Customer Experience

Customer feedback is critical for helping your customer service agents tackle problems—whether closing the inner loop directly with customers or closing the outer loop with issues that keep surfacing repeatedly.

Organizations are typically quite comfortable tackling inner-loop programs. If you have agents that review and action customer feedback, and enough resources to contact unhappy customers, you’re off to a good start. 

However, closing the outer loop means addressing structural issues, and this is where it gets a bit harder. Here is how the Six Sigma methodology can be used as a template to help CX leaders close the outer loop and address systemic issues.

What Is Six Sigma? 

Six Sigma is a methodology that uses data analytics and statistics to analyze business processes and services to understand how they’re performing and how they can be optimized. The objective is to increase business outcomes, such as the customer experience, by reducing defects and improving services and processes.

Since collecting and analysing data is at the core of both Six Sigma and CX programs, it struck me that they seem like a natural fit.

What Is the Six Sigma Methodology?

The Six Sigma methodology is a combination of two underlying processes: DMAIC and DMADV. 

DMAIC process is define, measure, analyze, improve, control. 

DMADV process is define, measure, analyze, design, validate. 

The latter process is only used whenever a business has undergone optimization but is still not meeting metrics. When this happens, Six Sigma professionals will utilize the DMADV process as a means of redesigning processes within a business. 

The DMAIC process is the standard and most utilized process. Because of that, it will be what is used when providing a deep explanation of the Six Sigma methodology.

The 5 Steps of the Six Sigma Methodology

Six Sigma follows the DMAIC process made up of the following steps:

1. Define
2. Measure
3. Analyse
4. Improve
5. Control

We’ll go through these one by one:

Define

The first step is to prioritise the pain points that you want to tackle as a business. With Six Sigma we typically ask 3 questions:

• Is there a gap between the current process and the customer expectations around that process?
• Is the cause of the issue understood?
• Is the solution known?

The gap between current processes and customer expectations should be found in your voice of the customer data. By analyzing this data along with measuring the key drivers of your main business metric, you will be able to identify what issues are causing the most dissatisfaction across your customer base.

After analyzing your VoC data and key drivers, you may come across issues where you already have the solution. For example, you may have tried to understand why your business wasn’t doing well with 18-28-year-old customers anymore. But, after digging deeper into the data, you discovered that those customers weren’t invited to sign up for a revamped customer loyalty program. This is an issue you have a solution for, and it can be fixed quickly. 

On the other hand, if you uncover another issue not meeting customer expectations in which you do not have a solution or do not know the root cause, you can utilize the Six Sigma Methodology.

Measure

The measurement step is where a baseline is established. You can do this by using your existing customer experience metrics. If you have the data to identify the problem, you can use the same data to create a baseline on which improvements can be compared. 

Analysis

This is where it gets interesting—the analysis phase involves identifying the root cause, which is critical if you want to find an effective solution. We typically start with brainstorming different ideas and then once we settle on some hypotheses we can test, we can then check if we have the data available to us to check this hypothesis or if we need to collect more data in order to test it.

It’s important to understand the true root cause. It’s quite easy to find correlated themes that do not directly cause the issue. Driver analysis on both structured and unstructured data can help with this process.

Improve

Once you have identified the root cause, it’s time to come up with solutions to address it. This is where customer experience consulting can be useful. Customer experience consultants will help you compare and contrast ideas to fix your problem.

Once we’ve settled on the most promising ideas, it’s time to start A/B testing the concepts, which involves trialing the solution(s) with test groups and then comparing the performance against a control group.

In the end, there’s typically a cost benefit analysis to understand which action would have the most impact. This needs to be contrasted against the relevant investment.

Control

Once the best course of action has been identified, it can be implemented. Once it is implemented you can track the improvement. InMoment’s reporting studio makes this easy by giving you the ability to track changes in your customer metrics, create and share reports, and identify the most important actions that need to be taken to improve your business. 

Utilize the Six Sigma Methodology with InMoment

Often companies struggle with putting a framework in place to tackle the outer loop in an effective manner. Six Sigma is one method that can be utilized to put a structure in place that can be used to root cause structural issues, identify potential solutions and identify the most effective one. InMoment’s services will help you close the loop most effectively while using the XI Platform. With the guidance of our Strategic Insights team and the tools within the InMoment platform, no CX problem cannot be fixed. To see what we can do for your business, schedule a demo today!

This article was originally posted on Quirk’s Media.

Every successful business outcome benefits from having a reliable, flexible, actionable and amply proven template and improvement guide. This is as true for employee experience (EX) as customer experience (CX).

There is a clear path to greater, more progressive employee experience, insights and greater stakeholder centricity for any organization, and it begins with understanding the concept of experience improvement (XI) as it proceeds and matures.

The most basic definition of employee experience often has to do with overall happiness on the job (or what is generally understood as employee satisfaction). Subsequent stages in EX maturity build upon that first step. Exploring that progression, and how it will lead to experience improvement for everyone in your organization’s universe, is the focus of this discussion.

Employee Satisfaction: Providing a Little More Than the Basics

Employee satisfaction typically encompasses basic job functions like compensation, workload, flexibility, teamwork, resource availability and so forth. It’s built on the basic premise that if employees are happy, they will be productive and remain with their employer. Satisfied employees, then, are generally not aspirational and remain positive if things stay pretty much the same. Much like customer satisfaction, employee satisfaction is largely attitudinal and tactical.

A major challenge with employee satisfaction, though, was identified some time ago. Companies want to keep employees happy and reduce turnover, of course, but it was found that programs and strategies that support improved satisfaction can often result in demoralized staff – especially among employees who either want to perform at higher levels or are unmotivated to contribute more.

Even consultants and professional HR associations like the Society for Human Resource Management have determined that even high-level satisfaction doesn’t necessarily mean closer connection to the employer or greater employee performance.

Here’s a brief example of what can occur through satisfaction-based initiatives, irrespective of intentions. A large national financial services company, concerned that it was experiencing over 30% turnover among new employees, decided to give them a 13% bonus. Those employees who were “satisfied” happily took the additional money, but the result was no discernible decrease in churn.

Employee Engagement: Doing What (Almost) Everybody Else Does

The predominant EX construct that most organizations follow these days is, at its core, to consider employees as necessary costs of doing business. The overall objective of this construct is to optimize employees’ overall fit, utility and productivity within the enterprise. This construct is engagement, which also seeks to quantify emotional and rational job satisfaction (as well as motivation to think, feel, and act). The principal intents of employee engagement, then, are to identify:

• What originally drew individuals to the company.
• What keeps them there.
• What they see as their jobs and how involved they are in them.
• How aligned they are with the company’s overall business goals and culture.

Engagement, however, represents a mix of loosely related concepts and ideas rather than a single, objectively defined term. As a result, it only marginally impacts customer experience and downstream behavior. In 2006, The Conference Board published “Employee Engagement: A Review of Current Research and Its Implications.” According to findings in this report, a total of 12 major studies on employee engagement had been published over the prior four years by top research firms. Each of the studies used different definitions and, collectively, came up with 26 key drivers of engagement. For example, some of the studies emphasized underlying cognitive issues, while others addressed underlying emotional issues. What is absent from these drivers, though, is a focus on how employee behavior connects to, and drives, customer experience. And, though these findings from The Conference Board’s research are, as of this writing, close to 20 years old, the concept of engagement still puts very little emphasis on employees’ role(s) in customer focus and value delivery. In other words, though many companies might infer that happy employees equal happy customers, that relationship isn’t necessarily real or causal.

Further compromising the enterprise value and actionability of engagement is the fundamental issue of measurement. Again, there has never been a reliably clear and consistent definition of employee engagement on which to base performance since the term was first coined by an academic almost 30 years ago. Combine that with the current job market landscape, and it’s clear that employee engagement can no longer be considered a sufficient behavioral standard or organizational goal.

Employee Commitment: Joining the Ranks of the Advanced and Progressive

Employee commitment represents that which most directly shapes and creates the full (and current) EX landscape. It considers employees actively contributing stakeholders who are connected to company culture, derive fulfillment and purpose from their work, and create value for customers. Fundamentally, the concept of commitment recognizes and leverages the employees and their behavior as highly valued enterprise assets and contributions to business outcomes.

Essentially, commitment draws on key elements of both behavioral science and behavioral economics – emphasizing an employee’s emotional connection to the culture, goals, practices and customers of the enterprise – as critical operating resources. Employees have become center stage in optimizing customer behavior and perceived personal benefit in this stage, and have three key traits:

• Commitment to the organization itself, its purpose and its culture.
• Commitment to the organization’s value proposition, its products and its services.
• Commitment to the organization’s customers and fellow employees.

The HR objectives of staff fit, alignment and productivity emphasized in employee satisfaction and engagement are also important in employee commitment; however, this stage of the

employee experience maturity journey recognizes the extent to which employees are in direct and indirect contact with, and providing benefit for, customers. Employees should be enthusiastic and actively supportive representatives of the brand. If, today, employee satisfaction and engagement are not designed to meet this critical objective of the customer experience, then customer-perceived value and customer experience will almost assuredly be impacted.

Employee commitment behavior (and the culture, processes and programs that support it) produce consistently stronger business outcomes (lower staff turnover, more effective experiences for customers, greater loyalty behavior, etc.) in both hard- and soft-cost terms. This more progressive approach to EX also enables companies to directly link, and intersect, employee behavior with customer behavior, making it more powerful than either satisfaction or engagement in creating brand equity.

But there’s even more for ambitious, stakeholder-centric organizations and employees to attain here, and that’s the last, and ultimate, stage of our experience improvement journey: employee advocacy.

Employee Advocacy: Becoming One of the World’s Best

The select brands and organizations that have implemented employee advocacy and its correlating outcomes use stakeholder value creation as the central flow point for operations, processes and culture. Emotional foundations, experience memories and how employees communicate them play a much greater role in employee behavior here (as almost defacto personas). This can be viewed in Figure 1 and Figure 2, which identify levels of both customer and employee value. 

Organizations can use employee advocacy to identify the drivers behind employees’ emotional and rational commitment to both their own experience and that of customers. In some instances, though, brands and groups will emphasize social media, image and other related factors as vital facets of employee responsibility. Such factors certainly have their value, of course, but understanding them doesn’t automatically correlate to meaningful experience improvement.

The advocacy concept optimizes employee commitment to the organization, its goals, its value proposition and its customers. Recognition has become especially pronounced in recent years, at least in part due the upheaval produced by the COVID-19 pandemic and subsequent Great Resignation. This, and the impact on employee dynamics, has incentivized many brands to finally invest substantially in a previously underfunded (or unfunded) myriad of employee support resources.

Additionally, advocacy creates a state in which every employee is tasked with delivering customer value as part of not just his/her job description, but as part of their broader role within the enterprise.

Customer value is delivered irrespective of employee location, function or level. In short, everyone from frontline teams to CEOs must understand how their role affects customer experience and remember that it does so regardless of how far they are from the front lines.

Summarizing the Stages, and Trajectory, of Experience Improvement

Regardless of which of stage your organization currently resides in its journey to experience improvement, it’s imperative to regularly assess both goals and priorities, understand what each of these steps has to offer and then work toward a culture of employee commitment and advocacy. A combination of recent talent landscape dynamic events and extensive research has cemented the notion that merely satisfying your employees is insufficient for retaining them or making them feel valued (let alone improving employee or customer experience).

Figure 3 shows a guide that organizations can follow as they pursue employee experience improvement.

By adopting this more progressive and outcomes-oriented set of approaches, organizations can create commitment within their employees, adopt an advanced culture of stakeholder experience centricity and continuously achieve experience improvement for themselves and their customers. The benefits, and the human connections underlying them, are immense.

There is so much more to communication than just the words we say. Take sarcasm, for instance. Sarcastic comments often rely heavily on irony, conveying the opposite meaning from the one being directly expressed. But this irony is hard to convey without the added benefit of voice inflection and bodily cues (which is why it can be so problematic when someone tries to be sarcastic in a text message or email). 

At the same time, non-verbal cues may even go so far as to reveal deeper meaning even beyond what a person intends to express—lack of eye contact during a conversation may indicate that one is uncomfortable with the situation while leaning forward can mean that they are actively engaged and paying attention. In fact, studies suggest that as much as 90% of communication is non-verbal. And while there’s some debate over the accuracy of that number, no one can deny that there’s more in what we say than is carried in the words we speak (or type). 

This can create real problems for your business. Given that most customer feedback is text-based (such as emails, social media posts, surveys, in-app feedback, SMS, live chat, etc.), it can be extremely difficult to discern the actual meaning behind the words. To keep up with expectations and provide a positive customer experience, companies in all industries need a more accurate way to understand and categorize their customer feedback. This is where sentiment analysis comes into play. 

What Is Sentiment Analysis?

Sentiment analysis is a term that describes the tools and strategies designed to help organizations extract unspoken meaning and emotion from text. By using sentiment analysis to contextually mine written communication for subjective information, your business can gain a greater understanding of how your customers view your brand, services, products, and more. 

At its most basic, sentiment analysis can, with reasonable accuracy, determine whether written or spoken feedback should be classified as favorable, unfavorable, or neutral, and how intensely that sentiment is being expressed. 

To make this possible, sentiment analysis is generally supported by sentiment scoring (also called polarity analysis). Often the polarity or overall sentiment is expressed using a numerical score ranging from -100 up to 100, with 0 representing a completely neutral sentiment. This kind of sentiment analysis scoring can be applied to specific phrases or points in the customer feedback or may be calculated for the entire text. Thus, your organization can apply sentiment analysis to create a mathematical data model representing the overall opinions or attitudes of your customers—either as individuals or groups. 

But sentiment analysis can also go beyond the basics, picking out subtle clues in messages to help you better understand what your customers are feeling and how you can help them have a positive experience. 

How Does Sentiment Analysis Work?

The origin of sentiment analysis as a field of study traces itself back to the mid-20th century, when researchers would comb through and compare written documents to better understand the authors’ intent. But it wasn’t until the advent of digital communication and big data mining that sentiment analysis became a viable business tool. Today, technology advancements in AI, deep learning, and natural language processing (NLP) make it possible for organizations to mine massive amounts of customer data to gauge public opinion, conduct market research, monitor reputation, and better understand the customer experience.

At the heart of modern sentiment analysis are algorithms designed to automate the identification of text sentiment based on specific methods and analysis models. And although individual organizations may differ somewhat in their approach, most sentiment analysis processes fall into one of three categories:

Machine-Learning Sentiment Analysis

Using automated techniques, machine-learning sentiment analysis allows computer systems to learn from provided texts and apply those learnings to future evaluations. To do this, companies will provide the sentiment analysis model with a training set of natural language feedback that has already been tagged with labels showcasing which words or phrases demonstrate a positive, neutral, or negative sentiment. The model takes these correlations and then applies them to new natural language sets. 

Over time, the machine-learning sentiment analysis model becomes more effective at automatically identifying emotional sentiment within text. 

Rule-Based Sentiment Analysis

Rule-based sentiment analysis relies more heavily on human-built rules to locate hidden sentiment within a text. In its most simple form, the algorithm is provided with a detailed lexicon of possible words, terms, and expressions, with each assigned a sentiment score ranging from negative to positive. Then the algorithm simply tabulates the total score from each word or phrase within the text to determine the overall sentiment of the data set. Rule-based sentiment analysis may require further refining to account for things like idioms, sarcasm, or other unique verbal cues.

Hybrid Sentiment Analysis

For increased accuracy, organizations will often combine rule-based and machine-learning sentiment analysis models to create a hybrid approach to sentiment analysis. This allows the model to retain the statistical accuracy of machine learning while also incorporating hand-written rules for a more stable sentiment analysis solution. In this approach to sentiment analysis, different types of classifiers back each other up, so that if one fails, the next can step in to ensure that no sentiment is overlooked.

Why Is Sentiment Analysis Important?

As communication technologies continue to improve, today’s customers expect their voices to be heard. As such, sentiment analysis has grown into an essential tool for monitoring and understanding opinions relevant to business.

Using sentiment analysis to mine these opinions from customer feedback, social conversations, service agent interactions, etc. can give your organization key insights into how customers and other stakeholders feel about your business and its offerings. You can then refine your processes, products, and services to better meet these expressed—and unexpressed—needs. The advantages of effective sentiment analysis range from being able to resolve customer concerns more quickly, to tracking and identifying trends and relevant factors in customer satisfaction scores across predefined periods.

Those businesses that offer a multichannel or omnichannel experience gain further benefits. Sentiment analysis empowers teams to automatically categorize feedback by the channel it was received in, and to develop an accurate picture of customer perception across individual platforms. 

Types of Sentiment Analysis

Even within the categories mentioned above, there are different ways to approach sentiment analysis. Some of the most widely used sub-types of sentiment analysis include:

Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis tracks emotional sentiment related to specific aspects of a business or its products/services. For example, an organization that rolls out a new feature as part of its app may employ aspect-based sentiment analysis to better understand how users feel about the upgrade. Aspect-based sentiment analysis would identify feedback, comments, and conversations relevant to the new feature and determine whether customer sentiment is positive, negative, or neutral. 

Clause-Level Analytics

Clause-level sentiment analysis breaks feedback down into clauses rather than sentences. For example, if a customer were to comment that a clothing product they recently purchased “Looks great but isn’t comfortable to wear,” clause-level sentiment analysis could be applied to better understand just how satisfied or dissatisfied the customer is with their purchase. This makes it possible for businesses to correctly categorize responses that may include both positive and negative sentiments in a single sentence. 

Emotion-Detection Sentiment Analysis

Emotion-detection sentiment analysis goes further than tracking negative-to-positive sentiment polarity and instead detects the emotional state of the person originating the feedback. Like other forms of sentiment analysis, emotion detection relies on lexicons of emotionally-charged words, machine-learning algorithms designed to detect emotional cues in text, or a combination of both. 

Intent Analysis

Customers may reach out to your company or provide feedback for many different reasons—a client who wants a refund will naturally be motivated by intentions that are not the same as those who are merely looking for information. Intent-based sentiment analysis analyzes the objective of the customer, categorizing the message so that it can be more accurately addressed. 

Multilingual Sentiment Analysis

Multilingual sentiment analysis applies the same processes to messages and feedback originating from speakers of more than one language. This adds to the complexity of the algorithms and may require additional processing and resources. In many cases, organizations will train an individual sentiment analysis model to address sentiment in a specific language, rather than attempting to create a model that can analyze sentiment in multiple languages. 

Sentiment Detection

Sentiment detection is a form of sentiment analysis used to pick out emotionally-relevant text from neutral or objective information. For example, sentiment detection applied to a movie review would identify “It was exciting” as a positive sentiment while making note that “The run time was 122 minutes” is simply a statement of information with no positive or negative sentiment attached to it. 

Smart Text Analytics

Smart text analytics can help you gain vital insights from unstructured feedback. This approach to sentiment analysis breaks down silos and connects data from various sources, applying an AI-based adaptive sentiment engine capable of closely analyzing customer messages to identify trends and themes over time. Click here to learn more.

Sentiment Analysis Examples

At the end of the day, most forms of sentiment analysis are tied directly to the words and phrases customers use when they discuss your brand, its business policies, and the products or services you offer. With this in mind, let’s take a look at some examples of sentiment analysis, and why some feedback may be easier to classify than others. 

• “I love how the new menu is arranged!”
  The sentiment here is fairly straightforward; the customer is expressing a positive feeling and providing clear feedback. Most sentiment analysis tools would have an easy time identifying the sentiment.
  
• “Oh man, I sure do love how you increased all the prices. Thanks so much for doing your part to drain my wallet.”
  The sentiment in this feedback is more difficult to identify from the text alone, requiring a more in-depth sentiment analysis. Although the customer is using positive terms (“love,” “Thanks”), they are clearly intending to convey a negative response.
  
• “I’m not unhappy with how the product looks.”
  The feedback here uses a double negative to indicate that the reviewer is not fully pleased, but also not fully displeased. Poor sentiment analysis of this phrase may incorrectly attribute polarity beyond what is being expressed.
  
• “The new slogan made me 😆.”
  Nonstandard characters can present a real challenge for sentiment analysis tools, unless the tools have been trained to recognize the sentiment of these characters.
  
• “The service agent was salty about something.”
  Sentiment analysis tools need to be adaptable enough to take into account new slang as it evolves. In this case, the term ‘salty’ may be too new for some sentiment analysis models to accurately identify as a negative.
  
• “The ending of the film was horrifying.”
  Often, whether a word or phrase carries positive or negative sentiment depends on the context. In this example, the “horrifying” ending may indicate a positive response, provided that horror was what the viewer was hoping to experience. To identify this, the sentiment analysis tool would need to be capable of taking other factors into account. 

In each case, the best sentiment analysis tools are those that can help you see beyond the words, and grasp the meaning and purpose behind your customers’ feedback. 

Sentiment Analysis with InMoment

Sentiment analysis can help your business more easily quantify your customer’s experience, providing you with unique insights into your reputation, service, and products. But as digital channels open up ever-expanding sources of customers and user feedback, sentiment analysis tools must likewise scale to meet increased demand. Without the right sentiment analysis solutions, you may find that keeping track of what your customers are saying (and how they are saying it) is prohibitively expensive in terms of cost, effort, and time.

If sentiment analysis is a concern for your business, then we have the solution. 

InMoment, the leader in people-oriented text analytics, brings advanced sentiment analysis to businesses in industries around the globe. leveraging industry-recognized metrics and real-time intelligence gathering, combined with powerful survey capabilities across every common digital channel, InMoment sentiment analysis tools give you the power to quickly and easily gather the insights you need to optimize onboarding processes, enhance product experience, improve customer support interactions, and boost customer relationships like never before. 

Don’t let hidden sentiments hamper your success. Learn how InMoment’s CXInsight sentiment analysis tool can help you get the most out of your customers’ feedback.