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Measure and Motivate the Right Contact Center Agent Behaviors

Increasingly banks must operate in a multi-channel environment.  While the changing role of the branch, combined with automated channels such as online and mobile, are getting a lot of attention, there remains a key role for the contact center in delivering an effective customer experience.  Central to this key role is designing an effective customer experience, comprised of the right sales and service behaviors – those which influence customer attitudes and behaviors in a profitable way yielding the most return on investment.

To provide direction with respect to what sales and service behaviors will yield the most return on investment, Kinesis conducted a series of mystery shops to identify which sales and service behaviors have the most influence on purchase intent. In addition to observing specific sales and service behaviors, mystery shoppers were also asked to rate how the call would have influenced their purchase intent if they had been a real customer. This purchase intent rating was then used as means of calculating the strength of the relationship between each behavior and purchase intent.

To determine the relationship between these service attributes and purchase intent, the data for these different studies was cross-tabulated by the purchase intent rating and subjected to significance testing. [i]

When the percentage of calls in which purchase intent significantly increased is tested against the percentage of calls where purchase intent significantly decreased, nearly all the sales and service attributes are statistically significant at or above a 95% confidence level.

 

Significantly Increased Significantly Decreased Test Statistic
Product knowledge 98% 35% 9.6
Explanations easy to understand 99% 45% 9.0
When thanked, respond graciously 98% 42% 8.5
Friendly demeanor / pleasant voice 100% 60% 8.4
Express appreciation for interest / thank you for business 92% 20% 8.3
Listen attentively 99% 60% 7.3
Ask probing questions 79% 10% 6.4
Offer further assistance 85% 25% 6.2
Speak clearly and avoid bank jargon 98% 68% 5.8
Listen attentively to your needs 80% 25% 5.3
Mention other bank product 99% 75% 5.3
Clear Greeting 95% 60% 5.1
Invite you to visit branch 64% 10% 4.6
Explain the value of banking with bank 57% 5% 4.4
Offer to mail material / mention website 66% 20% 4.3
Ask your name 68% 25% 3.8
Ask for your business / close the sale 57% 21% 2.9
Avoid interrupting 100% 95% 2.9
If no one available to assist you, offered options 100% 0% 2.2
Professional greeting 98% 89% 1.9

 

The differences between the highest and lowest purchase intent for product knowledge and ease to understanding explanations are the most significant, while a professional greeting is the least significant.

Dividing these behaviors into rough quartiles and comparing them side-by-side, reveals some interesting observations:

 

 

Quartile I

Product knowledge

Explanations easy to understand

When thanked, respond graciously

Friendly demeanor / pleasant voice

Express appreciation for interest / thank you for business

 

Quartile II

Listen attentively

Ask probing questions

Offer further assistance

Speak clearly and avoid bank jargon

When thanked, employee respond graciously

 

Quartile III

Listen attentively to your needs

Mention other bank product

Clear greeting

Invite you to visit branch

Explain the value of banking with bank

Offer to mail material / mention website

 

Quartile IV

Ask your name

Ask for your business / close the sale

Avoid interrupting

If no one available to assist you, offered options

Professional greeting

 

The attributes with the most significant differences between high and low purchase intent ratings appear to be those associated with reliability and empathy.  It appears mystery shoppers valued such “core” attributes as product knowledge or interest/enthusiasm for the customer.  They seem to be less concerned with more peripheral service attributes, such as asking for names, etc.  Influencing purchase intent is not as simple as merely using the customer’s name or answering the phone within a short period of time.  Rather it is a much more challenging undertaking of being competent in your job and having the customer’s best interests at heart.

[i] Significance testing determines if any differences observed are the result of actual differences in the populations measured rather than the result of normal variation.  Without getting into too much detail, significance testing produces a test statistic to determine the probability that differences observed are statistically significant.  A test statistic above 1.96 equates to a 95% confidence level, which means there is a 95% chance any differences observed are the result of actual differences in the populations measured rather than normal variation.  For all practical purposes a test statistic over 3.1 means there is 100% chance the differences observed are statistically significant (although in reality the probability never reaches 100%).  Finally, in interpreting the following analysis, it is important note that test statistics are not lineal.  A test statistic of two is not twice as significant as a test statistic of one.  The influence on significance decreases as the test statistic increases.  However, the test statistic does give us an opportunity to rank the service attributes by their statistical significance.


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Changes in Word of Mouth Advertising Based on the Customer Experience – Part 2

Previously we observed changes in customer purchase behavior based on the customer experience. 

Every time a company and a customer interact, the customer learns something about the company, and adjusts their behavior based on what they learn.

To explore this proposition, Kinesis conducted a survey of 500 consumers asking them to recall an experience with any provider that they found to be particularly positive or negative, and determined how these customer experiences influenced customer behavior.

Here is how respondents told us they changed their behavior based on the experience:

Change in Cust Behavior

 

This post specifically addresses positive word of mouth as a result of the experience.

Respondents shared positive word of mouth a median 4.3 times as a result of their positive experience, compared to negative experiences, which were shared about 20% more often (median 5.2 times).  In fact, they were more likely to share negative word of mouth across all mediums:

Word of Mouth as Result of Experience

Positive Experiences

Negative Experiences

Friend or family (Excluding Online or Social Media)

69%

80%

Coworkers (Excluding Online or Social Media)

42%

54%

Online Social Media

28%

47%

Online Reviews

20%

33%

Customers are far more likely to share negative experience using online mediums.  While they are about 1.2 times more likely to share a negative experience with a relative, friend or coworker via an off line medium, they are 1.7 times more likely to share negative experiences over positive via online mediums.

Again, every time a company and a customer interact, the customer learns something about the company, and changes their behavior based on what they learn.  And, as this study shows, they certainly will share this experience with others.   But what about the recipients of this word of mouth advertizing?  How does one customer’s experience influence the behavior of others?

Approximately 90% of respondents said their purchase decisions were influenced positively (93%) or negatively (85%) by social media or word of mouth reviews.

With customer trust at an all time low,  and social media providing a much more far reaching medium of person to person communication, positive word of mouth is becoming far more important in terms of defining the brand.  Increasingly social media is becoming the media.  With 9 out of 10 potential customers saying their purchase decisions are influenced reviews of others, it is increasing important that managers manage their customer experience to support and reinforce the brand.


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Changes in Purchase Behavior Based on the Customer Experience – Part 1

Every time a company and a customer interact, the customer learns something about the company, and adjusts their behavior based on what they learn.

To explore this proposition, Kinesis conducted a survey of 500 consumers asking them to recall an experience with any provider that they found to be particularly positive or negative, and determined how these customer experiences influenced customer behavior.

When asked to characterize the cause of the positive or negative experience, these customers’ descriptions were grouped into four common themes that mirrored each other regardless of whether the experience was positive or negative.  The most common themes for both experiences were: speed of service, pleasantness of personnel, efficiency of service, and the success of the outcome.

Causes of Positive & Negative Experiences

Positive Experiences Negative Experiences
Speed of Service/ Problem Resolution 72% 69%
Pleasantness of Personnel 70% 63%
Efficiency of Service/ Not Passed Around to Multiple People 60% 71%
Outcome Successful/ Problem Resolved/ Expectations Met 55% 49%

The speed of service was cited with about the same frequency (7 out of 10 cases) as a cause of the experience being positive or negative.  Pleasantness of personnel was mentioned 70% of the time as a driver of positive experiences compared to 63% for negative.  Efficiency of service (or lack thereof) was more commonly cited as a reason for the experience being negative (71%) compared to positive (60%).  The fourth most common theme mentioned as a reason for the success or failure of the customer experience is the successful outcome of the experience itself (55% for positive experience, 49% for negative).

Again, every time a company and a customer interact, the customer learns something about the company, and adjusts their behavior based on what they learn.  So…how did these experiences (positive or negative) influence customer behavior?

Here is how respondents told us they changed their behavior based on the experience:

Changes in Customer Behavior Based on Experience

Positive Experiences Negative Experiences
Change in purchase behavior (Buy more or less) 54% 57%
Told others (Positive or negative) 36% 43%
Considered change in purchase behavior 32% 38%
No change 14% 5%

Over half of the respondents said they changed their purchase behavior as a result of the experience, 54% of the customers recalling a positive experience told us they purchased more from the provider as a result of the positive experience, while 57% told us they purchased less as a result of the negative experience.

Furthermore, about a third of the respondents told us they considered a change in purchase behavior as a result of the experience; 32% considered purchasing more as a result of the positive experience, and 38% considered purchasing less as a result of a negative experience.

Finally, roughly four out of ten told others of the experience.  Thirty-six percent of participants told us they gave positive word of mouth as a result of the positive experience, while 43% gave negative word of mouth as a result of the negative experience.

Again, every time a company and a customer interact, the customer learns something about the company, and changes their behavior based on what they learn.  The two primary ways customers change their behaviors based on the customer experience is both their own purchase behavior and sharing the experience with others.

The next post in this series explores how customers share the experience with others and the ultimate influence this word of mouth advertising has on others.


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Not All Service Attributes Are Equal: Ranking Service Attributes by Their Correlation to Loyalty

Research without a call to action may be informative, but not very useful.  One way to build a call to action element into your customer experience research is to add a measure of customer loyalty.  Loyalty can serve as a basis for evaluating which elements of the service mix are most important in terms of driving customer loyalty, and as result, have more potential ROI.

Measuring customer loyalty, however, in the context of a survey is difficult.   Surveys best measure attitudes and perceptions.  Loyalty is a behavior.  Kinesis has had success with a model for estimating customer loyalty based on two measurements:

  1. Promoter: This is measured with the likelihood of referral to a friend relative or colleague, using a numeric scale.
  2. Trust: Trust is measured by capturing agreement with the statement, “the company cares about me, not just the bottom line.” Again answered in a numeric scale.

These two measures are combined together to calculate a loyalty index, which visually is the linear distance of the plot of these two measurements from the highest possible value for each scale (cases where promoter and trust received the highest possible rating).

Trust Promoter Plot

Mathematically, this index can be calculated with the following equation:

Loyalty Index Equation

Where:

T = Trust rating
P = Promoter rating
ST = Number of points on the Trust scale
SP = Number of points on the Promoter scale

Note this index measures the distance from the ideal or most loyal state.  Lower values estimate stronger loyalty.

Calculating a loyalty index has value, but limited utility.  A loyalty index alone does not give management much direction upon which to take action.  One strategy to increase the actionably of the research is to use this index as a means to identify the service attributes that drive customer loyalty.  Not all service attributes are equal; some play a larger role than others in driving customer loyalty.

So…how does the research determine an attribute’s role or relationship to customer loyalty?  One tool is to capture satisfaction ratings of specific service attributes and determine their correlation to the loyalty statistic.  The Pearson correlation coefficient is a measure of the strength of a linear association between two variables.

The following table contains a hypothetical list of service attributes and their correlation to the loyalty index.  Note lower values of the loyalty index indicate stronger loyalty, so the Pearson correlations to the attribute satisfaction ratings are negative.  The closer the correlation is to -1 equates to a stronger relationship to loyalty.

Pearson Correlation to Loyalty Index
Perform services as promised/right the first time -0.62
Show interest in solving problems -0.61
Employee efficiency -0.58
Problems resolved quickly -0.56
Employee courtesy -0.56
Willingness to help/answer questions -0.55
Perform services on time -0.54
Employee recommendations -0.53
Employees instill confidence in customer -0.52
Questioning to understand needs -0.45
Appearance/cleanliness of personnel -0.42
Knowledgeable employees/job knowledge -0.41
Appearance/cleanliness of physical facilities -0.37

As this table illustrates, the service attributes with the strongest correlation to the loyalty index are: perform services as promised/right the first time (-0.62), show interest in solving problems (-0.61), and employee efficiency (-0.58).  Under this hypothetical example, the hypothetical managers can conclude that of the attributes measured, these three are the strongest drivers of customer loyalty.  They now can use this research to make informed judgments as to where investments in the service mix will yield the most ROI.

Correlating service attributes to loyalty is not the end of the analysis; the next step is to further put this research to action by layering in the overall performance of each attribute relative to its relationship to loyalty.

Next Article: Using Gap Analysis to Put Loyalty Index into Action


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Using Promoter and Trust Measurements to Calculate a Customer Loyalty Index

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.  Depending on the industry, a small increase in customer loyalty (5%) translates into a 25% – 85% increase in profits.[1]

Customer loyalty is driven by the entire relationship with the company.  Image, positioning, products, price, cost of switching, and service all form a value equation each customer applies in their continuous decision to remain loyal.

Measuring customer loyalty, however, in the context of a survey is difficult.   Surveys best measure attitudes and perceptions. Loyalty is a behavior based on rational decisions customers make continually through the lifecycle of their relationship with the company.  Customer experience researchers therefore need to find a proxy measurement to determine customer loyalty.  One 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 the firm, or a new customer may be very satisfied and highly loyal.

Kinesis has had success with a model for estimating customer loyalty based on two measurements: likelihood of referral and customer advocacy.  Likelihood of referral captures a measurement of the customer’s likelihood to refer the company to a friend, relative or colleague.  It stands to reason, if one is going to refer others to the bank, they will remain loyal as well.  These promoters are putting their reputational risk on the line founded on a feeling of loyalty and trust.  This concept of trust is perhaps more evident in the second measurement: customer advocacy.  Customer advocacy is captured by measuring agreement with the following statement: “The Company cares about me, not just the bottom line.”  Customers who agree with this statement trust the firm to do right by them, and will not subjugate their best interests to profits.  Customers who trust the company to do the right thing are more likely to remain loyal.   Trust Promoter Plot

Kinesis uses likelihood of referral, hereafter labeled “Promoter” and customer advocacy, hereafter labeled “Trust” to calculate an estimate of the customer’s loyalty.  Imagine a plot where each customer’s promoter score is plotted along one axis and the trust score plotted along the other.  Using this plot we can calculate the linear distance between the perfect state of the highest possible trust and promoter ratings.  This distance yields a loyalty estimate, where the lower the value, the higher the estimate of loyalty – low values are good.  The mathematical equation for this distance is as follows:

 Loyalty Index Equation

Where:

  • T = Trust rating
  • P = Promoter rating
  • ST = Number of points on the Trust scale
  • SP = Number of points on the Promoter scale
 Kinesis’ experience plotting these indices, across a variety of scales, typically yields five zones of loyalty defined as follows:Loyalty Ranges
  1. Strongest Loyalty: The strongest zone of loyalty contains cases where both the Trust and Promoter attributes received the highest possible rating.
  2. Strong Loyalty: The next zone is where the loyalty index lies within 35% of both the Trust and Loyalty axis.
  3. Moderate Loyalty: The zone of moderate loyalty is where the index lies within 60% of the highest possible Trust and Promoter ratings.
  4. Weak Loyalty: The zone of weak loyalty lies within 90% of the highest possible Trust and Promoter ratings.
  5. Weakest Loyalty: The zone with the weakest loyalty are cases where one or both of the Trust and Promoter scores are less than 90% of the highest possible for Trust and Promoter..

Given that for many industries the business attribute with the highest correlation to profitability is customer loyalty; it is incumbent upon survey researchers to gather a measure of customer loyalty as part of their customer experience measurement.  Kinesis’ approach of calculating a loyalty index based on “would recommend” and “customer advocacy” ratings has proven to be a useful tool for segmenting customers by an estimate of their loyalty.  The next step in this analysis is to put this segmentation to work identifying which service attributes will yield the most ROI in term of driving customer loyalty.

Next Article: Using Gap Analysis to Put Loyalty Index into Action


[1] Heskett, Sasser, and Schlesinger The Service Profit Chain, 1997, New York: The Free Press, p 21


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Call to Action: Using Gap Analysis to Put Loyalty Index into Action

For most service industries the business attribute with the highest correlation to profitability is customer loyalty. It is, therefore, very important to gather a measurement of customer loyalty. However, simply calculating a loyalty index is not enough. Estimating customer loyalty is important, and an obvious first step; however, alone – without any context – is not very useful.

What’s needed is a methodology to transition research into action, and identify clear paths to maximize return on investments in the customer experience. What managers need is a tool to help them prioritize the service behaviors on which to focus improvement efforts. One such tool is an analytical technique called Gap Analysis.

Gap Analysis compares performance of individual service attributes relative to their importance, providing a frame of reference for prioritizing which areas require attention and resources.

To perform Gap Analysis, each service attribute measured is plotted across two axes. The first axis is the performance axis. On this axis the performance of each attribute is plotted. The second axis is the importance axis. Each attribute is assigned an importance rating based on its correlation to the loyalty index. Service attributes with strong correlations to loyalty are deemed more important and service attributes with low correlations are deemed less important.

This two-axis plot creates four quadrants:

Gap Analysis Loyalty

  1. Quadrant 1: Areas with high correlations to loyalty and low performance.  These service attributes are where there is high potential of realizing return on investments in improving performance.
  2. Quadrant 2: Areas with high correlations to loyalty and high performance.  These are service attributes to maintain.
  3. Quadrant 3: Areas with low correlations to loyalty and low performance.  These are service attributes to address if resources are available.
  4. Quadrant 4: Areas with low correlations to loyalty and high performance.  These are service attributes which require no real attention as their performance exceeds their importance.

To illustrate this analysis methodology, consider the example below with the following service attributes:

Performance

Loyalty Correlation

Appearance/cleanliness of physical facilities

4.9

0.37

Appearance/cleanliness of personnel

4.8

0.42

Perform services as promised/right the first time

4.8

0.62

Perform services on time

4.9

0.54

Show interest in solving problems

4.9

0.61

Willingness to help/answer questions

4.7

0.55

Problems resolved quickly

4.4

0.56

Knowledgeable employees/job knowledge

4.6

0.41

Employees instill confidence in customer

4.7

0.52

Employee efficiency

4.7

0.58

Employee courtesy

4.9

0.56

Employee recommendations

4.8

0.53

Questioning to understand needs

4.9

0.45

Plotted on the above quadrant chart, they yield the following chart:

Gap Example

In this example, problems resolved quickly, employee efficiency, willingness to help, employees instill confidence are the four behaviors with relatively high correlations to the loyalty index and relatively low performance  As a result, improvements in these attributes will yield the highest potential for ROI in terms of improving customer loyalty.

Using gap analysis, managers now have a valuable indicator to identify service attributes to focus improvement efforts on.  Directing attention to the attributes in Quadrant I should have the highest likelihood realizing ROI in terms of the customer experience improving purchase intent.

Related Article: Using Promoter and Trust Measurements to Calculate a Customer Loyalty Index


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It’s Personal: Drivers of Positive Impressions of the Branch Experience

What impresses customers positively as a result of a visit to your branch?

To answer this and other questions, Kinesis conducted research into the efficacy of the branch sales process and identified several service and sales attributes that drive purchase intent.  (See the insert below of a description of the methodology).

In our observational research of 100 retail banking presentations, mystery shoppers were asked to describe what impressed them positively as a result of the visit to the branch.   Excluding the branch atmosphere, the five most common themes contained in these open-ended comments were:

  • Attentive to Needs/ Interest in Helping/ Personalized Service,
  • Professional/ Courteous/ Not Pushy, Positive Greeting,
  • Friendly Employees, and
  • Rep. Product Knowledge/ Informative/ Confidence in Rep.

In an effort to understand the relative importance of these behaviors in driving purchase intent, shoppers were asked to rate their purchase intent, as a result of the presentation, as if they had been an actual customer.  Shops were then grouped into those with positive and negative purchase intent and compared to each other.

Of these drivers of a positive impression, three have positive relationships to purchase intent – they tend to be present with greater frequency in shops with positive purchase intent compared to those with negative purchase intent.

 

Reason for Positive Purchase Intent

Relative Frequency Positive to Negative Purchase Intent

Rep. Product Knowledge/ Informative/ Confidence in Rep.

2.7

Attentive to Needs/ Interest in Helping/ Personalized Service

2.5

Friendly Employee

2.3

The banker’s product knowledge was present 2.7 times more frequent in shops with positive purchase intent relative to shops with negative purchase intent.  Similarly, attention to needs and personalized service was present 2.5 times more in shops with positive purchase intent compared to those with negative purchase intent.  Finally, shoppers were 2.3 times more likely to cite the friendliness of the bankers in shops with positive purchase intent relative to negative.

The observations contained within this research are not rocket science.  What customers want, what drives purchase intent, is personal: attention to needs, interest in helping, personalized service, professional, courteous and friendly encounters.

Methodology

To evaluate the state of the in-branch sales process, Kinesis mystery shopped 100 branches among five banks with significant North American footprints.  Among the objectives of the study were to:

1) Define the sales process among different institutions.

2) Evaluate the effectiveness of specific sales behaviors.

Shoppers were asked a mixture of closed-ended questions to evaluate the presence or frequency of specific behaviors, and open-ended questions to gather the qualitative impressions of these behaviors on the shoppers – in short the how and why behind how the shopper felt.  Finally, to provide a basis to evaluate the effectiveness of each sales behavior, shoppers were asked to rate their purchase intent as a result of the visit. This purchase intent rating was then used as a means of evaluating what behaviors tend to be present when positive purchase intent is reported as opposed to negative purchase intent.


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