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.
Research has determined the business attribute with the highest correlation to profitability is customer loyalty. Customer loyalty lowers sales and acquisition costs per customer by amortizing these costs across a longer lifetime – leading to some extraordinary financial results.
Measuring customer loyalty in the context of a survey is difficult. Surveys best measure attitudes and perceptions. Loyalty is a behavior not an attitude. Survey researchers therefore need to find a proxy measurement to determine customer loyalty. A researcher might measure customer tenure under the assumption that length of relationship predicts loyalty. However, customer tenure is a poor proxy. A customer with a long tenure may leave, or a new customer may be very satisfied and highly loyal.
Likelihood of referral captures a measurement of the customer’s likelihood to refer a brand to a friend, relative or colleague. It stands to reason, if one is going to refer others to a brand, they will remain loyal as well, because customers who are promoters of a brand are putting their reputational risk on the line. This willingness to put their reputational risk on the line is founded on a feeling of loyalty and trust.
Any likelihood of referral question can be used, depending on the specifics of your objectives. Kinesis has had success with both a “yes/no” question, “Would you refer us to a friend, relative or colleague?” and the Net Promoter methodology. The Net Promoter methodology asks for a rating of the likelihood of referral to a friend, relative or colleague on an 11-point (0-10) scale. Customers with a likelihood of 0-6 are labeled “detractors,” those with ratings of 7 and 8 and identified as “passive referrers,” while those who assign a rating of 9 and 10 are labeled “promoters.”
In our experience asking the “yes/no” question: “Would you refer us to a friend, relative or colleague?” produces starker differences in this two-dimensional analysis making it easier to identify which service attributes have a stronger relationship to both loyalty and engagement.
Similar to loyalty, customer engagement or wallet share can lead to some extraordinary financial results. Wallet share is the percentage of what a customer spends with a given brand over a specific period of time.
Also similar to loyalty, measuring engagement or wallet share in a survey is difficult. There are several ways to measure engagement: one methodology is to use some formula such as the Wallet Allocation Rule which uses customer responses to rank brands in the same product category and employs this rank to estimate wallet share, or to use a simple yes/no primary provider question.
Using these loyalty and engagement measures together, we can now cross tabulate the array of service attribute ratings by these two measures. This cross tabulation groups the responses into four segments: 1) Engaged & Loyal, 2) Disengaged yet Loyal, 3) Engaged yet Disloyal, 4) Disengaged & Disloyal. We can now make comparisons of the responses by these four segments to gain insight into how each of these four segments experience their relationship with the brand.
These four segments represent: the ideal, opportunity, recovery and attrition.
Ideal – Engaged Promoters: This is the ideal customer segment. These customers rely on the brand for the majority of their in category purchases and represent lower attrition risk. In short, they are perfectly positioned to provide the financial benefits of customer loyalty. Comparing attribute ratings for customers in this segment to the others will identify both areas of strength, but at the same time, identify attributes which are less important in terms of driving this ideal state, informing future decisions on investment in these attributes.
Opportunity – Disengaged Promoter: This customer segment represents an opportunity. These customers like the brand and are willing to put their reputation at risk for it. However, there is an opportunity for cross-sell to improve share of wallet. Comparing attribute ratings of the opportunity segment to the ideal will identify service attributes with the highest potential for ROI in terms of driving wallet share.
Recovery – Engaged Detractor: This segment represents significant risk. The combination of above average share of wallet, and low commitment to put their reputational risk on the line is flat out dangerous as it puts profitable share of wallet at risk. Comparing attribute ratings of customers in the recovery segment to both the ideal and the opportunity segments will identify the service attributes with the highest potential for ROI in terms of improving loyalty.
Attrition – Disengaged Detractor: This segment represents the greatest risk of attrition. With no willingness to put reputational risk on the line, and little commitment to placing share of wallet with the brand, retention strategies may be too late for them. Additionally, they most likely are unprofitable. Comparing the service attributes of customers in this segment to the others will identify elements of the customer experience which drive attrition and may warrant increased investment, as well as, elements that do not appear to matter very much in terms driving runoff, and may not warrant investment.
By making comparisons across each of these segments, researchers give managers a basis to make informed decisions about which service attributes have the strongest relationship to loyalty and engagement. Thus identifying which behaviors have the highest potential for ROI in terms of driving customer loyalty and engagement. This two-dimensional analysis is one way to turn customer experience observations into insight.
Beyond Loyalty: Engagement/Wallet Share
In two earlier posts we discussed 1) including a loyalty proxy as part of your brand perception research and 2) determining the extent to which your desired brand image is reflected in how customers actually perceive the brand.
Now, we expand the research plan to move beyond loyalty and brand perception, and investigate customer engagement, or the extent to which customers are engaged with the brand through share of wallet.
Comparison to Competitors
The first step in measuring customer engagement is capturing top-of-mind comparisons of your brand to competitors. There are many ways to achieve this research objective, perhaps the simplest is to present the respondent with a list of statements regarding the 4-P’s of marketing (product, promotion, place and price) and asking customers to compare your performance relative to your competitors.
The statements you present to customers should be customized around your industry and business objectives, but they may look something like the following:
- Their products and services are competitive
- They are more customer-centric
- They have lower fees
- They have better service
- They offer better technology
- They are more nimble and flexible
- They are more innovative
Similar to the brand perception statements discussed in the previous post, these competitor comparison statements can be used to determine which of these service attributes have the most potential for ROI in terms of driving loyalty, again, by cross tabulating responses to the customer loyalty proxy.
The next step in researching customer engagement is to determine if the customer considers you or another brand their primary provider. This is easily achieved by presenting the customer with a list of providers, including yourself, and asking them which of these the customer consider their primary provider.
Finally, we can tie industry comparisons to primary provider by asking why they consider their selection as a primary provider. This is best accomplished by using the same list of competitor comparison statements above, and asking which of these statements are the reasons they consider their selection to be the primary provider.
Similar to the brand perception statements discussed in the previous post, these competitor comparison statements can be used to determine which of these service attributes have the most potential for ROI in terms of driving loyalty, by cross-tabulating responses to these statements to the loyalty segments.
A New Normal: Implications for Bank Customer Experience Measurement Post Pandemic – Planned Interactions
Part 2: Research Tools to Monitor Planned Interactions through the Customer Lifecycle
As we explored in an earlier post, Three Types of Customer Experiences CX Managers Must Understand, there are three types of customer interactions: Planned, Stabilizing, and Critical.
Planned interactions are intended to increase customer profitability through the customer lifecycle by engaging customers with relevant planned interactions and content in an integrated omni-channel environment. Planned interactions will continue to grow in importance as the financial service industry shifts to an integrated digital first model.
These planned interactions are frequently triggered by changes in account usage, financial situation, family profile, etc. CRM analytics combined with Big Data are becoming quite effective at recognizing such opportunities and prompting action toward planned interactions. Customer experience managers should have a process to record and analyze the quality of execution of planned interactions with the objective of evaluating their effectiveness – regardless of the channel.
The key to an effective strategy for planned interactions is relevance. Triggered requests for increased engagement must be made in the context of the customer’s needs and with their permission; otherwise, the requests will come off as clumsy and annoying, and give the impression the bank is not really interested in the customer’s individual needs. By aligning information about execution quality (cause) and customer impressions (effect), customer experience managers can build a more effective and relevant approach to planned interactions.
Research Plan for Planned Interactions
The first step in designing a research plan to test the efficacy of these planned interactions is to define the campaign. Ask yourself, what customer interactions are planned through these layers of integrated channels. Mapping the process will define your research objectives, allowing an informed judgment of what to measure and how to measure it.
For example, after acquisition and onboarding, assume a bank has a campaign to trigger planned interactions based on triggers from past engagement. These planned interactions are segmented into the following phases of the customer lifecycle: engagement, growth, and retention.
Often it is instructive to think of customer experience research in terms of the bank-customer interface, employing different research tools to study the customer experience from both sides of this interface.
In our example above, management may measure the effectiveness of planned experiences in the engagement phase with the following research tools:
These post-experience surveys are event-driven, where a transaction or service interaction determines if the customer is selected for a survey. They can be performed across all channels, digital, contact center and in-person. As the name implies, the purpose of this type of survey is to measure experience with a specific customer experience.
Ultimately, employees are at the center of the integrated customer experience model.
Employee surveys often measure employee satisfaction and engagement. However, there is far more value to be gleaned from employees. We employ them to understand what is going on at the customer-employee interface by leveraging employees as a valuable and inexpensive resource of customer experience information.
They not only provide intelligence into the customer experience, but also evaluate the level of support within the organization, and identify perceptual gaps between management and frontline personnel.
Overall satisfaction surveys measure customer satisfaction among the general population of customers, regardless of whether or not they recently conducted a transaction. They give managers valuable insight into overall satisfaction, engagement, image and positioning across the entire customer base, not just active customers.
Be it a website or mobile app, digital mystery shopping allows managers of these channels to test ease of use, navigation and the overall customer experience of these digital channels.
Mystery shopping is about alignment. It is an excellent tool to align the customer experience to the brand. Best-in-class mystery shopping answers the question: is our customer experience consistent with our brand objectives? Historically, mystery shopping has been in the in-person channel, however we are seeing increasing mystery shopping to contact center agents.
In the growth phase, we measure the effectiveness of planned experiences on both sides of the customer interface with the following research tools:
Awareness of the brand, its products and services, is central to planned service interactions. Managers need to know how awareness and attitudes change as a result of these planned experiences.
In these unique mystery shops, mystery shoppers are seeded into the lead/referral process. The sales behaviors and their effectiveness are then evaluated in an outbound sales interaction.
These shops work very well in planned sales interactions within the contact center environment.
These surveys are used to evaluate customer engagement with and loyalty to the institution. Specifically, they determine if customers consider the institution their primary provider of financial services, and identify potential road blocks to wallet share growth.
Finally, planned experiences within the retention phase of the customer lifecycle may be monitored with the following tools:
CIT is a qualitative research methodology designed to uncover details surrounding a service encounter that a customer found particularly satisfying or dissatisfying. This research technique identifies these common critical incidents, their impact on the customer experience, and customer engagement, giving managers an informed perspective upon which to prepare employees to recognize moments of truth, and respond in ways that will lead to positive outcomes.
Employees observe firsthand the relationship with the customer. They are a valuable resource of customer experience information, and can provide a lot of context into the types of bad experiences customers frequently experience.
Closed account surveys identify sources of run-off or churn to provide insight into improving customer retention.
If an integrated channel approach is the objective, one should measure the customer experience in an integrated manner.
In lifecycle shops, shoppers interact with the bank over a period of time, across multiple touch points (digital, contact center and in-person). This lifecycle approach provides broad and deep observations about sales and service alignment to the brand and performance throughout the customer lifecycle across all channels.
Comment tools are not new, but with modern Internet-based technology they can be used as a valuable feedback tool to identify at risk customers and mitigate the causes of their dissatisfaction.
Call to Action – Make the Most of the Research
For customer experience surveys, we recommend testing the effectiveness of planned interactions by benchmarking three loyalty attitudes:
For mystery shopping, we find linking observations to a dependent variable, such as purchase intent, identifies which sales and service behaviors drive purchase intent – informing decisions with respect to training and incentives to reinforce the sales activities which drive purchase intent.
As the integrated digital first business model accelerates, planned interactions will continue to grow in importance, and managers of the customer experience should build customer experience monitoring tools to evaluate the efficacy of these planned experiences in terms of driving desired customer attitudes and behaviors.
In the next post, we will take a look at stabilizing experiences, and their implications for customer experience research.