first thing that comes to customers’ minds about your brand prior to the effects of any bias introduced by the research instrument itself.  There are many ways to capture unaided top-of-mind impressions.  We like a simple approach, where you ask the customer for the one word that they would use to describe the company.  This research question will yield a list adjectives that can be quantified by frequency and used to determine the extent to which customers top-of-mind impressions match the desired brand image.

Aided Image

After we have defined top of mind impressions of the brand, we recommend comparing brand perception to your desired brand identified in the brand definition exercise described above.  This is a fairly simple process of presenting the customers with your list of brand personality adjectives and asking the customer which of these adjectives would the customer use to describe the company.

In a much earlier post we discussed using word clouds to interpret brand personality adjectives.

The next step in comparing the reality of brand perception to your branding goals is to ask the customers to what extent do they agree with each of the brand personality statements described above.  As with the list of adjectives, this holds a mirror up to your desired image and measures the extent to which customers agree that you are perceived in the manner that you want to be.

Identifying Attributes with the Most ROI Potential

The value of these brand perception statements goes beyond just evaluating if you live up to your brand.  Used in conjunction with the loyalty proxies discussed in the previous post, they become tools to determine which of these brand personality attributes will yield the most ROI in terms of improving customer loyalty.  This is achieved with a simple cross-tabulation of agreement with these statements by customer loyalty segment.  For example, if NPS is used as the loyalty proxy, then we simply compare agreement to these statements from promoters to detractors to determine which attributes have the largest gaps between promoters and detractors.  Those with the largest gaps have the most ROI potential in terms of customer loyalty.

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