What is Voice of Customer Analytics and Why Does it Matter to CX Teams?

VoC analytics is a powerful tool that helps businesses understand their customers' needs and preferences. Explore this guide to learn more.
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Customer support leaders face the growing challenge of consolidating data from multiple channels. 

As customer interactions extend beyond traditional phone calls and emails, businesses grapple with an overwhelming influx of customer data from varied sources. This dispersion of data across numerous platforms poses a significant hurdle, making it difficult for customer support teams to understand the Voice of the Customer (VoC) comprehensively.

This is where a Voice of Customer Analytics can alleviate the issue by consolidating and analyzing the data for you. But how does it work, and how do you leverage it effectively at your organization? 

In this article, we'll explain this concept while showing you how to leverage Dashbot to simplify this process.

What is Voice of Customer Analytics?

Voice of the Customer (VoC) Analytics is a solution that collects and analyzes feedback from customers to understand their wants and needs. 

The goal is to understand their expectations at every stage of the customer journey—so that you can cater to them. And this is also why it's not a one-time activity. Instead, it's an ongoing process requiring continuous monitoring and analysis—making it tedious for customer support, marketing, and sales teams.

The feedback comes from various sources, either qualitative surveys or quantitative sources like sales and customer support data. But stitching these up together is a challenge. It also makes the decision-making process a lot harder because there's always a context gap at every step, preventing companies from drilling down into what makes their customers tick.

This is why VoC analytics solutions are essential, as they incorporate artificial intelligence (AI) technologies like sentiment analysis to understand the language and tone of the feedback. As a result, they can fill contextual gaps in their customer data reports.

Why is it important to measure VoC?

The most important benefit of measuring VoC is that you get to make customer-driven decisions, which positively impact your revenue. 

Too often, companies get stuck in the motion of evaluating the market based on their assumptions instead of what's actually happening. But the only way to truly understand the ripple effects of customer sentiment is if you go right to the source.

Also, with the number of disparate sources available, creating a narrative about your customer's expectations becomes harder when you don't have the data to support it. Eventually, making a case to leadership about the value of your work as a customer support or customer experience department becomes challenging.

Senior Analyst Colleen Fazio from Forrester says it boils down to two issues: an inability to connect outcomes with data and a lack of communication around measuring VoC. 

“The ability to say the return on the investment is really low in the CX (customer experience) team responsibilities, and that filters down to the VoC team. The skill sets around showing value and connecting outcomes with metrics are low,” says Fazio.
“I also think there's a communication problem. People assume there's a fallacy around assuming communication has occurred and putting up a dashboard, and they assume everyone's just going to go do things with it. But there's a change management piece to this that's really hard. And as I said, that Insights to Action Gap is where we see a lot of companies getting stuck.”

So invest in a dedicated VoC solution to continuously monitor omnichannel customer feedback. But don’t just stop there. Make strategic decisions based on these customer insights to tie the outcomes of these decisions to hard-hitting data.

What are the steps to analyzing VoC?

Here are three steps you can use to analyze VoC data:

1. Gather omnichannel data

The first step in analyzing VoC is to collect customer feedback from various channels such as online surveys, social media, emails, live chats, call recordings, and online reviews. This can be done through automated systems, feedback forms, or third-party tools. Some of these sources include:

  • Chatbot transcripts from a website or social media bots
  • Support calls with live agents
  • Support emails for asynchronous support
  • Customer survey data from tools like Medallia, Qualtrics, or SurveyMonkey
  • Social media triaging tools like Sprinklr
  • Blog or social media comments from LinkedIn, Twitter, etc.
  • Help center data like feature requests
  • Phone data like SMS, MMS, and chat apps

So integrate your tech stack with Dashbot or use its API to build custom integrations and connect all these sources.

2. Analyze the customer data

Typically, the data analysis process requires three steps:

  • Data preparation: Clean, normalize, and transform data to remove inconsistencies, duplicates, and biased entries.
  • Natural language processing (NLP): Use text mining to extranet valuable insights from unstructured customer feedback. Then, use sentiment analysis to understand the context and sentiment behind the feedback.
  • Data correlation: Identify themes and underlying topics by clustering feedback. It helps you determine common pain points, suggestions, or issues. 

With Dashbot, you can remove this manual process altogether. It uses proprietary large language models (LLMs) and machine learning (ML) to transform customer feedback into actionable insights automatically. 

3. Interpret and report on the analyzed data

To make sense of your data, use visualization tools like charts and graphs for dashboards to present the data. It makes it easier for you and your team to understand and interpret the insights without missing critical details.

Dashbot does all of this for you without manual inputs from your end. It creates a tailored dashboard (based on your preferences) and turns unstructured data into actionable insights. It goes beyond quantitative metrics to show you exactly why a certain topic or sentiment is trending right now—allowing you to devise targeted resolution strategies. It's beneficial for CX or support leads who must create executive briefings to report their findings and make data-driven decisions. 

5 ways to leverage VoC analytics in your organization

Here are five use cases for VoC analytics—and how you can leverage them:

Understanding the context behind customer conversations

It’s challenging to understand the context of a customer’s journey with a brand when you can’t connect the different touchpoints to make sense of the data. With Dashbot, you can pull omnichannel data and feed that into our conversational AI platform.

The rest is up to the bots. It’ll analyze each customer interaction and give you a complete executive report for these interactions. 

Let’s say your contact center has registered 100,000 conversations this month. And most of them were focused on queries like “returns/refunds,” or “incorrect size.” Dashbot parses the sentiment and ties it to each support question. In this case, these are negative sentiment conversations. Then, you get a complete overview of the most pressing customer issues in one dashboard.

Sentiment analysis to pinpoint the nuance of customer conversations

Sentiment analysis focuses on understanding customers' emotional tone and sentiment in their discussions. Dashbot uses LLMs that incorporate natural language processing and text analytics technologies to detect and categorize customer sentiments as positive, negative, or neutral.

Analyzing sentiment at scale allows you to uncover patterns and trends that reveal the drivers behind customer satisfaction or dissatisfaction. It enables proactive intervention to address issues, personalize interactions, and enhance customer experience.

Combining survey data with call data to understand the root causes behind customer issues

Cross-reference survey data with call data to identify common themes or issues that emerge during customer support interactions. 

For instance, if a particular product feature receives consistently negative feedback in surveys, dig deeper by analyzing call recordings related to that feature. That’ll give you more context on the problem, allowing you to understand the root cause faster.

Armed with these insights, you can take targeted actions to address the root causes, refine your products or services, and enhance customer satisfaction.

Discovering new product or marketing ideas from customer data 

Analyze customer feedback from various channels, such as customer surveys, social media comments, and online reviews, to determine: 

  • Customer preferences
  • Current unmet needs
  • Emerging support trends

VoC allows you to identify customer suggestions, feature requests, or pain points that can drive innovation. For instance, if more customers request information on your product's AI lead routing feature, create educational material around it. This shows them you're listening to and willing to act on their concerns.

Analyzing best-performing support representatives to help train other agents

You might have 10 customer support agents or 80, but ultimately, your team is only as strong as its weakest link. This is why agent training is an ongoing process, and you need to be armed with internal data to show them how it's done.

Pick your best support reps and analyze their call recordings. Feed them into Dashbot and let it identify the top themes, objections, concerns, and phrases that bring in more deals. This analysis helps identify the specific skills, techniques, or knowledge areas that set the best-performing support reps apart.

Use this data to train your reps and improve their skills too. As on-the-job training is necessary, it'll significantly improve your department's competency and bottom line.

Invest in VoC analytics to build a customer-centric business 

Understanding and meeting customer expectations is not a nice-to-have anymore. With rising competition and customer acquisition costs, it's time to invest in a solution that helps you reduce customer churn rates. 

Ideally, you should analyze qualitative and quantitative data to get a complete picture of what customers need from your company. Now businesses can go beyond surface-level insights and delve deeper into customer sentiments, preferences, and pain points. VoC analytics is a rich source of inspiration for innovation and growth. Through continuous data mining, you can eventually build a business that's known to be customer-centric, snowballing your growth over time.

Interested in learning how Dashbot can help you streamline the VoC analysis process? Book a demo with us today!

FAQs for Voice of Customer Analytics:

  1. What is the Voice of Customer?

The Voice of the Customer (VOC) is a term used to describe the feedback or opinions of your customers regarding your product or service. It can be collected through various methods such as surveys, social media, customer reviews, and feedback forms. By mining these insights, you can create a better customer experience.

  1. What is Voice of Customer Analytics?

Voice of Customer Analytics (VoC) refers to analyzing customer feedback and opinions to gain insights into the customers' needs, wants, and preferences. It helps businesses tailor their products and services to meet their needs.

  1. How do you analyze Voice of Customer?

Analyzing VoC involves three steps:

  1. Gather omnichannel data: Consolidate customer data from channels like email tickets, social media forums, chatbots, etc.
  2. Analyze the customer data: Feed the data into a solution like Dashbot that uses proprietary LLMs to ingest, extract and transform the data.
  3. Report the analyzed data: Use Dashbot to create comprehensive executive reports to help stakeholders identify areas driving and draining revenue.
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