Traditionally, customer support data analysis focused on quantitative aspects, such as response times and ticket resolution rates. While those metrics are well established performance indicators, they fail to contextualize customer service ticket data. To that end, conversational support analytics adds a qualitative layer, deciphering the sentiment, intent, and contextual nuances embedded in customer conversations.

If you are looking to contextualize your Zendesk Support conversations and go beyond mere transactional insights, conversational analytics can unravel the intricacies of customer needs, pain points, and satisfaction levels.

What are Conversational Analytics?

Conversational analytics refers to the systematic analysis of interactions between customers and support channels, particularly in written or verbal exchanges such as chat messages, emails, or phone conversations.

By leveraging natural language processing (NLP) and other advanced analytical techniques, conversational analytics enables customer-centric organizations to gain a deeper understanding of the qualitative aspects of customer communication to identify trends, patterns, and opportunities to enhance the customer experience, ultimately leading to more informed decision-making and improved customer satisfaction.

In platforms like Zendesk, conversational analytics plays a pivotal role in decoding customer conversations to provide a holistic view of customer interactions.

Conversational Analytics vs. Feedback Surveys

While customer feedback surveys have long been a staple for gauging satisfaction, conversational analytics emerges as a more dynamic and nuanced solution. Customer feedback surveys often rely on predefined questions, limiting the scope of responses to predetermined options. This structured approach may inadvertently overlook the subtleties of customer sentiment, as customers might find it challenging to express complex feelings within the constraints of a survey.

Conversational analytics, on the other hand, operates in a more flexible and adaptive manner. By analyzing customer interactions, it captures unfiltered and authentic expressions of sentiment and provides a real-time, contextual analysis of customer data.

Furthermore, conversational analysis can help you enhance and validate your understanding of strategic KPIs such as Net Promoter® Score (NPS), Customer Effort Score (CES), Customer Satisfaction (CSAT) you monitor via feedback surveys.

Zendesk’s Current Analytics Capabilities

Zendesk users can use the platform’s analytics solution Zendesk Explore to track and monitor quantitative metrics which can include ticket attributes or agent responses and performance KPIs. Zendesk Explore, however, does not offer built-in text or sentiment analytics capabilities.  These limitations make it challenging to contextualize and understand qualitative support ticket data.

How to Use Surveypal Insights to Analyze your Zendesk Conversations

Here’s what you can do by implementing Surveypal Insights into your Zendesk Instance:

Gauge Customer Sentiment

Implementing conversational analytics within Zendesk enriches customer support with a deeper understanding of customer sentiment, intent, and contextual nuances. This enables you to gain insights beyond surface level-metrics which can be used to craft personalized responses, identify emerging trends, address repetitive issues preemptively, and adapt support strategies dynamically.

Real-time Insights

One of the standout advantages is the real-time nature of conversational analytics. Unlike traditional surveys, which offer a window into customer perceptions post-service, conversational analysis provides you with immediate insights into ongoing customer interactions. This allows you agility in the context of daily support operations and the opportunity to adapt strategies by making data-driven decisions.

Automated Analysis of Large Ticket Volumes

By automating the analysis of large volumes of customer interactions you can uncover patterns, identify common issues, and streamline workflows. This automation not only saves time but also allows you to allocate resources more effectively, reducing response times and optimizing the overall customer support process.

Merge Contextual Topic Analysis with Zendesk Metrics

Surveypal Insights analyzes your Zendesk ticket data to automatically discover topics that emerge from customer conversations. This process enables you to capture insights without the need of manual coding. By merging contextual topic analysis with Zendesk metrics you can:  

1. Uncover process and knowledge gaps

Combining conversational analytics with operational metrics such as ticket reopen rates or ticket resolution rates you can gauge your teams’ ability to address and resolve issues associated with any give topic and assess the efficiency of processes such as ticket routing, support automation, and training agent training programs.

2. Evaluate the financial impact of your support tickets

Surveypal Insights crunches conversational data in conjunction with other operational metrics to derive the average cost per ticket on any topic. This way you can quantify the financial impact of your support operations and evaluate how addressing a product issue or a process inefficiency will lower your customer service costs.

3. Predict support performance

If every customer were to rate the support they received from your team, how would you score? The Predictive Performance Score is a metric designed to help you answer that question. In order to generate your Predictive Performance Score, Surveypal Insights analyzes your support tickets through the prism of more than 10 performance-focused metrics to deliver a more holistic image of your customer service experience.

Conclusion

Gaining qualitative insights from your Zendesk conversations is easier with Surveypal. Combine Zendesk Support with the analytical power of Surveypal to get actionable insights of your customer service data and gain a deeper understand of the customer experience.

In the dynamic world of customer experience, the Net Promoter@ Score (NPS) has been unraveling the intricacies of customer sentiment and loyalty for the past twenty years. As we celebrate this milestone, we’ll explore the origins of NPS, its benefits as a growth catalyst, and its resilience amid challenges.

Join us on the NPS anniversary through NPS’s history, acknowledging the minds behind its creation and early adopters. We’ll navigate the terrain of benefits, where NPS has been used to not only measure but steer companies toward growth and lasting customer relationships.

Brief History of the Net Promoter® Score

The Net Promoter Score (NPS) originated in 2003, conceived by Fred Reichheld, a partner at Bain & Company, and introduced through his seminal article in Harvard Business Review. Reichheld aimed to simplify customer feedback and loyalty measurement, giving rise to the straightforward question, “How likely are you to recommend our product/service to a friend or colleague?” The methodology categorizes respondents into promoters, passives, and detractors based on their likelihood to recommend, establishing a numerical scale.

NPS gained rapid traction, becoming a staple in business strategy due to its simplicity and actionable insights. Early adopters, including industry leaders like Apple and General Electric, recognized its potential to measure customer loyalty effectively. By the mid-2000s, NPS had permeated various sectors, cementing its status as a pivotal metric in the customer experience. The history of NPS underscores its journey from a conceptual framework to a ubiquitous tool, shaping how businesses gauge and enhance customer satisfaction.

Benefits of NPS

The Net Promoter Score delivers multifaceted benefits that propel businesses toward customer-centric success. In the following paragraphs we’ll discuss the key benefits of the Net Promoter Score:

Catalyzing Growth and Retention

According to a 2006 Bain and Company study, the NPS acts as a catalyst in driving growth and customer retention. The study revealed that NPS leaders consistently outpaced their rivals more than two times, affirming the role of the metric NPS as a springboard for sustainable market share expansion.

Informed Excellence

Companies leveraging NPS to identify pain points experience a 20% or more boost in customer satisfaction. NPS transcends guesswork, providing a robust foundation for strategic planning and continuous improvement as a wealth of statistics from NPS surveys can be transformed into actionable inisights that propel data-driven decision making.

Enhanced Customer Advocacy

The NPS serves as a potent tool for converting detractors into promoters. As per a global NPS benchmark report, companies adept at addressing detractor concerns witnessed a 50% increase in customer advocacy. The possibility to turn feedback into loyalty and advocacy position the NPS as a proactive driver of positive word-of-mouth.

Operational Efficiency

NPS insights can play an instrumental role in enhancing operational efficiency by precisely identifying areas for improvement. Empirical research underscores that businesses integrating NPS feedback into their operations realize a 25% reduction in service costs. Observe how NPS intricately streamlines internal processes beyond its impact on customer satisfaction. This seamless integration ensures a harmonious equilibrium between operational excellence and the pursuit of customer delight, positioning NPS as a strategic ally in optimizing business operations.

NPS Challenges and Criticisms

Despite its popularity, the Net Promoter Score has yet to be immune to scrutiny and critique. Over the course of the last two decades, challenges and criticisms that have emerged, shedding light on potential pitfalls that businesses should be aware of in their pursuit of measuring the customer experience using the NPS.

Potential Bias in Feedback

The simplicity of the NPS question, “How likely are you to recommend our product/service to a friend or colleague?” may inadvertently lead to biased feedback. A survey by CustomerGauge found that 30% of respondents felt the NPS question didn’t capture their whole experience, hinting at potential biases introduced by the simplicity of the metric. This underscores the importance of acknowledging that a singular question might not accurately encapsulate the complexity of customer sentiments.

Oversimplification of Customer Loyalty

While providing a straightforward categorization, the classification of respondents into promoters, passives, and detractors oversimplifies the intricate nature of customer loyalty. According to a study by Forrester Research, 45% of businesses find it challenging to translate NPS into actionable strategies due to oversimplified categorization. This limitation raises concerns about overlooking the nuanced reasons behind customer loyalty, potentially hindering the development of targeted improvement initiatives.

Neglect of Neutral and Negative Feedback

Critics argue that NPS prioritizes positive feedback, potentially neglecting valuable customer insights in the neutral or negative categories. A survey by Medallia revealed that 25% of customers who gave neutral or negative NPS scores felt their concerns needed to be adequately addressed. This criticism highlights the need for businesses to ensure an equal and thorough examination of feedback across all categories to drive holistic improvements.

Industry-Specific Variation

NPS effectiveness exhibits variations across industries, raising concerns about its universal applicability. A report by the Temkin Group showed that industries such as healthcare and utilities tend to have lower average NPS scores compared to sectors like technology and retail. Understanding these industry-specific nuances is crucial for interpreting NPS data accurately and tailoring strategies to the unique challenges within each sector.

Limited Contextual Understanding

A significant challenge associated with the NPS lies in its inability to provide context for the feedback received. A study by Qualtrics revealed that 60% of businesses need help understanding the context behind NPS. With context, interpreting the meaning behind scores becomes easier, enabling businesses to making informed decisions. This limitation underscores the importance of supplementing NPS with additional qualitative data to enrich the understanding of customer sentiments.

Competing Metrics

In the realm of customer experience metrics, a spectrum of tools, including Net Promoter Score (NPS), competes for attention alongside Customer Satisfaction Score (CSAT), Customer Effort Score (CES), Customer Lifetime Value (CLV), Customer Retention Rate, and Churn Rate. Each metric brings unique strengths and weaknesses, catering to distinct aspects of the customer journey.

Net Promoter Score (NPS)

Strengths: Provides a concise measure of customer loyalty and advocacy, offering a forward-looking perspective. Its simplicity allows for widespread adoption.

Weaknesses: Limited in-depth insights; categorizing promoters, passives, and detractors may oversimplify customer sentiments.

Customer Satisfaction Score (CSAT)

The Customer Satisfaction Score (CSAT) measures how satisfied customers are with a product, service, or overall experience provided by a company. It is often assessed through surveys where customers are asked to rate their satisfaction on a numerical scale or using descriptive terms.

CSAT is used. to gauge overall customer satisfaction, identify areas for improvement, and track changes in customer sentiment over time. High CSAT scores generally indicate that customers are content with the provided products or services, while lower scores may prompt businesses to investigate and address potential issues.

Strengths: CSAT surveys deliver immediate feedback after an interaction making the metric versatile for assessing satisfaction across different touchpoints. Additonally, CSAT scores can be benchmarked over time to track changes in customer satisfaction and to compare performance against industry benchmarks or competitors.

Weaknesses: CSAT lacks granularity as it doesn’t always reveal the specific reason behing a customers’ satisfaction of dissatisfaction. Furthermore, interpretation of satisfaction can be subjective as different customers have different expectations and perceptions of a “satisfactory” experience.

Customer Effort Score (CES)

The Customer Effort Score (CES) is used to measure the ease with which customers can complete a specific task or achieve a goal with a company, typically related to their interaction with a product, service, or support. It is designed to assess the level of effort a customer has to exert in order to get a resolution or accomplish a particular objective.

A lower CES generally indicates that customers find it easy to interact with a company and are more likely to have a positive experience. Reducing customer effort is often associated with increased customer satisfaction and loyalty. Companies use CES as a key performance indicator to identify areas where they can simplify processes and enhance the overall customer experience.

Strengths: Measures the ease with which customers can achieve their goals. It reflects the efficiency of customer interactions.

Weaknesses: It might not capture the full spectrum of customer emotions or loyalty. Primarily focused on transactional experiences.

What’s Your Customer Effort Score?

Even if you’ve been living in a cave, you must’ve probably had to interact with a brand, whether to make an inquiry, a purchase, or lodge a complaint. Subconsciously or not, you may have held certain expectations of convenience in your interaction with them.  If you think you’re alone in this sentiment, think again…

Read this article What’s Your Customer Effort Score?

Customer Lifetime Value (CLV)

Customer Lifetime Value (CLV or LTV) represents the total revenue a business can expect to earn from a single customer throughout their entire relationship. In other words, it quantifies the value of a customer to a business over the course of their engagement.

Calculating CLV involves considering factors such as the average purchase value, the frequency of purchases, and the duration of the customer’s relationship with the business. A higher CLV generally indicates a more profitable and sustainable customer base.

Strengths: Quantifies the total value a customer brings to a business over their entire relationship. Crucial for strategic resource allocation.

Weaknesses: Complex to calculate accurately; requires historical data and assumptions about future customer behavior.

Churn Rate

Customer Churn Rate is a business metric that measures the percentage of customers who stop using a product or service over a given period of time. It is a crucial metric for businesses, especially in subscription-based models or industries where customer retention is essential.

The churn rate is typically calculated as the number of customers lost during a specific time period divided by the total number of customers at the beginning of that period, multiplied by 100 to express it as a percentage.

Strengths: It quantifies the percentage of customers who discontinue their relationship with a business. It offers insights into customer dissatisfaction or external market factors.

Weaknesses: Provides information afterward; businesses may lose customers before detecting issues.

Customer Retention Rate

The Customer Retention Rate measures the percentage of customers a business has retained over a specific period. Unlike churn rate, which focuses on the customers lost, retention rate emphasizes the customers that a business has managed to keep.

A high customer retention rate is generally a positive indicator, suggesting that a business is successful in keeping its existing customers satisfied.

Strengths: Measures the percentage of customers retained over a specific period, reflecting loyalty and satisfaction.

Weaknesses: Does not differentiate between satisfied and dissatisfied retained customers. It might not capture overall customer sentiment.

Choosing the right metric depends on business objectives and the specific aspect of the customer experience under consideration. NPS excels in predicting customer advocacy, while CSAT and CES offer transactional insights. CLV, Customer Retention Rate, and Churn Rate focus on customer relationships’ long-term value and health. The optimal approach often combines multiple metrics to understand the diverse facets of customer experience comprehensively.

The Current State of NPS Metric

In the contemporary realm of customer experience assessment, the Net Promoter Score (NPS) retains its prominence as a pivotal metric widely adopted by businesses looking to decipher customer satisfaction and boost the customer experience. Its present-day relevance is underscored by its simplicity, versatility, and adaptability.

For many businesses across diverse industries, the NPS has evolved beyond a mere metric; it is now a strategic key performance indicator (KPI) that gives organizations a pulse on customer relationships.

In the financial sector, a bank’s focus on elevating NPS scores translated into a remarkable 30% increase in customer loyalty and a subsequent 25% surge in the average customer lifetime value. This showcases how NPS goes beyond measuring satisfaction to influencing crucial financial metrics, demonstrating its holistic impact on various facets of business performance.

As organizations navigate the complex landscape of customer-centric strategies, NPS remains a robust tool, offering a concise measure of customer satisfaction and loyalty. However, companies are becoming more and more aware of the limitations of the NPS with organizations shifting their attention to AI technologies to mitigate the challenges with which the NPS is associated and enhance their understanding of their customers.

How AI is Revolutionizing the NPS

Integrating Net Promoter Score (NPS) with AI-powered analytics is a revolutionary advancement transforming how businesses collect, analyze, and act upon customer data.

Enhanced Data Collection, Analysis, and Interpretation

AI technologies bring unprecedented efficiency to NPS data processing. Natural Language Processing (NLP) algorithms enable text analysis and sentiment analysis, allowing businesses to discern the emotional nuances in customer responses and contextualize the customer experience. Machine learning algorithms refine data interpretation over time, ensuring a more accurate understanding of customer sentiments.

Automating Follow-Up Actions and Personalized Responses

AI-driven automation streamlines the post-NPS feedback process enabling businesses to successfully close the feedback loop. This enables businesses to identify patterns in customer responses and conversations, automating follow-up actions based on predefined criteria.

This not only accelerates response times but also allows for personalized interactions. For instance, AI can promptly trigger automated workflows to address their concerns if a customer expresses dissatisfaction in an NPS survey.

What is Personalized Customer Service?

Making your customer service communication lines as easy, frictionless, and specifically targeted to individual members of your audience as possible is imperative in a world where a great deal of emphasis is placed on using digital technology and social media to provide elevated customer experiences.  Making a quantifiable impact on customers is facilitated by…

Read this article What is Personalized Customer Service?

Conversational Analytics

Over the last few years Artificial Intelligence has introduced a new era of customer intelligence – one where businesses can not only measure customer satisfaction more accurately but also predict it by analyzing existing historical data in combination with real-time customer conversations. Conversational analytics holds the power to transform the way businesses understand the customer experience and forecast satisfaction levels based on individual customer interactions.

Conversational Analytics – Benefits and Use Cases

Conversational analytics is an innovative approach that leverages advanced technologies to analyze and derive insights from customer conversations. By tapping into the vast amount of data generated through various customer communication channels, you can deeply understand your customers’ needs, preferences, and pain points. This article explores the intricacies of conversational analytics, including its working…

Read this article Conversational Analytics – Benefits and Use Cases

When used properly, the predictive power of AI analytics can be an invaluable tool which could address the NPS challenges mentioned in the previous paragraphs. For example, conversational analysis can enhance your understanding of what drives your promoters, detractors and passives as well as offer the context that is missing from NPS measurement.

Business Examples

Several businesses have successfully harnessed AI-powered analytics to elevate their NPS initiatives. Take the example of an e-commerce giant utilizing machine learning to analyze NPS feedback. The company identified pain points in the customer journey by automatically categorizing and prioritizing responses, leading to targeted improvements and a subsequent boost in NPS scores.

In the telecom industry, an AI-enhanced NPS approach facilitated proactive issue resolution. Machine learning algorithms flagged potential service disruptions based on NPS feedback, enabling the company to address issues before widespread customer impact, resulting in improved satisfaction and reduced churn.

Additionally, a financial institution implemented AI to personalize responses to NPS feedback. The system generated tailored responses by analyzing customer interactions and preferences, fostering a sense of being heard and valued. This personalization not only enhanced customer satisfaction but also contributed to increased loyalty and positive word-of-mouth.

The integration of AI with NPS not only expedites processes but also adds a layer of intelligence to customer service strategies. Businesses can enhance customer satisfaction and proactively address issues by automating tasks, providing personalized responses, and extracting actionable insights, ultimately contributing to a more robust and responsive customer experience.

The Future of NPS

The future of the Net Promoter Score (NPS) promises to be shaped by technological advancements and emerging trends in customer experience measurement. Artificial intelligence and machine learning are anticipated to play pivotal roles in refining NPS analytics, with companies like Salesforce reporting a 33% increase in customer satisfaction through AI-driven insights.

Predictive analytics is emerging as a significant trend, allowing businesses to forecast customer behavior based on historical data. For instance, Gartner predicts that by 2023, 40% of customer service organizations will leverage AI to augment customer engagement.

There’s a growing emphasis on real-time, omnichannel feedback collection to adapt NPS for the future. According to a Forrester survey, 73% of companies prioritize improving customer experience across channels, indicating a shift towards more holistic feedback mechanisms.

While NPS is likely to remain a dominant metric, adaptation is essential. The question of its continued dominance is nuanced, as evidenced by the growing popularity of the Customer Effort Score (CES). As companies embrace new methodologies, the future of NPS will rely on its ability to evolve, incorporating innovations that align with changing customer expectations and technological capabilities.

Wrapping Up

In conclusion, our exploration of the Net Promoter Score (NPS) has illuminated its enduring significance in the customer experience realm. From its inception two decades ago to its present-day integration with AI and analytics, the NPS anniversary remains a linchpin for businesses seeking to measure customer satisfaction and loyalty. The history unveiled the simplicity that became its strength, while the benefits showcased its impact on growth and retention.

Despite common challenges and criticisms, NPS continues to thrive, adapting to industry-specific nuances and technological advancements. Incorporating AI has streamlined data analysis and empowered businesses to automate responses and foster personalized interactions.

As we ponder the future, the trajectory of NPS appears poised for evolution, embracing trends like predictive analytics and omnichannel feedback.

Its enduring importance lies in its ability to encapsulate customer sentiments succinctly, shaping the future of customer feedback and steering businesses toward ever-improving customer-centric strategies.

In this dynamic landscape, NPS stands as a stalwart metric, navigating the currents of change and contributing to the ongoing evolution of customer experience measurement.

Making your customer service communication lines as easy, frictionless, and specifically targeted to individual members of your audience as possible is imperative in a world where a great deal of emphasis is placed on using digital technology and social media to provide elevated customer experiences.

 Making a quantifiable impact on customers is facilitated by implementing a tailored customer service approach. According to a Twilio survey, just 35% of businesses believe they effectively use omnichannel customization, indicating that most still need to figure out the financial return on investment.

It’s evident that personalization is now essential, and companies should concentrate on giving customers a tailored experience that meets their requirements and preferences.

It’s crucial to comprehend the idea of individualized customer service before adjusting to the changing ways customers behave.  

What Is Personalized Customer Service?

When consumers think of personalized customer service, they often recall conversations and chats in which they were addressed by an agent using their first name.

Although this is an exciting technique, customization is much more than that.

Using consumer information to create a customized experience for them is known as personalized customer service.

When you employ customer service customization correctly, your consumers will feel more appreciated and appreciate your efforts.

Numerous studies have shown how vital customization and customer service are to consumers. Over 80% of consumers are willing to pay more for superior customer service alone. They are willing to spend more for more costly products!

Customer Service Personalization Examples

Here are some real-life customer service personalization examples:

Amazon

Using AI and machine learning, Amazon’s tailored shopping experience examines your previous purchases. It makes your shopping experience efficient and pleasurable by offering suggestions for goods that align with your tastes. To increase consumer happiness and loyalty, Amazon also customizes the material on its site and makes recommendations for movies, books, and other items.

Spotify

Spotify is excellent at customizing accounts. You can make and customize playlists, follow other people’s playlists, and discover new music. Based on your listening history, its AI-powered recommendation engine proposes music and playlists. Spotify’s user-friendly design allows you to pick themes and create profiles, establishing a feeling of community among music enthusiasts.

Bank of America

Bank of America’s 24/7 customer service is made possible with AI and tailored chatbots. These virtual assistants can analyze user data and respond appropriately. This way, they may increase client trust and pleasure by proactively sending account activity updates and relevant information depending on your choices.

Starbucks

Starbucks’ mobile app and loyalty program demonstrate their mastery of individualized customer care. Customers may use the Starbucks app to pay with their smartphones, order ahead of time, and get incentives. Starbucks’ use of consumer data to provide tailored incentives and promotions is impressive. Starbucks could send you special offers or discounts on the drink or snack you choose if you’re a frequent customer. This degree of customization not only encourages brand loyalty among consumers but also keeps them coming back.

Netflix

The most popular streaming service, Netflix, is well known for its tailored content suggestions. It examines your watching habits and past selections using advanced algorithms to provide personalized movie and TV program recommendations. Whether you favor romance, science fiction, or documentaries, Netflix’s suggestions make it simple to find new shows and movies you’ll like. This degree of customization is an excellent example of improving the streaming experience since it keeps people interested and subscribed.

Strategies to Deliver Personalized Customer Support

It is essential to include customized customer service in your customer relationship management strategy for many reasons. It’s advantageous for your company’s bottom line as well as for the welfare of your clients, who may end up becoming repeat business.

Let’s take a look more closely at some of the strategies to deliver personalized customer support:

Leverage Customer Data

Use the customer data you’re gathering—more than you probably realize—to your benefit. Find information about the browsing and buying patterns of your clients so that you may customize your messages to their particular preferences.

This eliminates uncertainty in your dealings with customers. Based on your client’s interests, you may, for example, utilize this information to create tailored emails or provide well-informed product suggestions.

Create an Omnichannel Customer Service Experience

An omnichannel strategy may significantly increase the effectiveness of individualized customer care. It offers assistance and cross-channel troubleshooting in addition to assisting companies with marketing and sales of their goods and services.

Omnichannel increases consumer comfort when connecting with you and helps you minimize friction between touch points. Because the purchase process is consistent, it further boosts client retention and brand loyalty.

You may get insights into the behavior and intents of your customers by implementing an omnichannel approach. In light of client expectations, you may enhance your response system, value propositions, and brand communication.

Train and Coach Your Agents

Documentation, such as pre-written responses, may be used to give guidance to customer interactions. However, it’s also a good idea to give your support workers the leeway to deviate from the script and make each contact with a customer their own.

Providing coaching and training opportunities only adds the personal touch we discussed before but also allows you to adapt to each customer’s specific demands. Having agents manage the relationship makes it seem more authentic.

Humanize Support Interactions

Everyone who interacts with your company should be handled with respect and kindness, no matter what channel they use or what point they are at in their customer journey. That’s the very least you need to provide for your consumers to avoid starvation: the gas station sandwich.

For example, using the customer’s name in conversation is an easy way to communicate that you care about them beyond their problem. They won’t be made to feel like just another number to be processed and moved along.

Using the customer’s name in the conversation establishes a personal connection and shows you care about solving their problem. Using one another’s names can help build trust and camaraderie.

Offer Recommendations and Custom Solutions

Most companies are beginning to understand the importance of data and analytics management. An ineffective understanding of the client journey will arise from the neglect of customer data.

Find out what experiences your customers have had with your company. You can use text analytics or customer sentiment analytics to determine their dislikes, preferences, and pain areas. This apporach can enable you to effectively adjust internal processes, training methods, and other areas to elevate their experience with your brand.

Customer feedback data may be used to address negative experiences instantly. Using technology to gather customer data is an additional choice— customer Relationship Management (CRM) software for monitoring user behavior and past purchases.

Identify Customer Preferences

A customer journey encompasses all the stages a customer takes while engaging with your company, from discovery to conversion.

The customer journey map helps you visualize the many phases that make up the process. It is a system that provides a real-time summary of each customer’s experience at different points in the trip.

As a result, you can see the areas where the customer’s expectations are not being met and fix them before they become worse. Using a trip map makes it easier to identify and resolve problems in your conversion cycle without completely stopping it.

It enables you to provide an exceptional client experience, which boosts conversion retention and happens quickly. It also promotes client loyalty and lowers attrition. 

Utilize Business Analytics Tools

With customer service analytics, you can learn more about your consumers based on how they use and engage with your company rather than just what they tell you. The process of monitoring is intricate and tricky. It requires sifting through a large number of raw data to get a contextual understanding of your consumers’ expectations. However, the benefits of such an investment in individualized service to customers may be substantial.

Provide Options

You may significantly improve your customers’ perceptions of your service quality by tailoring the support experience to their preferences and past actions.

As a first step in customization, you may utilize the customers’ actual names. They will feel more appreciated, allowing you to make an excellent first impression. Through an omnichannel strategy, you may also enrich the experience by furnishing customers with resources like frequently asked questions (FAQs), knowledge bases, blogs, articles, and industry studies that are tailored to specific phases of the customer journey.

If there are any snags in the sales process, they can locate assistance and contact you from any touch points. Providing clients with individualized discounts or the option to earn loyalty points might help increase their likelihood of purchasing.

Enable Automation

Automation of customer service has advanced significantly. It may save expenses, optimize your workflow, and lessen strain and conflict for your staff and clients. Many, however, believe that automation is inherently hostile to customized customer care.

This is only sometimes the case, however.

Automation may be a powerful tool for customization if it is implemented with the consumer in mind, giving them the option to make their own decisions.

For example, providing an automated shopping system to guide clients through their purchases may be helpful, especially if they are repeat consumers and already know about your goods. It’s quick, effortless, and practical.

Make Use of Chatbots

There may be various drawbacks to using human representatives at a customer care desk. This is especially true when managing the massive volume of data gathered from many platforms to optimize and build a customer profile.

However, the problem may be effectively resolved by integrating AI technologies into customer support systems. Chatbots are advanced AI interfaces that can gather consumer insights across channels, build precise customer profiles, and utilize the data to answer routine questions tailored to the user’s preferences.

These chatbots can recognize the online behavior of customers using real-time customer data and respond appropriately. In addition, you can program the chatbots to respond to the most frequent questions automatically. It finally improves the client experience by increasing your availability and reaction time.

Offer Self-Service Options

Offering self-service options is a win-win for businesses and customers alike. By providing user-friendly interfaces, comprehensive knowledge bases, and AI-driven tools, companies empower their customers to find answers and resolve issues independently.

This not only saves time and resources but also enhances customer satisfaction and loyalty. Self-service options are the key to delivering efficient and personalized support, ensuring that customers can access the assistance they need on their terms.

Ask For Customer Feedback

The last step is to conduct consumer surveys to learn more about their needs and wants. Gathering client feedback can help you fine-tune your approach to customized customer service based on how consumers interact and feel.

Your customization features’ usefulness and popularity may be tracked via user reviews, and any modifications can be implemented once the data has been analyzed.

Deliver Proactive Support

Delivering proactive support is the future of exceptional customer service. Instead of waiting for customers to reach out with problems or questions, proactive support involves anticipating their needs and addressing issues before they arise. By using data and AI-driven insights, businesses can provide timely solutions, offer personalized recommendations, and enhance the overall customer experience. This approach not only prevents issues but also fosters customer trust and loyalty, making it a strategic imperative for modern businesses.

Why Is Personalized Customer Service Important?

When a company offers personalized service, they focus on meeting customers’ unique wants and requirements. Customers who get personalized experiences tend to have stronger brand loyalty.

Among the many advantages of personalized customer service are the following:

Improved Customer Satisfaction

Having customized service solutions in place provides your company with the data required to create very intimate connections with clients. Sometimes, it’s as easy as using the customer’s name up front instead of requiring them to provide it or use a placeholder.

Businesses may build deeper relationships with their consumers and boost customer satisfaction through personalization.

Enhanced Customer Interactions

Faster service delivery means happier customers who can get on with their day sooner rather than later. In addition to making clients feel that the company cares about them, personalized service will demonstrate that it is adaptable to their specific requirements.

Businesses may reap benefits from providing speedier customer care, including increased productivity among customer-facing staff, better use of available resources, and shorter response times.

Increased Loyalty and Trust

Personalized customer service may assist in enhancing customer loyalty by forging a stronger bond between the company and the client. Businesses may develop trust and trustworthiness that is difficult to obtain via more general marketing tactics by personalizing messaging, goods, and experiences to specific consumers.

Providing individualized service demonstrates to consumers that you care and helps them feel valued and appreciated, resulting in loyalty and retention.

Increased Retention

Personalization may also contribute to higher customer retention rates by lowering the churn rate, which is the rate at which customers leave a company. Customers are less inclined to explore other options when they have a tremendous and customized experience with a company they patronize.

Conclusion

Ultimately, by providing a more tailored and relevant experience for consumers, customization may have a significant influence on customer happiness and loyalty. Personalization methods help firms build more significant connections with their consumers, increasing customer happiness, loyalty, and engagement and eventually leading to better business results.

My name is Pauli and I have been working at Surveypal as a frontend developer for about four years even though I have never received any official training or education in computer science. In fact, my field of expertise is officially in the world of classical music, more precisely composition, theory and history.

I have been extremely passionate both about music and programming since I was a child. I have tried and failed over and over again to create simple graphical effects and games with QuickBasic on an old Intel 286 just as much as i have tried and failed to create music on the classic Impulse Tracker 2.14.

Before I joined Surveypal, programming was a hobby: creating static web pages with PHP, developing games with C#, attempting to build web apps using JavaScript and JQuery. My experience with modern web development technologies like TypeScript, React and Node.js was practically zero, but that didn’t scare me: I saw an interesting job posting, I applied, and I got in.

Everyone at Surveypal has been supportive of me joining the company, its codebase and its culture.

I never expected any workplace, especially my first workplace, to be so open to new people with such a limited skill set, yet Surveypal has certainly been that.

Programming can be extremely difficult without passion. Often a programmer must think in a way that’s unnatural for humans, and even simple problems may not be as straightforward as you might think. It is a creative endeavor where one must try and fail and try again, which requires a lot of patience, and that patience comes from passion.

So, in a way, writing code and writing music are not that different from each other, are they?

Now I am a reasonably experienced developer in some of the most relevant technologies of the day, but the journey is not over. No-one is ever a complete programmer. Even those who have been in the business for decades must learn new things regularly. Sometimes it’s a new programming language, sometimes a new style, sometimes new software, sometimes a whole new paradigm. That is the nature of this industry, and it is for the better not just for the industry, but also for personal cognitive health, mental health and life balance.

On the side, I write lots of original orchestral music and arrange music from video games, anime and films for symphony orchestras, choirs and wind bands. I also play video games, mostly focusing on one titled Overwatch 2, and some board games, mostly focusing on one known as Riichi Mahjong. If I have the time, you might catch me watching a game of hockey or pesäpallo.

How do your customers feel when they engage with your brand? This is an important question to answer because customers rely on their emotions (how they feel) or can be influenced by the emotions of others when deciding on a product or service.

Figuring out what your customers feel when they engage with your brand can be the difference between business success or failure. This is where customer sentiment analysis comes in!

Sentiment analysis helps you measure customer emotions, giving actionable insight into what to offer to make your customers happy.

But what exactly is customer sentiment analysis, and how can you use it to boost your business efforts? This comprehensive guide will answer all those questions. Let’s get started.

What is Customer Sentiment Analysis?

Customer sentiment analysis is the process of examining online communication to find out how customers feel about your product or service.

Sentiment analysis involves fine-combing customer data to identify specific emotions. It helps determine whether customers have positive, neutral, or negative views of your brand.

Positive sentiments are usually expressed with words like “happy,” “good,” “wonderful,” “great,” “recommend,” etc. Words such as “bad,” “hate,” “terrible,” “sucks,” “can’t recommend,” etc. are associated with negative sentiment. Neutral reviews are conveyed with words like “fair,” “average,” “don’t know,” “maybe,” “can’t say,” etc.

Deducing the specific emotions that your customers are having when they engage with your products will give you insights that will help you make the right decisions. For example, knowing your customers’ sentiments on variables like product features can help you with product improvement. When you know aspects of your product that are frustrating customers and features they’d love to see in the product, you can tweak your product accordingly, offering exactly what makes your customers happy.

Netflix used customer sentiment analysis in this way with great results. The company’s sentiment analysis revealed that people were frustrated with certain glitches in the Netflix app, especially after installing some updates. Knowing this, the company worked on solving the technical glitch.

Also read: Customer Service Analytics Explained

Sources of Sentiment Analysis Data

The sources of reliable customer data for customer sentiment analytics include: 

• Customer feedback
• Support interactions
• Social monitoring
• Customer reviews
• In-app ratings
• Voice of the customer programs (VoC)

Customer Feedback

Customer feedback refers to information your customers provide about their experience with your brand. While all types of customer feedback are great for sentiment analysis, direct customer feedback is especially useful.

Direct customer feedback is obtained when you specifically reach out to customers to request their thoughts/opinions.  

This type of customer feedback is one of the best for sentiment analysis because each customer can immediately see your intention for requesting their thoughts. Also, it gives them the feeling that you value their opinions.

To gain accurate information about customer sentiment, request information on as many touchpoints as possible. You can reach out to customers to collect feedback by doing the following: 

• After resolving support tickets (ask them how they’ll rate their experience
• After a new feature/ product is launched (ask them what they think of the feature)
• Via website surveys

Support Interactions

Support interactions refer to any communication between customers and your customer service representatives.

Customers who reach out to you seeking help are good data sources for sentiment analysis because they tend to offer more personal information.

Customers reaching out to support usually have one issue or another, making these interactions good sources of neutral and negative sentiments. 

When you look at support communication for your customers’ issues, you’ll easily see the emotions they are experiencing, giving you insights into creating a better customer experience.

Some examples of customer care interactions for sentiment analytics include:

• Support emails
• Support tickets
• Support chat logs
• Support call logs

Also read: Service Desk Basics: Cost Per Ticket

Social Monitoring

Social monitoring means tracking what your customers are saying about your products/ services and brand on social media platforms. It involves tracking data such as mentions, comments, likes, and hashtags.

About 5 billion people around the world use social media to stay connected, express themselves, and share their thoughts. These people include your customers, and their thoughts include positive and negative reviews about your products and brand (from praise to complaints and everything in between).

Thus, social monitoring is a good way to obtain reliable data to track customer sentiments. Because of the real-time nature of social media, social monitoring enables you to respond to custom complaints in real time, helping you improve customer satisfaction.

Customer Reviews

Review sites allow customers to share their opinions and ratings about specific products and brands. Some of these platforms serve specific industries (such as TripAdvisor for the tourism industry), while others are generic (like Trustpilot).

These platforms allow customers to share the experience they may have had with a product or brand, good or bad. So, they are good sources for positive and negative sentiments. Monitoring them will give you insight into aspects of your business that your customers like and areas they are unhappy with.

In-app Ratings

An in-app rating is a score that users assign to a mobile application within the app, indicating their overall satisfaction with the app.

In-app ratings usually come in the form of star ratings, ranging from one to five stars (where one represents the lowest and 5 represents the highest rating). Individual star ratings are then used to compute a summary rating for your app.

In-app ratings also come with reviews, as users are allowed to explain the reasons for their rating. These ratings and accompanying comments about the app can help you deduce customers’ emotions toward making decisions that improve customer satisfaction.

Voice of the Customer Programs (VoC)

A Voice of the Customer (VoC) program is the entire process of collating customer feedback in one place, analyzing the data to see what customers care about and why, and acting on the customer feedback to create a customer-centric culture.

Companies that effectively run Voice of the Customer (VoC) programs enjoy 10 times greater year-on-year increases in annual company revenue compared to others.

You can start a VoC program with one customer survey type, like Net Promoter Score (NPS) or Customer Satisfaction Score (CSAT). This helps you make customer-centric decisions. You collect data from traditional and non-traditional sources for analysis to understand how your customers feel at different stages of their customer lifecycle, then act on the data.

Manual Customer Sentiment Analysis

Manual customer sentiment analysis is the process of deducing customers’ emotions by manually examining customer feedback data. Manual sentiment analysis does not involve using specialized software to extract customer emotions.

The steps for performing manual sentiment analysis include the following:

Choose Feedback Channels

Decide on the channels you want to collect custom feedback from for sentiment analysis. For effective analysis, it’s best to collect data from different sources, especially data from support interactions, customer reviews, and social monitoring.

Collect Customer Feedback

Next, collect the customer feedback data in one place. Google Sheets and Excel are popular for manual sentiment analysis for support.

Categorize the Feedback

Read the text of each feedback, then use the language, tone, and context to separate it into positive, negative, or neutral sentiments. Certain words can immediately reveal that feedback reveals a positive or negative customer sentiment. 

For example, “happy” in the feedback, “I am happy with the product,” shows positive sentiment. 

But sometimes, language alone may be misleading (hence the need to use tone and context when categorizing feedback). For example, while “long time” may reveal a positive sentiment for product durability, it reveals a negative sentiment for customer service time.

Identify the Topic Each Sentiment is About

Each category of feedback will cover different topics. To help you understand what your customers are happy or unhappy about, you should dig deep into each feedback to identify the topic each sentiment is about.

Thus, you can link customer’s emotions to specific aspects of your business (e.g., product availability, ease of use, customer service wait time, etc.).

Rank the Sentiments

Customer emotions are not equal, so it is best to use the tone of each feedback to determine the severity of the expressed emotions.

Consider the two negative feedbacks below:

• I am not happy with XYZ’s extra-slim model.
• The XYZ’s extra-slim model sucks. It’s an absolute disgrace that such crap can come from a company of XYZ’s standing.

While both feedbacks show negative sentiments, it’s clear that the second is more damning than the first. So, having them on an equal footing will not reflect people’s real feelings appropriately. 

This is where using sentiment scores comes in. A sentiment score lets you rank each sentiment, helping you consider the weight/ severity of customers’ sentiments during analysis.

Analyze the Recorded Sentiments

After identifying and ranking customer sentiments, proceed to analyze them to understand patterns and trends in customer feedback.

For example, you can look at the most common themes (to identify aspects of your business that elicit the most customer engagement), the themes with the highest sentiment score (to identify what your customers are least pleased with), etc. 

This analysis offers eye-popping insights about your business, such as identifying areas for improvement.

Challenges of Manual Sentiment Analysis

Extracting customer sentiment manually is only possible if you’re a small organization and your customer feedback data is not very large.

Even when you “fit the bill,” going at it manually comes with challenges, such as:

Stressful

Manual sentiment analysis involves a lot of work, as it involves manually looking for customer feedback, importing them to a spreadsheet, reading and sorting them, etc. This is what makes manual sentiment analysis impractical when you have a large volume of data.

Time-consuming

The process of manually tracking user sentiment is a long one. The time spent can be used for more productive activities.  

Bias

Manual sentiment analysis can be full of human biases. For example, bias enters when assigning scores to sentiments to establish their weight/severity.

Automated Customer Sentiment Analysis Tools

Automated customer sentiment analysis uses specialized software to deduce the emotions that people feel when they engage with your products or brand.

Automated customer sentiment analytics tools help you avoid the challenges of manual analysis. Some of the benefits include: 

• The tools take the work off your hands so you gain insights without stressing yourself.
• They give you more accurate insights as the automated tools eliminate human error and biases. 
• They serve you valuable insight quickly as they can crunch large volumes of customer feedback data quickly.

These tools are broadly divided into two categories:

Lexicon-based software

These tools know a vocabulary of words/ terms, which they associate with the different emotions (positive, negative, and neutral). They scan customer feedback data for these keywords and use them to calculate the overall sentiment of the feedback.

However, these tools struggle to understand linguistic nuances (like sarcasm, irony, etc.) and ambiguities. 

For example, consider the feedback, “Thank God this item finally arrived.” A lexicon-based sentiment analysis tool will not see the sarcasm in the feedback and will classify it as positive.

AI-based software

These tools use machine learning and natural language processing (NLP) to provide more accurate sentiment analysis. Natural Language Processing allows AI-based tools to easily solve many of the problems of lexicon-based tools.

The algorithms help the tool to look beyond keywords but also analyze context. So, it can detect sarcasm, irony, negation, etc.

Some of the top automated tools for analyzing customer sentiments include:

MonkeyLearn

MonkeyLearn is a powerful AI platform that lets you use machine learning to extract text from various sources (such as email, chats, and documents) and analyze them for insight, saving you hours of manual data processing.

MonkeyLearn is easy to use. You simply connect to your data, quickly turn your text into tags using premade models, and use the tags to extract new information about your business. 

The software integrates with over 1,000 web tools (including Zendesk) and is extremely easy to use. 

Also read: The Benefits of Text Analysis For Support Teams

Rosette

Rosette is an AI text analytics solution that understands human languages. Language identification is the first step in any text analysis or natural language processing (NLP) pipeline. 

If the tool misunderstands the language, all subsequent models will produce inaccurate results. This is where Rosette Text Analytics shines.

When customer feedback data has multilingual text, Rosette is a great tool for accurately analyzing it for customer sentiment. 

The software can extract key pieces of information from unstructured data and interpret content in a fraction of the time it takes many other applications. 

Rosette can be easily integrated with applications you already use via APIs, allowing optimization without costly system replacement.

Clarabridge

Clarabridge is an AI-powered text and speech analytical tool that can help you collect and analyze customer feedback across several touchpoints.

Its natural language processing engines evaluate text to determine contextual meaning, helping you deduce customer emotions accurately. 

It uses built-in categorization, an 11-point sentiment scale, semantic analysis, and more.

It also integrates with your existing CRMS, allowing you to analyze customer sentiments comprehensively.

Idiomatic

Idiomatic advertises as an AI-driven customer intelligence platform. The platform can help you track customer sentiment and identify the “why” behind your customer feedback.

You can create custom data labels organized in easy-to-understand categories, surfacing trends you may not have seen before. The tool offers different sentiment analysis models for each data source. This helps you track more accurate sentiments by channel. 

Getting Started with Customer Sentiment Analysis

Below is a step-by-step guide for beginners to start tracking customer sentiment effectively:

Define Objectives and Goals

Why do you want to measure customer sentiment? Answering this question helps you define your objectives and goals, giving you direction. 

For example, your objectives determine the kind of feedback data you’ll use or the kind of analysis you’ll run on the data.

Choose the Data Sources that Align with your Objectives

The best customer feedback data for sentiment tracking depends on your objectives. So, after defining your objectives, the next step is to gather feedback data from the appropriate source. 

For example, in-app ratings and reviews are good sources of feedback data to evaluate the sentiments of your app’s users, while social monitoring is a good data source to gauge the general public’s sentiments.

Select the Appropriate Sentiment Analysis Method (Manual or Automated)

After choosing data sources that align with your goals, the next step is to decide on an appropriate sentiment analysis method.

You may choose to manually analyze customer sentiment if your data is not large. But when you have large volumes of data, then use automated tools. 

Even a small company can have 100k+ mentions in a month. It’ll be back-breaking to read through each one to deduce the sentiment.

Gather and prepare your data

You can manually upload your data to the analytic tool by downloading the user comments and feedback in a .csv file. Automated tools make things easier as you can connect to your data sources via direct integrations or APIs.

Once the data is in the pipeline, natural language processing, semantic classification, and other models clean and categorize it so that it can be analyzed for sentiments.

Perform Sentiment Analysis

After categorizing feedback and identifying themes/ aspects, perform an analysis to identify patterns in the data, determine the proportion of positive to negative sentiment, etc.

Interpret the Results and Take Action

The objective of sentiment analysis is to understand the motivation for customer behavior and take action to improve customer satisfaction.

Thus, the final step in customer service sentiment analysis is to interpret the results as they affect your business and take action. 

Real-world Success Stories and Examples of Customer Sentiment Analysis

Many companies have achieved significant improvements using customer sentiment analysis. Let’s explore some of these stories and the results. 

Airbnb

Airbnb is a platform that matches people looking for accommodation in a particular city to people willing to rent out their place. The company used sentiment analysis to understand the true feelings of its users.

Challenge: Airbnb guests and hosts have real-life interactions that often force the guests to leave inflated reviews for the hosts. Thus, guests’ star ratings and reviews on Airbnb were not the true feelings of guests.

Solution: Airbnb used sentiment analysis to analyze its guests’ feedback on third-party review platforms. 

Result: The analysis helped Airbnb understand the true feelings of guests.

Starbucks

Starbucks, the premier retailer of specialty coffee, is another company at the forefront of using customer sentiment analysis. Starbucks uses sentiment analysis to track poor customer experience for effective management.

Challenge: Starbucks gets a lot of mentions, as there’s an average of 10 tweets about the company every second. To effectively respond to poor experiences, it needs a system to track the numerous user reviews and identify negative sentiments.

Solution: Starbucks used sentiment analysis to interpret the deluge of customer feedback it gets from social media.

Result: With sentiment analysis, Starbucks easily crunches its vast customer feedback data and compiles them to identify poor customer experiences. 

Management also sifts through information to identify public opinion on different aspects of business, such as cleanliness standards at specific Starbucks locations. This helps Starbucks to respond to poor experiences quickly. It also helps the company make important decisions toward service improvement.

Netflix

Netflix is one of the biggest online streaming providers of video-on-demand distribution. The company uses sentiment analysis in different ways, including to predict the performance of certain Netflix Originals.

Challenge: When Netflix adds a movie, the marketing department will want to know what users feel about it and use this information to predict its performance.

Solution: Netflix used sentiment analysis to analyze users’ feelings about its Original series, House of Cards. The analysis looked at users’ data from Twitter and compared it with reviews on IMDb (Internet Movie Database).

Result: Sentiment analysis revealed that users highly rated the movie, and this information was used to predict that the movie would continue to lead the trend.

Benefits of Customer Sentiment Analysis 

Reduces Escalations

Early detection of negative sentiments in customer communications can enable you to prompty address the issue before it escalates.

Reduces Resolution Times

Sentiment analysis helps you understand the underlying emotions in customer feedback, reducing the need for multiple back-and-forths to clarify and resolve issues. 

Automated tools can also identify negative sentiments in real time and automatically route tickets to appropriate agents, allowing for increased first contact resolution or faster overall resolution. 

Enhances Support Productivity

Sentiment analysis automatically classifies customer feedback based on sentiments and reveals the underlying emotions in complaints. This helps your customer service team boost productivity and ultimately offer better support. 

Personalizes Customer Interactions

By understanding when a customer is happy, sad, frustrated, or angry, support agents can tailor their response accordingly and personalize the service to the needs and concerns of individual customers.

Decompresses Backlog

Since sentiment analysis can help reduce resolution time. You can, therefore, handle more support complaints, decompressing backlogs. 

Anticipate and Reduce Churn

If customers have poor experiences when they engage with your product or service, they’re likelier to leave you, increasing your churn rate. 

Sentiment analysis helps you understand customers’ feelings, which you can use to predict churn rates. You can then do something about it by responding to unhappy customers and doing something to fix what makes them unhappy.

Boost Customer Satisfaction, Retention, and Royalty

Sentiment analysis helps you understand what makes customers happy so you can offer more of it or what makes them unhappy so you can fix it and make them happy. 

Therefore, sentiment analysis boosts customer satisfaction, which improves customer retention and brand loyalty. 

Improve Products and Service Offerings

Understanding how customers feel about your products helps you identify problem areas in your offerings. You can then work to fix these, thereby improving your products and better meeting clients’ needs. 

Design Better Coaching and Training Programs

Analyzing customer sentiments over time can help you identify areas where agents need improvement. This can help you create training and coaching programs to address specific agent weaknesses. 

You may also integrate sentiment analysis into KPIs for agents, whereby you evaluate them based on their ability to maintain positive customer sentiment.

Common Challenges with Customer Sentiment Analysis

Ambiguous Words

Sentiment analytic tools often struggle to capture nuances of language, especially with words having different meanings. 

For example, “House of Dragons is killing it.” Models will classify the sentence as negative because of the keyword “killing,” but it is positive feedback that means that the show is extremely good. 

Sarcasm and Irony

People often use sarcasm and irony when expressing negative feelings. This entails using a positive-sounding word to express a negative emotion.

For example, “Thank God, the Jordans finally arrived.”  Models may classify the feedback as positive when it is negative.

Double Negation

There are certain keywords that sentiment analysis models see as negative sentiments. However, negations reverse their meaning. 

For example, “The pasta is not bad” or “The movie is not unpleasant.” Algorithms will classify the feedback as negative because of the keywords “bad” and “unpleasant.” However, the negations before these words make the sentences positive.

Subjectivity

Sentiment is subjective and can vary from person to person. What one person considers as negative may be neutral or positive to another. Sentiment analysis models do not account for these differences.

Best Practices and Tips to Make the Most of Sentiment Analysis

The following will improve the accuracy of sentiment analysis results:

High Quality Training Data

Train your algorithm with large data sets representing the domain and language you’re analyzing.

Data Preprocessing

Clean your data to remove noise (such as special characters and irrelevant information) and fix abbreviations.

Select the Right Model

Choose the sentiment analysis model that aligns with your needs. You may choose a lexicon-based or an AI-based model. Consider using a model that supports multiple languages if your data involves multiple languages.

Regular Model Update

Keep your sentiment analysis model up-to-date by periodically retraining the algorithm on new data to adapt to changing language patterns and slang.

Takeaway: Tap into Customer Sentiment to Deliver Positive Experiences

Customer sentiment analysis helps you know how your customers feel when they engage with your business.

Understanding whether your customers have positive or negative sentiments can be the difference between business success and failure. 

When you identify positive sentiment, you can offer more of what generated it to improve customer delight. Additionally, when you identify negative sentiment, you can respond appropriately to solve the issues and fix the problem areas.

Thus, if you haven’t started, you should start paying attention to how your customers feel.

It’s also important to stay updated with the latest trends in sentiment analysis. Algorithms and models are continuously developed to enhance the accuracy of sentiment analysis.

This gives you a competitive advantage, as you’ll be able to understand customers more effectively, make data-driven decisions, and promptly respond to market changes.

Wouldn’t it be great if you were able to automatically analyze all your Zendesk tickets to gain actionable insights?

Customer service interactions produce a goldmine of data that can be used in many ways to improve support performance and the customer experience. Most of us, however, do not have enough time or resources to comb through this data and make sense out of it manually. The good news is that AI has come very far and technologies such as text analytics enable you to examine your customer experience data on a deeper, more insightful level.

If you are thinking of leveraging AI to power up your Zendesk with text analysis, then you are on the right track. Unfortunately, the limitations of Zendesk’s analytics capabilities leaves you with three options.

1. You could take the manual approach, open excel, and try to extract meaning from your textual data by yourself. This could actually work if you have an extremely low volume of customer service inquiries. In any other case, this approach is time and resource consuming.
2. Ask your data scientists to write scripts to extract insights from your support conversations. If you are lucky enough to have a data scientist in your company, chances are you will have to compete with other departments in your organization for their time. Additionally, the analysis of your Zendesk interactions will not happen in real-time leading you to miss out on valuable insights.
3. You can perform automatic text analysis by using a tool built specifically for Zendesk.

Keep on reading to find out what are the benefits of text analytics tools for customer service teams and how to enhance Zendesk with an automated text analysis solution.

What is Text Analytics?

Text analytics, in the context of customer service, refers to the process of extracting deeper insights and meaning from unstructured textual data, such as customer feedback, emails, chat*, ticket content, etc. In its essence, it transforms the wealth of unstructured customer data into actionable intelligence, driving informed decision-making and delivering more efficient and effective customer service experiences.

Text analytics harnesses natural language processing (NLP) and machine learning techniques to systematically analyze and understand the sentiments, preferences, and concerns expressed by your customers in written form therefore, enabling your team to identify patterns, trends, and emerging issues, facilitate proactive responses, personalize support, and enhance overall customer satisfaction.

Making the Case for Automated Text Analytics in Zendesk

Here are some of the main reasons why you need a text analytics solution in Zendesk.

Improved Customer Understanding:

Gain deep insights into customer sentiment, needs, and pain points. By analyzing customer tickets and messages, you can identify recurring issues and understand preferences, to tailor your support efforts more effectively.

Enhanced Issue Prioritization:

With text analytics, you can automatically categorize and prioritize tickets based on urgency and severity. As a result, you can quickly address critical issues and allocate resources efficiently, leading to faster response times and improved customer satisfaction.

Automated Ticket Routing:

Automatically route tickets to the most suitable agents or departments based on the content and context of the customer’s request. This ensures that customers receive assistance from the right experts, reducing resolution times and minimizing frustration.

Proactive Issue Resolution:

Text analytics empowers you to identify emerging trends and potential problems before they escalate. This proactive approach allows you to address issues early, preventing negative feedback and customer churn.

Personalized Customer Support:

By analyzing historical interactions and customer profiles, text analytics apps can provide agents with valuable context during conversations. This personalization enhances the customer experience by making interactions more relevant and empathetic.

Efficient Knowledge Management:

Index and categorize a vast amount of textual information, making it easier for agents to access relevant knowledge articles and resources when handling tickets thus streamlining issue resolution and reducing agent research time.

Quality Assurance:

Text analytics can assist in monitoring and evaluating the quality of customer service interactions. It can flag issues like compliance violations, inconsistencies, or missed opportunities for upselling, ensuring that the service provided aligns with company standards and Service Level Agreements (SLAs).

Cost Reductions:

By automating ticket categorization, routing, and initial responses, text analytics can help you optimizing resource allocation to reduce operational costs while maintaining or even improving support quality.

Training and Coaching:

Text analytics tools can help you systematically evaluate agent performance by examining an array of data such as response time, accuracy, and ability to resolve issues at first contact. By analyzing big volumes of customer care interactions, you can identify recurring issues or common challenges your agents may face and design targeted training modules or coaching sessions to address them.

Competitive Advantage:

Leveraging text analytics in customer service can set your businesses apart from competitors. It demonstrates a commitment to understanding and meeting customer needs, which can result in customer loyalty and positive word-of-mouth recommendations.

Surveypal’s Text Analysis Solution for Zendesk

When it comes to text analysis tools for Zendesk Support, Surveypal offers one of the most well-rounded solutions in the market. Here’s the main reasons why:

Contextual Topic Analysis

Analyze customer interactions, such as emails, chat logs, support tickets, and social media conversations, to understand not just the keywords or topics being discussed, but also the context in which they are mentioned. As a result, Surveypal generates granular, root-cause-level insights that can be used to improve productivity, CX, and sales.

Integrate Contextual Topic Analysis with Zendesk Metrics

Make your data work harder by combining contextual topic analysis insights with your Zendesk KPIs to gain a comprehensive view of customer outreach, pinpoint areas of friction, and a deeper understanding on how customer perceptions and sentiments impact your core perfrormance metrics.

Predictive Performance Score

If every single customer that reached out to you with an issue were to rate the level support they received, what would that score be? If this score existed, it would give you a much more accurate overview of the state of your customer service experience. Surveypal is able to generate a Predictive Performance Score based on your existing customer care data – including text analytics data- and get you a step closer to making decisions based on data that is not limited to small subset of interactions but represents the perceptions of your broader customer base

Multilanguage and Multichannel Coverage

You customers might speak many different languages and choose to contact you through a variety of channels – such as email, chat, messaging apps, etc. Surveypal’s text analytics solution automatically covers multiple languages and channels to ensure that every customer’s voice is heard.

Open – ended Feedback Analysis:

Aggregate and analyze customer feedback to identify common themes and areas for improvement. Combine unstructured customer feedback data analysis with KPIs and structured metrics to shape product development and service enhancements.

TL;DR

Here are the most important takeaways from this article:

• Automated text analysis enhances your ability to better understand your customers, offer superior support, and make data-driven decisions that impact your bottom line and improve the customer experience
• Zendesk does not offer built-in text analytics capabilities but fear not, text analytics solutions such as Surveypal Insights can be easily integrated into Zendesk and deliver real-time insights
• Find Surveypal Insights in Zendesk Marketplace to make the most ofthe textual data in your support environment

Hey there customer service pro!

If you have landed on this page, I’m guessing you’re in need of some comic relief – after all customer care is challenging and we all deserve to take a break and have a laugh every once in a while. With that in mind, I scoured the worldwide web to find the funniest customer service memes and what I found didn’t quite make me LOL. So, I created some myself and here they are for the internet to behold.

Feel free to share those customer service memes with your customer service friends or, alternatively, slide into my LinkedIn DMs to explain to me in detail why I’m not as funny as I think I am.

10 Funniest Customer Service Memes (According to Yours Truly)

1. Knowledge is Power

You have to take care of your support reps if you want them to take care of your customers . Sometimes, all it takes is proper training and coaching.

2. This guy

Saying you’re customer centric is one thing. Being customer-centric is a whole different story which requires a Voice of Customer strategy.

3. Not Technically a Customer Service Meme…

Still funny tho .

4. When your Customer Care Ticket Backlog Does You Wrong

Sometimes customers reach out on multiple support channels in hopes of getting a faster response. This might be why your backlog is increasing. Or, it might just be that you have to create a knowledge base, invest in self-service, or design processes to offer proactive support to get your ticket volumes to decrease.

5. What Does it Take to Elevate Customer Service?

One thing’s for sure. It takes much more than meeting your Service Level Agreements .

6. The Million Dollar Question

This is actually the best case scenario. The worst case scenario would be that CSAT (Customer Satisfaction) is decreasing and you have no idea why .

7. Are Customer Feedback Surveys Dead?

At the dawn of the era of AI-powered customer service analytics, customer feedback surveys are taking the back seat. Do you agree?

8. How Many Times is Too Many?

In an ideal world, customer service requests should be resolved at first contact and when they are not, it’s everybody’s loss

9. What He Said

Listen to Morpheus. Morpheus is cool.

10. Customer Service as it’s Meant to Be, Am I Right?

What if everyone had this reaction after contacting support? What would that world look like?

In the dynamic landscape of business, customer experience (CX) has emerged as the cornerstone of success. Amidst the buzz of technological advancements and evolving consumer expectations, one constant remains: the significance of an exceptional customer journey. Businesses have come to realize that it’s not just about selling a product or service; it’s about delivering an immersive experience that resonates with customers on a personal level.

In this blog post, we’re diving into the world of customer experience through the eyes of seasoned CX professionals who we interviewed for our series Support Spotlight to gain firsthand insights into the challenges they face, the strategies they employ, and the lessons they’ve learned along the way.

From navigating the intricate realm of customer satisfaction, delving into the nuances of human interaction and weaving strategies that leave an indelible impact – let these real-life customer experience quotes inspire and challenge you.

Quotes on The Importance of CX

A seamless and memorable customer experience not only drives initial purchases but also cultivates lasting loyalty, transforming one-time buyers into brand advocates. Businesses that prioritize customer experience tap into a powerful avenue for differentiation, forging emotional connections that resonate deeply and foster sustainable growth.

Quotes on CX Technology

Technological advancements have revolutionized the way businesses craft and deliver customer experiences. From personalized recommendations based on browsing history to instant, round-the-clock customer support through chatbots, technology enables companies to anticipate and fulfill customer needs with unprecedented precision and speed. Integrating these innovations not only enhances convenience but also showcases a company’s commitment to staying at the forefront of service, further elevating the overall customer perception and solidifying its competitive edge in the market.

Quotes on Organizational CX Structure

Building a customer-centric organization is paramount as it instills a culture where every decision and action is guided by the goal of exceeding customer expectations. By prioritizing the customer experience, a company demonstrates its dedication to understanding and addressing the unique needs of its clientele. This approach fosters loyalty, encourages repeat business, and ultimately propels the company towards sustainable success by creating a virtuous cycle of positive feedback and growth.

Quotes on CX Leadership

Effective CX leaders leverage their strategic vision to drive organizational alignment, foster a culture of continuous improvement, and create an environment where customer needs are at the forefront of decision-making – but what’s the missing link?

Quotes on CX Challenges

Implementing CX initiatives comes with its share of challenges for professionals in the field. Navigating organizational resistance to change often proves daunting, requiring adept communication to demonstrate the long-term benefits of customer-focused strategies. Here’s what our CX professional have to say about identifying and navigating CX challenges:

As our journey into the world of customer experience through the eyes of CX professionals comes to an end, one resounding truth emerges: crafting exceptional customer journeys requires dedication, strategic planning, and a genuine understanding of human dynamics. Would you agree?

In today’s hyper-connected world, customer service is no longer just a department within a company but a pivotal differentiator that can make or break a business. To stand out in a competitive market, companies must prioritize exceptional customer experiences. One technology that has emerged as a game-changer in this realm is the concept of digital twins.

Digital twins are virtual replicas of physical assets, products, processes, or systems that can be used to improve customer service in remarkable ways.

In this blog post, we’ll delve into the fascinating world of digital twins and explore how businesses can leverage them to enhance customer service through real-life use cases. 

Understanding Digital Twins 

Before we dive into the applications, let’s grasp the fundamentals of digital twins. Imagine having a virtual doppelganger of your physical assets, equipment, or products that mirrors their real-world behavior in real-time. That’s the essence of a digital twin. It allows businesses to simulate, monitor, and optimize assets remotely, gaining valuable insights into performance, maintenance, and potential issues. 

Using Digital Twins to Improve Customer Service

Streamlining Customer Support through Real-time Monitoring 

One of the most significant advantages of digital twins in customer service is their ability to monitor products and services in real-time. Companies can create digital twins of their products or systems and continuously monitor their performance. By collecting data and analyzing patterns, businesses can predict potential failures, address issues proactively, and provide timely support to customers. For instance, an Internet of Things (IoT) enabled digital twin of a smart home device can detect anomalies, enabling customer support teams to troubleshoot remotely or dispatch technicians with the right information, thus reducing downtime and customer frustration. 

Enhancing Personalization through Customer Insights 

Delivering personalized experiences is a top priority for modern businesses. Digital twins play a crucial role in achieving this goal by providing valuable customer insights. By integrating digital twins with customer relationship management (CRM) systems, companies can gather comprehensive data about individual customer interactions, preferences, and behavior. Armed with this knowledge, support agents can offer tailored solutions, anticipate needs, and build stronger, lasting relationships with customers. For instance, a digital twin linked to a customer’s usage patterns can suggest personalized product recommendations or anticipate potential issues, allowing proactive customer service outreach. 

Facilitating Remote Assistance with Augmented Reality (AR) 

Digital twins can be coupled with augmented reality (AR) technology to enable remote assistance like never before. When a customer encounters a problem with a product or service, support teams can leverage AR-enabled digital twins to visualize the issue from the customer’s perspective. This capability allows agents to guide customers step-by-step through troubleshooting procedures or repairs, leading to quicker resolutions and improved customer satisfaction. For example, an AR-powered digital twin of complex machinery can guide field technicians or customers through intricate repair tasks without the need for on-site visits. 

Optimizing Products and Services through Digital Simulation 

The power of digital twins extends beyond customer support. They can significantly contribute to improving products and services. Companies can create digital twins to simulate the performance of new products or services before physically launching them. These simulations provide valuable feedback and enable companies to identify potential flaws or areas of improvement. By refining offerings through this iterative process, businesses can deliver more reliable, efficient, and customer-centric solutions. 

Minimizing Downtime and Disruptions 

Downtime is a nightmare for both customers and companies. Digital twins enable predictive maintenance, reducing the risk of unplanned downtime. With real-time data from the digital twin, companies can detect early warning signs of equipment failures or potential disruptions. By scheduling proactive maintenance based on actual usage and wear, businesses can minimize downtime, optimize resources, and uphold a seamless customer experience. 

Improving Supply Chain Efficiency 

Digital twins are not limited to just customer-facing aspects. They can also transform the supply chain landscape. Creating digital twins of the supply chain can help companies gain visibility into inventory, logistics, and production processes. As a result, they can respond quickly to changes, optimize operations, and meet customer demands more efficiently. Improved supply chain efficiency directly impacts customer service by ensuring timely deliveries, reducing stockouts, and enhancing overall reliability. 

Conclusion 

The rise of digital twins represents a groundbreaking paradigm shift in the world of customer service. By harnessing the potential of digital twins, businesses can streamline customer support, enhance personalization, facilitate remote assistance, optimize products and services, minimize downtime, and improve supply chain efficiency. Embracing this technology demonstrates a commitment to exceptional customer experiences, ultimately fostering loyalty and brand advocacy. 

In the ever-evolving landscape of customer service, digital twins offer a transformative solution that can keep businesses ahead of the competition. As technology continues to advance, we can expect even more innovative use cases of digital twins in customer service, further solidifying its status as a vital tool for fostering customer satisfaction and business success. So, it’s time for businesses to embrace the potential of digital twins and take customer service to the next level. 

Today, business success depends on the development of customer-centric experiences. To that end, Voice of the Customer data delivers insights on individual and segment motivations which can help you measure and manage the customer experience.

What is the Voice of the Customer?

The Voice of the Customer (VoC) refers to the collective insights, opinions, needs, expectations, and preferences expressed by customers about a product, service, or brand. It is a critical business concept, aiming to capture and understand the sentiments of customers to shape and improve business strategies and offerings

Many firms develop a Voice of the Customer (VoC) strategy with the intention to enhance the customer experience and propel business growth. VoC analytics is complimentary to customer journey analytics and can be used to evaluate the customer experience across touchpoints and over time.

A successful implementation of a VoC strategy helps support a set of activities which comprise a closed-loop feedback process.

More to the point, a Voice of the Customer strategy enables you to:

• Listen to your customers
• Analyze the resulting data
• Act upon it to improve the customer experience, and
• Monitor results

How can I capture the Voice of the Customer?

As the VoC has grown to become the backbone of the customer experience, it is important to have a clear idea of the different types of data you can gather when implementing a VoC strategy. There are three types of VoC data:

Direct feedback: This is the type of feedback your customers intend to provide your organization with. Direct VoC data refers to any touch-points in the customer journey whereby the customer expects the business to be listening. Direct feedback, typically, comes in the form of a survey, market research, written complaint, formal letter, a forum/panel, etc.

Indirect feedback: Feedback which refers to instances when the customer is speaking about or interacting with the organization but does not necessarily have the intention to give feedback to the business. This includes feedback extracted from social networking sites, review sites, or customer care interactions conducted in a variety of communication channels such as email, chat, phone, etc.

Inferred feedback: This is feedback derived from transactional, behavioral and operational data associated with the customer experience or the customer journey across different touchpoints. This type of feedback is extracted from historical data and is the hardest to capture. Examples of inferred data include website click-stream data, purchase history or contact center data.

Why you should I be familiar with different VoC feedback sources?

It is crucial to discern the three different sources of VoC data and where they come from in order to obtain a holistic perspective on the customer experience. VoC data can be used for a wide range of purposes but it all starts with a basic understanding of the business requirements of VoC analytics and a clear specification of the analytical outputs you set out to achieve. Once this initial step is complete, you can proceed with deciding which feedback sources are the most relevant to your business.

The general rule is to use more than one source to collect data. This way you will get feedback through a variety of diverse channels and compare the information to make sure you are getting consistent and accurate insighst. However, not all feedback sources are equally relevant. Depending on the industry some feedback sources might prove to be more valuable than others. Once you pinpoint the data sources which are most relevant to your business you can identify and establish appropriate channels through which you can gain access to that data.

What is a Voice of the Customer strategy?

A Voice of the Customer strategy is in essence a process which allows your company to capture customer data and feedback from different channels, analyze, and act upon it to improve the customer experience. One of the most challenging aspects of setting up a VoC strategy is to establish which metrics to track.

There are several VoC metrics you can track but, ultimately, you should set up those metrics that make the most sense for your company. The reason behind this is that VoC metrics help guide effective decision making towards business success. It is, also, important to remember that just because somebody else is using a specific metric you shouldn’t feel compelled to use it as well.

Where to start with Voice of the Customer metrics

At the early stages of establishing a VoC strategy it is crucial to identify the business challenges you are dealing with. This process will guide decision making regarding the metrics you need to track. Furthermore, it will ensure that the strategy supports your overall business objectives.

Another point to take under consideration before implementing any metrics is stakeholder engagement. A successful VoC program requires that all internal stakeholders are on board. To ensure stakeholder engagement you must align VoC objectives with their business/department objectives. Your main goal is to gain insights from VoC data that is of value to various internal stakeholders and can drive desired outcomes. This is an additional step to help you identify appropriate Voice of Customer metrics for your organization.

Finally, before diving into VoC metrics, it is essential that you link KPIs and loyalty metrics. You can go ahead and start collecting feedback only after you examine background data already in your database. You can later use this data to analyze customer segments alongside feedback. This will, also, give you a better understanding on which metrics you should start tracking.

The most important Voice of the Customer metrics

As mentioned earlier, customer feedback comes in three different forms. Along the customer journey you can capture direct, indirect, or inferred feedback. The goal is to collect data from more than one source and establish the most suitable metrics to track. Here is a list of the most well-established Voice of Customer metrics.

Net Promoter® Score

The Net Promoter® Score (NPS) measures whether a customer would personally recommend your product or service to a family member, friend, or colleague. The NPS is widely popular because it’s easy to understand and calculate. Additionally, you can benchmark your company’s score against the average industry NPS and see how you perform in comparison.

Customer Effort Score

The Customer Effort Score (CES) is calculated based on the premise that the less effort a customer asserts to get an issue resolved, the better. The CES is best suitable to call centers or customer support environments. The challenge with the CES is that it uses a different scale that other metrics. Thus, internal stakeholders find it difficult to understand and compare with other metrics.

Customer Satisfaction Score

Even though the customer satisfaction score is not considered the best indicator for future financial performance, the metric is still widely used in transactional surveys. Its purpose is to pinpoint how customers feel about a specific element of the overall experience you are measuring.

Loyalty Index

As the name suggests, this metric measures loyalty. You can calculate the index by taking an average across a number of questions that pertain to customer loyalty behavior. Combining the results from these questions generates an accurate loyalty index with more predictive power.

Drawing everything together

Setting up Voice of Customer metrics is clearly not enough to provide measurable Return on Investment (ROI). But, it is one of the first steps you need to take towards that end. Choosing the metrics and linking them to business goals and other KPIs and financial indicators will help you ascertain the ROI of your VoC strategy.

Customer experience practitioners often ask the following question: “How do you measure the monetary value of a Voice of Customer?”. The simplest way to answer that question is the following equation:

Happier customers = increased profitability

At a macrolevel, evidence shows that companies exhibiting an ongoing commitment to the customer experience show higher percentages of growth rates than companies responding slowly to the customer experience. A Voice of the Customer strategy delivers timely and relevant data that help drive the systematic improvement of the customer experience. This process, however, takes time to pay off financially. The real challenge, therefore, lies within making a business case that determines the VoC initiative’s return on investment at a microlevel. In other words, you must prove that your VoC strategy generates business value to justify the ongoing investment.

How to build a Voice of the Customer strategy in 5 steps

Voice of the Customer strategies generate data that improve customer experience analytics and provide insights that steer business transforming decision-making. In order to yield positive results and cater to short and long term business goals, design your Voice of the Customer strategy based on company needs, available resources and desired outcomes that support a cycle of four activities that comprise a closed-loop feedback process: capturing customer feedback, gaining insights, reacting to improve the experience, and monitoring the results. So, how do you go about creating a successful VoC strategy?

1. List your company’s most promising feedback sources

Customer feedback comes in three forms: direct, indirect, or inferred. The first thing you need to do is make a list of established feedback sources that fall into those categories. Later, expand the list by adding sources of feedback that you are not currently utilizing but would provide you with valuable customer insights.

2. Rank feedback sources based of volume and value

At this point you should start rating the feedback sources you have collected in step one. Be systematic in doing so. Determine beforehand the business goals you try to achieve and proceed with rating the feedback sources depending on whether they help you reach these goals. Keep an eye out for feedback volume and value per source/channel.

3. Pinpoint the most appropriate feedback sources for your company

After you have a clear understanding of the available feedback sources, you should determine which of these sources are most relevant to your business. Unless you have unlimited resources, you are not going to be able to track every single feedback source known to man. Figure out which ones are most useful to your business and the people that make decisions for it.

Step 4. Start with the highest-ranking feedback source

During this fourth step, you need to start optimizing. You should now know which sources to track. Therefore, you need to enter in execution mode. If your highest-ranking sources are already established within your company you need to make sure they are optimized. If your highest-ranking sources are not established, you should put in place processes that allow for feedback to be collected through those sources and find its way back to you.

Step 5. Visualize VoC data

This final step is the fruition of your VoC strategy. You now have VoC data rolling in. It is your job to make it count. You should make sure you translate the data into a language that everybody understands and make it accessible to the right people. AI-driven tools and are your best chance to help you with this as they display data in a condensed and simple form.

To ensure the continuity and success of a Voice of the Customer strategy you need to frequently revisit these steps. Your VoC strategy should reflect the challenges and opportunities your business is faced with. Therefore, it needs to develop and grow alongside your organization. Working your way through these steps, whenever necessary, will enable you to turn VoC insights into a competitive advantage that shapes the customer experience.

How to measure the ROI of a VoC strategy

In business contexts we measure value in terms of money. As a result, business executives need to know the monetary value of expenditures. This information helps them assess benefits against costs to determine the value delivered. The problem with VoC initiatives is that they produce qualitative benefits which you cannot measure with traditional accounting metrics. For that reason, it is necessary to establish new measures of value that you can tie back to your traditional accounting metrics. These measures of value will help you quantify the ROI of a voice of customer strategy and enable you to convert its intangible benefits into monetary benefits. Here is how to achieve that:

Establish the main business outcomes various stakeholders expect to achieve with the VoC strategy

Within your organization there are different stakeholder groups involved with your VoC strategy. These groups are tasked with key responsibilities in terms of implementing the strategy, but, at the same time expect certain business outcomes out of it. Organize these different stakeholders and use measurable business outcomes to define the rewards you expect to recap from the Voice of the Customer.

Clearly define a starting point from which to implement the VoC strategy

After selecting the business metrics and before, actually, implementing the VoC strategy assess how your organization is currently performing in regards to the metrics established. This will serve as a comparison measure in the future. You cannot assess how your VoC strategy performs unless you have something to compare that performance against.

Start collecting VoC feedback and act on it

At this point, you need to consider how the collection and analysis of this data will affect existing operational processes. You need to be aware that certain VoC metrics have an impact on certain operational processes. Let’s say you send out an NPS survey to your customers after they’ve had an interaction with a customer support agent. A low NPS score from a customer will result to a customer call-back. Therefore, the VoC metric impacted the operational process of customer support. Your job is to define all the instances within the operational process that can be impacted by the VoC strategy. Later, you can decide how to improve the business process according to the feedback. This ensures that every department in the company will aligns its goals with the improvements that need to take place.

Convert the necessary improvements into monetary value

Once a VoC metric has pinpointed an improvement that needs to happen in the operational process, your financial department needs to take over. The financial department is tasked with mathematically tying VoC insights with accounting metrics to determine monetary value. When completing this step, you will have a clear idea of the costs and the benefits of the VoC strategy. If the costs exceed the benefits, the strategy is not viable – you have to rethink it. If the benefits exceed or are equal to the costs then the VoC is a viable initiative for your organization.

Who owns the Voice of the Customer?

Your ability to understand how to meet customer expectations has a significant impact on business strategy and overall success. A voice of the customer strategy provides you with insights that help deliver a superior customer experience. So, who owns the Voice of Customer? Think of the customer experience as a car. Voice of the Customer data is the fuel that allows the car to move. If you own the car, you sure own the gasoline in its tank. Therefore, whoever owns the customer experience owns the Voice of Customer. The million dollar question, therefore, is:

”Who owns the customer experience”?

A very common misconception is that the marketing department owns the customer experience. Another view is that the sales department owns the customer experience. But, maybe instead of asking who owns the customer experience you should ask whether your company is actually prioritizing the customer experience. And, that is a leadership responsibility.

Even though, successful voice of customer strategies require an all hands on deck approach, the executive level is responsible for encrusting the customer experience into the company’s DNA. After that, the successful implementation of a voice of the customer strategy is a matter of execution.

Capturing the voice of the customer is a process that requires cross-company integration of insights. The customer-centric organization demands interconnected metrics, processes and people. In this context, everything starts at the top and company leaders must ensure that voice of customer is measured in relation to revenue and other organization-wide customer experience metrics to determine ROI.

What is everybody else’s role?

The Voice of the Customer has the power to transform the customer experience. Every company representative has a role in making a difference based on whatever insights the VoC findings reveal. As a result, you need to make sure that the entire team is engaged and committed. This way you can expect action and accountability from everyone. Make sure that your people share in a common vision and adopt behaviors that guarantee the impeccable execution of the improved customer experience.