Implications of CX Consistency for Researchers – Part 2 – Intra-Channel Consistency
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.
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.
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).
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.
Calculating ROI: A Simple Intuitive Customer Experience ROI Calculator
Calculating ROI on the customer experience typically takes the blind faith approach, where ROI on customer service is considered a given, and the sophisticated approach, where predictive models explain the links between service attributes, customer satisfaction and profitability. Such models can, in fact, be valuable as a means for understanding the associations among different service and profit factors. They can also provide insight into how service attributes interact with each other to influence customer perceptions. A major drawback, however, is that these models tend to have too many moving parts to function as a practical, day-to-day business tool, give the appearance of being far more precise than they actually are, and may be too sophisticated for some audiences.
Sometime managers need a simpler, more intuitive approach to estimating ROI on customer service.
First let me suggest the proposition that every time a company and service provider interact the customer learns something positive or negative and adjusts their behavior, again positive or negative based on what they learn. This is a behavioral approach to managing the customer experience, that by managing customer behaviors in profitable ways, service providers can maximize return on investment in the customer experience.
Using this behavior approach as a model, it possible to construct a simple intuitive ROI estimate of the customer experience.
List customer behaviors with financial implications
The first step in this methodology is to list all customer behaviors that directly drive revenues or costs. Ask yourself, “What specifically, do we want customers to do more or less of?” Don’t include attitudes, such as satisfaction, or feelings such as delight – only include empirically measureable behaviors such as purchase more, purchase more frequently, call for support less often, use more profitable channels, return merchandise less frequently, etc.
Before moving on to the next step, review this list and eliminate any customer behaviors that cannot be influenced through service interactions.
List service attributes that likely influence customer behaviors
Next, work backwards making a second list of specific , measureable service attributes that likely influence desired customer behaviors. This list should only include attributes for which there is a realistic cause and effect relationship between the service attribute and customer behavior. Ask yourself, “What can we (across all service channels) do more of, less of, or do differently to influence customer behaviors?” If it can’t be measured, if it can’t be trained (or programmed) or if it has no likely effect on measureable customer behaviors that affect profit, it should be removed from the list.
Consider how to influence desired customer behaviors (systems, skills, incentives, measurement & rewards)
Now, consider what specific systems, knowledge and skills are required to provide the service that will influence desired customer behaviors. Consider what employee incentives will be most effective in reinforcing the use of those skills and what measurement tools need to be in place to gather the metrics to trigger appropriate rewards.
Link list of customer behaviors to costs and revenues (incremental change)
Finally, link the first list (customer behaviors) to costs and revenues. To do this, calculate the financial impact of an incremental change in each item. For example, what would be the effect on revenue of increasing the average customer purchase by one dollar? What would be the effect on costs if the volume of complaints to call centers were reduced by five percentage points? It quickly becomes clear that even a small change in some customer behaviors can have a substantial financial impact. It also becomes clear which service changes will have the biggest effect.
ROI CALCULATOR: See the attached spreadsheet for an example of a Customer Experience ROI calculator based on this approach.
Thus far you have identified the customer behaviors you want to change, the general influence of each behavior on revenue or cost, and the dollar value of an incremental change in each behavior.
The major element missing from the formula is magnitude. How much change can the company expect to create? Can complaints be reduced by 1%, 5%, 10%? Will average purchase amounts increase by 50 cents? Ten dollars?
Also missing is the interaction among different variables. For example, aggressive up-selling may lead to a 10% increase in the average transaction amount, but it could also lead to a 2% increase in customer turnover, which might counteract the benefit.
The only way to answer these questions is to experiment.
Finally, this method excludes word of mouth customer behavior. In this ad of social media, increasing word of mouth (positive or negative) is an important customer behavior to manage. It’s been excluded from this tool do difficultly empirically measuring its benefits. See the attached post for a description of word of mouth measurement.
Shareholder Return and the Customer Experience: A Case for Investment in the Customer Experience
I recently came across a very intriguing bit of research that suggests the benefits of investments in the customer experience in terms of shareholder return.
Great customer service processes and people are not built overnight. They take years of investment to cultivate. Unfortunately, for some publically traded companies, the short-term demands of Wall Street make such investment difficult. The demands of investors to meet earnings estimates for the next quarter can make it difficult for managers to invest in the customer experience – the payback is too slow and uncertain.
Stockholders have little patience nowadays with investments that do not show a clear and quick return. To ensure that managers are acting in the owners’ interests, management incentives are more frequently tied to quarterly financial performance than to difficult-to-measure variables like customer loyalty.
Given great customer experiences are not built overnight, they are constantly at risk of budget cuts by managers who would boost short term earning at their expense. Service initiatives have a tendency to come and go in large companies before they have a chance to prove their worth, resulting in customer frustration, employee cynicism and widespread service mediocrity.
Service gurus talk about the need for “investor loyalty” as a counterbalance to customer loyalty, but that requires a visionary, motivated and stable management team who can convince investors to look farther ahead.
Easier said than done, right? How does one make the case for investments in the customer experience in an environment that demands making the next quarters numbers?
Jim Picoult, founder of Watermark Consulting, has an answer. Jim has created a stock index based on Forester’s annual Customer Experience Index (CXI). Jim calculated the returns of two hypothetical portfolios consisting of the top and bottom 10 publicly traded companies in Forester’s CXI for a six year period ending in 2012. Each year he rebalanced the two portfolios based on Forester’s new rankings. The portfolio comprised of companies ranked in Forester’s top 10 yielded a cumulative return of 43%, compared to 14.5% for the S&P 500. The portfolio containing the bottom 10, yielded a cumulative return of negative 33.9% – it lost a third of its value.
Now, correlation is not causation, and there are a lot of factors at play here. But clearly the managers of firms in the portfolio of Forester’s top 10 were able to both deliver shareholder value and invest in the customer experience.
It all comes down to thinking of the customer as an asset in which to invest and realize a return.
Not All Service Attributes Are Equal: Retail Bank Transaction Drivers of Loyalty
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. In one study of the retail banking industry, a 5% increase in customer loyalty translated into an 85% increase in profits.[1]
Customer loyalty is driven by the entire relationship with bank. Image, positioning, products, price and service all mix together in the customer’s’ value equation as customers make a continual decision to remain loyal.
What customer service attributes drive customer loyalty?
This article summarizes research into specific transaction service attributes with the intent of identifying which transaction attributes drive customer loyalty, and provides an analytical tool to help managers determine which attributes will yield the highest potential for ROI in terms of improving customer loyalty.
In order to determine transaction attributes which drive customer loyalty, Kinesis surveyed bank customers who had recently conducted a transaction at a branch.
With respect to the transaction, customers were asked to rate the following service attributes:
- Professional dress
- Branch cleanliness
- Prompt greeting
- Greeting made customer feel welcome
- Dependable and accurate
- Prompt service
- Willingness to help
- Job knowledge
- Interest in helping
- Best interests in mind
- Actively listened to needs
- Ability of bank personnel to help achieve financial needs
- Desire of bank personnel to help customers achieve financial goals
- Commitment to community
The next step in the research is to capture a measurement of loyalty against which to compare these attributes.
Measuring customer loyalty 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 bank. 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 the bank, or a new customer may be very satisfied and highly loyal.
Measuring customer loyalty 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 bank. 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 the bank, or a new customer may be very satisfied and highly loyal.
Kinesis proposes 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 bank to friend, relative or colleague. It stands to reason, if one is going to refer others to the bank, they will remain loyal as well. Because customers who are promoters of the bank 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. 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: “My bank cares about me, not just the bottom line.” Customers who agree with this statement trust the bank to do right by them, and not subjugate their best interests to profits. Customers who trust their bank to do the right thing are more likely to remain loyal.
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 for each customer, where the lower the value, the higher the estimate of loyalty – low values are good.[i]
See Using Promoter and Trust Measurements to Calculate a Customer Loyalty Index for a complete description of this methodology.
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.
Comparing the correlation of the above service attributes to this loyalty estimate yields the following Pearson Correlation for each attribute:
|
Pearson Coefficient |
|
| Want to help me achieve financial goals |
-0.69 |
| Commitment to community |
-0.66 |
| Ability to help achieve financial goals |
-0.64 |
| Best interests in mind |
-0.60 |
| Greeting made customer feel welcome |
-0.56 |
| Interested in helping |
-0.56 |
| Willing to help |
-0.55 |
| Prompt service |
-0.51 |
| Actively listened to needs |
-0.50 |
| Prompt greeting |
-0.49 |
| Dependable and accurate |
-0.45 |
| Professional dress |
-0.42 |
| Knew job Job knowledge |
-0.41 |
| Branch attractive |
-0.39 |
| Branch clean |
-0.37 |
Note the Pearson values are negative; the loyalty estimate is an inverse, where lower values indicate a stronger estimate of loyalty. As a result the stronger negative correlation translates into a correlation to our estimate of loyalty.
The four attributes with the highest correlation to loyalty are:
- Want to help me achieve financial goals,
- Commitment to community,
- Ability to help achieve financial goals, and
- Having my best interests in mind.
Two common themes in the top-four attributes are empathy and competence. Bank customers value relationships with banks that care about their needs and have the ability to satisfy those needs. Again, customer loyalty is driven by the entire relationship with bank. However, in terms of transactional service, customers clearly value empathy and competency and will reward banks who deliver on these two attributes with loyalty.
[i] The mathematical equation for this distance is as follows:
Where:
T = Trust rating
P = Promoter rating
ST = Number of points on the Trust scale
SP = Number of points on the Promoter scale
[1] Heskett, Sasser, and Schlesinger The Service Profit Chain, 1997, New York: The Free Press, p 21
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.
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:
Where:
- T = Trust rating
- P = Promoter rating
- ST = Number of points on the Trust scale
- SP = Number of points on the Promoter scale
- Strongest Loyalty: The strongest zone of loyalty contains cases where both the Trust and Promoter attributes received the highest possible rating.
- Strong Loyalty: The next zone is where the loyalty index lies within 35% of both the Trust and Loyalty axis.
- Moderate Loyalty: The zone of moderate loyalty is where the index lies within 60% of the highest possible Trust and Promoter ratings.
- Weak Loyalty: The zone of weak loyalty lies within 90% of the highest possible Trust and Promoter ratings.
- 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
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:
- 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.
- Quadrant 2: Areas with high correlations to loyalty and high performance. These are service attributes to maintain.
- Quadrant 3: Areas with low correlations to loyalty and low performance. These are service attributes to address if resources are available.
- 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:
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
It’s Personal: Retail Banking Sales and Closing Behaviors That Drive Purchase Intent
There is continued discussion about the branch’s role in the future of banking. The current consensus is it will continue to evolve from a transactional center to a sales center. Banking is a professional service. To avoid commoditization and selling on features other than rates and fees, a professional and effective sales process is required.
Our research into the efficacy of the branch sales process has identified several service and sales attributes that drive purchase intent. (See the insert below for a description of the methodology).
This article focuses specifically on closing behaviors, attempting to identify best practices in terms of driving purchase intent.
In short, for closing behaviors to be effective, the banker must first demonstrate competence and sincere concern for the customer’s best interests and needs. Closing behaviors without this predicate can be very dangerous to the sale.
What are the most common closing behaviors?
In our observational research of 100 retail banking presentations, key closing and presentation behaviors were observed in approximately two thirds of the sales presentation.
| Express interest in your business or make feel valued as a customer |
70% |
| Ask for the business or some commitment to action |
70% |
| Discuss products in terms of benefits designed to meet needs |
68% |
| Make comment expressing value of banking with the bank |
63% |
Asking for the business and making the shopper feel valued as a customer were the most common, followed closely by discussing products in terms of benefits designed to meet needs, and finally by expressing the value of banking with the bank.
Which behaviors are most effective?
To answer which of these four behaviors are most effective, let’s look at their relationship to the mystery shoppers purchase intent as a result of the sales presentation.
Of these four behaviors, expressing interest or making the customer feel valued as a customer has the strongest relationship to purchase intent. This behavior was present 3.6 times more frequent in shops with positive purchase intent relative to those with negative purchase intent.
What drives feeling valued as a customer?
Now, let’s take a look at the most significant behavior. What drives feeling valued as a customer? What caused shoppers to feel valued? To gain insight into this, Kinesis asked shoppers an open-ended question regarding how the banker expressed interest in their business. An analysis of the responses to this question is instructive.
When these responses are grouped by theme they generally group into four themes:
Looking at these comments with respect to whether or not the shopper reported positive purchase intent, two of these themes have a positive relationship to purchase intent: personal attention (45% for positive purchase intent compared to 0% for negative) and concern for needs (43% in shops with positive purchase intent compared to 11% for shops with negative purchase intent).
| Comments with POSITIVE relationship to purchase intent. | ||
| How expressed interest/Made feel valued as customer… |
Positive Purchase Intent |
Negative Purchase Intent |
| Personal/ Full Attention/ Not Rushed |
45% |
0% |
| Sincere/ Best interests in mind/ Concern for needs |
43% |
11% |
The other two behaviors have a negative relationship to purchase intent. One of these is both significant and instructive.
| Comments with NEGATIVE relationship to purchase intent. | ||
| How expressed interest/made feel valued as customer… |
Positive Purchase Intent |
Negative Purchase Intent |
| Offer to open account/ Effort to get business |
6% |
61% |
| Informative/ Answered questions |
17% |
50% |
A more overt effort to get the business, including opening the account, was present ten times more often in shops with negative purchase intent (61%) compared to positive purchase intent (6%). An effort to ask for the business without appearing to have the customer’s best interests in mind or giving the customer personal attention will not drive purchase intent. While asking for the business is an important part of any professional sales presentation, when doing so, the ground needs to be prepared by making the customer feel you have their best interests in mind. Otherwise, the banker can seriously undermine the presentation.
As branches continue to evolve from a transactional to a sales center, it is important not to divorce service from sales. Good sales is good service. The sales behavior with the strongest relationship to purchase intent is expressing interest in the customer and making them feel valued. The most effective way to make customers feel valued and interested is to provide them your full attention and sincerely demonstrate concern for the customers needs and best interests.
Visit the next article in this series. Beyond Needs Analysis: Asking Motivation Questions to Drive Purchase Intent – http://bit.ly/11sK9vG
———–
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 what 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.
Same-Branch Deposit Growth & The Customer Experience
It seems like only yesterday the branch was history. Do you remember twenty years ago when everyone predicted the death of the branch in favor of alternative delivery channels? How things have changed.
Now, the branch is seen as critical to delivering value to not only customers but shareholders as well. Now it appears branches and, more specifically, same-branch deposit growth is a key driver of shareholder value. First Manhattan Consulting Group has determined that other than return on equity and revenue-per-share growth, same-branch deposit growth is the strongest driver of total shareholder return. Furthermore, they quantify this relationship, concluding that 60% of the variance in shareholder return is explained by organic retail deposit growth. The shares of institutions with higher same-branch deposit growth tend to trade at higher multiples-to-earnings than institutions with lower same-branch deposit growth rates.
With the understanding that same-branch deposit growth is a key driver of shareholder value, Kinesis has endeavored to test and understand the relationship, if any, between the customer experience and same-branch deposit growth. We have determined that both purchase intent and same-branch deposit growth appears to be strongly associated with behaviors associated with reliability, empathy, and assurance.
To test the relationship between same-branch deposit growth, Kinesis has conducted thousands of mystery shops of a broad spectrum of institutions ranging in size from community banks with two branches to very large institutions with branch networks in the thousands. To conduct this test, Kinesis used a measurement instrument based on the five-dimensional SERVQUAL model, which defined the customer experience by the following five-dimensions: tangibles, reliability, responsiveness, empathy and competence.
The specific objectives of this analysis were to:
1) Test specific service behaviors/attributes against purchase intent to determine which behaviors, if any, appear to correlate with purchase intent.
2) Evaluate the relationship between the presence of these behaviors and branch deposit growth.
3) Test the efficacy of a mystery shop scoring system and branch deposit growth.
Kinesis first worked to test the relationship between elements of the customer experience and purchase intent, and to determine key drivers of purchase intent in the customer experience. As part of the solution to achieve this end, Kinesis asked each shopper, how the experience would have influenced their intention to purchase had they been an actual customer. Responses to this inquiry were collected on a 5-point scale ranging from “significantly increased” to “significantly decreased” the intention to purchase. Furthermore, immediately following this purchase intent rating, Kinesis asked each shopper to explain why they rated purchase intent as they did.
To help assure the validity of the analysis, two independent analysis plans were applied to the purchase intent data. First, the open-ended comments regarding why the shopper rated their purchase intent as they did were grouped according to common themes and according to the purchase intent rating. Second, the balance of the responses to the mystery shop questionnaire was cross tabulated by the purchase intent rating to determine which specific behaviors correlated most closely with purchase intent.
The results of this first part of the analysis plan revealed the following:
Once a shopper enters the branch, branch personnel clearly drive purchase intent. Over two-thirds (69%) of the reasons given for positive purchase intent are the result of branch personnel, only about one-in-five (18%) were product related, while 8% were due to the branch atmosphere.
The branch personnel driven elements include: generally positive, friendly service (26%), product knowledge/informative/confidence in the personnel (16%), attentive to needs/interest in helping/personalized service (14%), and professional/respectful/not pushy employees (10%). Prospective customers want confidence and trust not just in the bank, but also in the people who are the human face of the institution.
The second part of the analysis plan revealed very strong correlations between the following behaviors and purchase intent:
- Friendly & Courteous
- Greeting
- Interest in Helping
- Discuss Benefits & Solutions
- Promised Services Get Done
- Accuracy
- Professionalism
- Express Appreciation
- Personalized Comment (i.e., How are you?)
- Listen Attentively
Kinesis then grouped these highly correlated behaviors into the five-dimensional SERVQUAL model and found they group into three of the five-dimensions (reliability, empathy, and assurance) as follows:
- Reliability: Promised Services Get Done; and Accuracy
- Empathy: Interest in Helping; Discuss Benefits & Solutions; Personalized Comment (i.e., How are you?); and Listen Attentively
- Assurance: Friendly & Courteous; Greeting; Professionalism; and Express Appreciation
Intuitively, this result makes sense, beyond the basic requirement of reliability; customers also want to interact with bank personnel who have empathy (care about their best interest) and assurance (the knowledge and courtesy of employees and their ability to convey trust and confidence).
To evaluate the link between the customer experience, and the above behaviors, to the bottom line, Kinesis compared mystery shop results to same-branch deposit growth using publicly available deposit data from the FDIC. This analysis determined that branches with above average frequencies of reliability, empathy and assurance behaviors experienced 26% stronger three-year branch deposit growth rate than branches with low frequencies of these behaviors.
Finally, to evaluate the efficacy of the mystery shop scoring methodology, mystery shop scores were compared to branch deposit growth. This analysis revealed branches with above average mystery shop scores experienced a 78% greater branch deposit growth compared to those with below average mystery shop scores.
Our research and experience leads us to the conclusion that there is a link between the customer experience and such critical financial metrics such as same-branch deposit growth. With an understanding of which attributes drive this relationship, managers can now focus training, incentives and other management techniques on reinforcing empathy and assurance among its personnel, and make a financial case to all stakeholders (management, employees and shareholders) that the customer experience does drive financial performance.
A Guide to ROI in Customer Service
Introduction
Most business people would agree that there is value in good service. An abundance of literature exists supporting the notion that service can affect retention, spending, word-of-mouth endorsements and other customer activities that make a company more profitable. However, there are also many examples of companies with excellent service that chronically suffer from poor financial performance.
Good service is expensive. It requires research, training, measurement and the payout of incentives to managers and employees. Because it costs so much, companies struggle with the question of what their return on investment should be. Some even ask whether the investment is worth making at all. Could the money dedicated to improving service be more profitably spent in some other way?
The question is a fair one. Simply assuming that good service is a good investment is not very businesslike. Investment opportunities should be weighed against each other, with expected risks and returns assessed to determine the best choices. Unfortunately, few companies have had success calculating the ROI of customer service, making it difficult for them to determine whether their money will be, or has been, well spent.
Approaches to calculating service ROI appear to fall into two major camps: “Blind Faith” and “Rube Goldberg”. Rube Goldberg was a Pulitzer Prize winning cartoonist, famous for his outrageous “inventions”, which performed simple tasks in complicated ways. He produced cartoons similar to this:
Faith in Service
The Blind Faith approach begins with the unchallenged belief that good service always leads to higher profits. Companies launch service crusades, making grand promises to their customers as they whip their staff into a frenzy of friendly service activity. They intone ritual phrases, like, “We’re dedicated to excellence,” and “The customer is number one.” And they contribute a substantial amount of money to the effort, confident that it is all going to a good cause.
In the end, the miracle they had hoped for seldom appears. Customers may be more satisfied, but the expected rise in profitability rarely occurs. There may be profit changes, up or down, but it is devilishly difficult to figure out how much effect service quality had on the change.
At this point many companies experience a crisis in faith and revert to their old practices: cost-cutting, reductions in staff, new ad campaigns. Poorer but wiser, they look back at their crusade and wonder how they could have been so naive.
The Service Machine
The Rube Goldberg camp takes a more mechanistic approach. These folks don their white lab coats and attempt to build predictive models that explain the links between service attributes, customer satisfaction and profitability. They use statistical techniques to uncover correlations and coefficients and co-variation, revealing that a twelve-second reduction in average wait times will result in a one-point rise in customer satisfaction, which will turn into a half-cent increase in per-transaction revenue at a cost of a quarter of a penny, etc., etc.
Such models can, in fact, be valuable as a means for understanding the associations among different service and profit factors. They can also provide insight into how service attributes interact with each other to influence customer perceptions. A major drawback, however, is that these models tend to have too many moving parts to function as a practical, day-to-day business tool. In addition, they tend to give the appearance of being far more precise than they actually are. Many companies have spent considerable effort and money constructing such models, only to find that their applicability is marginal and their useful lifespan limited.
The Third Way
There is another approach – a third way that is simple, practical and intuitive. It does not purport to be as precise as the Rube Goldberg predictive models, nor does it treat service as sacrosanct, as with the Blind Faith followers. Rather, the Third Way takes the view that service isn’t profitable because it’s good, it’s good because it’s profitable.
The Third Way begins with the company making a list of customer behaviors that directly affect revenues or costs. The company asks itself, “What, specifically, do we want customers to do more of or less of?” Attitudes (such as satisfaction) and feelings (such as delight) aren’t included – only measurable, observable behaviors, such as, “use our service more often,” “call our support line less often,” “purchase more items on an average visit to the store,” and “return merchandise less frequently.”
The next step is to reduce the list by eliminating any items that cannot plausibly be influenced through service interactions. (Note that service interactions do not necessarily involve employees. ATMs, web sites and unmanned kiosks are all, from a customer’s point of view, service providers.) Working backwards, the company next makes a second list composed of specific, measurable service activities that are likely to affect desired customer behaviors. This list should only include items for which a realistic, cause-and-effect scenario between service behavior and customer behavior can be articulated. Again, attitudes and feelings are not included. The company asks itself, “What can employees (or machines or web sites) do more of or less of, or do differently, to influence how customers act?” If it can’t be measured, if it can’t be trained (or programmed) or if it has no likely effect on measurable customer behaviors that effect profit, it is removed it from the list.
This process of deconstruction next moves to the subject of training: What specific knowledge and skills are needed to provide the service that will affect desired customer behaviors? Then, incentives and measurement: What rewards will be most effective at reinforcing the use of those skills? What metrics need to be gathered to trigger rewards?
Each list is winnowed to ensure that it applies only to the items on the previous list. In this way, the picture is never cluttered with irrelevant or ambiguous elements. Because every item on every list is concrete and measurable, the people who are accountable for delivering service and making it pay will know precisely what is expected of them.
The next step is to link the first list (customer behaviors) to costs and revenues. To do this the company calculates the financial effect of an incremental change in each item. For example, what would be the effect on revenue of increasing the average customer purchase by one dollar? What would be effect on costs if the volume of complaints to call centers were reduced by five percentage points? It quickly becomes clear that even a small change in some customer behaviors can have a substantial financial impact. It also becomes clear which service changes will have the biggest effect.
The company has thus far identified the customer behaviors it wants to change, the general effect of each behavior on revenue or cost, and the dollar value of an incremental change in each behavior. In addition, it has identified the service activities that are likely to influence changes in customer behaviors, and a strategy for promoting those activities through training, measurement and rewards. All of these steps can be accomplished in a day with a few managers and pot of strong coffee. But from this point on, the process gets a bit trickier.
The major element missing from the formula is magnitude. How much change can the company expect to create? Can complaints be reduced by 1%, 5%, 10%? Will average purchase amounts increase by 50 cents? Ten dollars?
Also missing is the interaction among different variables. For example, aggressive up-selling may lead to a 10% increase in the average transaction amount, but it could also lead to a 2% increase in customer turnover, which might counteract the benefit.
The only way to answer these questions is to experiment. The company must identify the most promising service investments and test them on a small scale. Service units (stores, call centers, etc.) should be compared, using test and control groups. The groups should be small enough to keep the experiment manageable, but large enough to wash out the influence of temporary, outside factors, such as bad weather or a blow-out sale by a competing store.
The experimentation does not stop with one test. The process is iterative, allowing the company to fine-tune its tactics and find the optimal mix of service activities that result in the highest return on investment. With patience, a reliable formula for ROI will emerge and the company can decide which service improvements it should invest in – or whether it should invest in service improvements at all.
To return to an earlier statement: The Third Way takes the view that service isn’t profitable because it’s good, it’s good because it’s profitable. This doesn’t mean there is no benefit to customers. On the contrary, the types of behaviors desired of customers will only come about if they are satisfied, loyal and occasionally delighted. The point is, companies cannot make the world a better place for customers unless they show a profit. By defining good service in terms of its effect on the bottom line, everybody wins.
Kinesis CEM 