Voice of the Customer Analytics: 6 Steps to Better Customer Insights

Voice of the Customer (VoC) programs live and die based on two main factors: the quality of the data you put in and the analytics you run that data through.

Unfortunately, both of these factors are far more complicated than they first appear. Not all data is created equally, and making sure you’re collecting a relevant, representative, and timely sample set requires the right tools and plenty of industry expertise. 

On the other hand, your data will only be as good as your eventual analysis. If you’re not sure how to analyze the data you’ve collected, then you won’t get the insights you’re looking for. 

All of this brings us to the crucial topic of Voice of the Customer analytics. In this guide, we’ll introduce you to Voice of the Customer analytics and show you how to improve your analytics so your new or existing VoC program is giving you the insights your company needs to grow and thrive in competitive markets.  

What is Voice of the Customer Analytics?

Voice of the Customer analytics is the practice of refining your data collection practices and analysis methodologies to get insights that more accurately represent your customers’ sentiments as they are right now. 

At a very high level, a VoC program is like a machine where customer data goes in one end and customer insights come out the other.

Everything that happens between data input and customer insights is the realm of VoC analytics. To be as useful as possible, you want to ensure that the insights your VoC program produces are:

• Recent: Insights that reflect a reality that is a year old aren’t very useful in fast-moving industries. 
• Relevant: Insights should be relevant to your specific offerings or products.
• Accurate: Insights should actually represent the general attitudes of your customers. 
• Specific: Insights should tell you something specific about your products or business.

By investing in VoC analytics, you can make sure that the insights you get out of your program continually hit these standards and help you meet your business goals. 

Why is Creating a VoC Analytics Strategy Important?

Investing in VoC can be a costly venture, requiring money, time, and effort to get the customer insights you’re looking for. However, if you aren’t doing everything you can to optimize your data analysis methods, then all of that effort could be wasted.

To get the best possible results from your VoC program, you need to refine your VoC analytics to make them as representative and insightful as possible. By doing so, you’ll see advantages like:

• Enhanced customer understanding: VoC analytics provides deep insights into customer needs, preferences, and pain points, allowing for more targeted and effective business strategies.
• Improved product development: Companies can analyze customer feedback to find the most effective areas for product improvement and innovation.
• Increased customer retention: Understanding and addressing customer concerns through VoC analytics can significantly improve customer satisfaction and loyalty.
• Competitive advantage: Companies that excel at understanding and responding to customer voices are better positioned to outperform competitors in rapidly evolving markets.

However, a refined VoC analytics strategy doesn’t happen by chance. If you want to improve the way you collect and analyze your VoC data, you’ll need to create an analysis strategy that will set you on the path to success.  

6 Steps to Refining Your VoC Analytics Strategy

1. Consider the Business Questions You Want Answered

Before diving into the finer points of data collection and analysis, it’s crucial to start with a clear understanding of what you’re trying to achieve. What specific business questions do you need your VoC program to answer?

Begin by gathering key stakeholders from across your organization. This might include representatives from marketing, product development, customer service, and executive leadership. Together, brainstorm the most pressing questions about your customers that, if answered, could improve your offerings and business.

Some examples might include:

• What features do our customers value most in our products and services?
• Where are the major pain points in our customer journey?
• How does customer satisfaction vary across different segments of our market?
• What are the primary reasons customers choose our competitors over us?

Remember, the goal isn’t to collect data for data’s sake. Each piece of information you gather should help you answer these questions. By starting with this step, you’ll ensure that your VoC program is focused and aligned with your business strategy from the outset.

Plus, your business goals will change with time. Try to revisit this step once a year and reevaluate where your goals are. This way, you can ensure that the rest of your VoC analytics strategy is always geared toward specific, grounded goals that mean the most to your business. 

2. Improve the Quality of Your Data

The best analytics can’t save you if your data isn’t any good. Before improving how you analyze your data, you should make sure you’re doing everything possible to get your VoC program the high-quality data it needs. 

But what does high quality mean?

In this case, you’ll want to ensure your data is:

• Coming from multiple sources: You want data to come from multiple channels to eliminate the chance of any kind of bias entering the data. Consider using a mix of surveys, social media listening, online reviews from your business listings, and more.
• Recent: The newer the data, the better, so you can make decisions based on insights that are still relevant today. 
• Accurate: If your data is going through multiple hands and tools before getting to analysis, there may be a chance that multiple errors are happening before you even get to analysis. 
• Honest: If you’re offering rewards for positive reviews, then you might not be getting the honest feedback you need to actually improve.
• Representative of multiple demographics: Ensure the data you collect is coming from across your target markets, including a diverse range of ages, genders, locations, etc.
• Including qualitative data: 1-10 scales may be easy to parse, but they don’t tell you much about why people feel the way they do.

With this in mind, here are some ways to improve the quality of your VoC data:

• Pull data from at least five different sources.
• Speed up your VoC program with the right tools so you can go from data collection to insights in as little time as possible (aim for less than a month).
• Remove chances for human error in data recording where possible.
• Get rid of any offers that might be leading people to give dishonest reviews.
• Audit data collection for diversity.

If you’re looking for just one way to dramatically improve the quality of your data, try adding more qualitative data to your VoC program. Currently, too many companies rely solely on quantitative data from simple NPS or CSAT surveys. Although these metrics have their place, they often lack the most important information — the “why” behind the score.

If NPS scores are trending down, what does that tell you if you don’t know why? Mixing open-ended question data with your qualitative data allows you to peer under the hood and better understand why customers are giving your businesses the reviews they do.  

3. Write Better Surveys

Surveys are often the backbone of VoC programs, but poorly designed surveys can lead to misleading or unhelpful data. Here’s how you can refine your survey design for the best possible VoC outcomes:

• Keep it concise: Long surveys lead to survey fatigue, resulting in abandoned responses or rushed, inaccurate answers. Aim for surveys that can be completed in 5-10 minutes at most. Prioritize questions that directly relate to your key business questions identified in Step 1.
• Use clear, unambiguous language: Avoid jargon, double-barreled questions, or leading language. Each question should be straightforward and easy to understand. For example, instead of asking, “How satisfied are you with our product’s user-friendly interface and robust feature set?” split this into two separate, clear questions.
• Mix question types: Incorporate a blend of closed-ended (multiple choice, rating scales) and open-ended questions. While closed-ended questions provide easily quantifiable data, open-ended questions allow customers to express their thoughts freely, often providing unexpected insights.
• Implement skip logic: Use conditional branching to create a personalized survey experience. This ensures respondents only see questions relevant to their previous answers, reducing frustration and improving completion rates.
• Consider timing and context: Send surveys throughout the customer journey. For instance, a product satisfaction survey can be sent a few weeks after purchase, or a customer service satisfaction survey can be sent immediately after a customer call.
• Align questions with business goals: The end goal of these surveys is to collect the data you need to answer your company’s most pressing questions. Audit your existing surveys to ensure they deliver the data you need to answer them. 

4. Pick the Right Kind of Analysis for the Job

To get the best possible VoC analysis, you’ll need to start with the right kinds of analysis methods that are best suited to your goals. Different analysis methods can provide unique insights, helping you extract maximum value from your data. 

Here are some key types of analysis you could be using:

Descriptive Analysis

Descriptive analysis is the most basic form of analysis. It involves summarizing and describing your data to help you answer the question, “What happened?”

How it helps: Provides a clear overview of customer feedback, identifying trends and patterns in satisfaction levels, common complaints, or praised features.

Example: Calculating that the average customer satisfaction score for your new smartphone model is 7.2 out of 10 for the past quarter, compared to 8.1 for the previous model.

Diagnostic Analysis

Diagnostic analysis digs deeper than descriptive analysis, answering, “Why did it happen?”

How it helps: Uncovers the root causes of customer satisfaction or dissatisfaction.

Example: Investigating why customer satisfaction scores dropped at one of your store locations. This might involve:

• Breaking down satisfaction scores by location
• Analyzing open-ended feedback for common complaints
• Comparing scores across different locations and customer segments
• Examining the correlation between satisfaction scores and specific changes made in that location

Predictive Analysis

Predictive analysis answers, “What is likely to happen?”

How it helps: Forecasts future customer behavior based on historical data, allowing you to anticipate and prepare for upcoming trends or issues.

Example: Using historical data on customer behavior, product usage, and satisfaction scores to forecast that 15% of customers who rated your new product below 6/10 are likely to switch to a competitor’s product within the next six months.

Prescriptive Analysis

Prescriptive analysis answers, “What should we do about it?”

How it helps: Provides actionable recommendations based on data insights, guiding decision-making for improving customer experience.

Example: Based on the results of your diagnostic and predictive analyses, you could be offered the following recommendations: 1) Releasing a software update addressing top customer concerns, and 2) Offering a freebie to customers who rated the product below 6/10.

Sentiment Analysis

Sentiment analysis determines the emotional tone behind words, helping you understand the attitude of your customers.

How it helps: Gauges overall customer mood, identifying areas of intense positive or negative sentiment that might require immediate attention.

Example: Analyzing social media posts and product reviews to determine that while overall sentiment toward your customer service is mixed (60% positive, 40% negative), sentiment regarding your new features is overwhelmingly positive (85% positive, 15% negative).

Text Mining and Natural Language Processing (NLP)

These techniques analyze unstructured text data from open-ended survey responses, social media posts, or customer reviews.

How it helps: Shows you the “why” behind customer sentiments that may not be captured in more quantitative survey questions.

Example: Analyzing online reviews and identifying that “waiting time” is mentioned by 30% of patients who were unhappy with your clinic.

Correlation Analysis

This method identifies relationships between different variables in your data.

How it helps: Uncovers connections between various aspects of customer experience, potentially revealing unexpected factors influencing customer satisfaction.

Example: Examining the relationship between the frequency of software updates and customer satisfaction scores, revealing a strong positive correlation.

Trend Analysis

Trend analysis involves examining data over time to identify patterns or changes.

How it helps: Tracks the evolution of customer sentiment, allowing you to assess the impact of changes you’ve implemented and identify emerging issues.

Example: Tracking changes in Net Promoter Score (NPS) for your product over the past three years.

Cohort Analysis

Cohort analysis groups customers based on shared characteristics or experiences.

How it helps: Reveals how different customer segments perceive your product or service, enabling more targeted improvements and marketing strategies.

Example: Comparing satisfaction levels between customers of different ages who visited your restaurants. 

Journey Mapping Analysis

Journey mapping analysis examines customer feedback at different touchpoints of their journey with your brand.

How it helps: Identifies pain points and moments of delight throughout the customer experience, allowing for targeted improvements at specific stages.

Example: Analyzing feedback at each stage of the car purchasing process, from reviewing web analytics when they’re exploring different models on your website to feedback forms after a car is dropped off.

By understanding these different types of analysis, you can choose the methods that best align with your business questions and goals. Remember, you don’t need to use all of these methods — select the ones that provide the most valuable insights for your specific needs. 

5. Choose Analysis Methods That Make Sense for You

As you’ve just learned, there are a ton of different kinds of analysis methods out there. Doing all of them likely doesn’t make a lot of sense, so how can you make sure you’re picking the right ones? 

Here are some things to consider to make sure you’re using the correct analysis methods for your needs and situation: 

• Align methods with your business questions: Revisit the questions you identified in Step 1. Different questions may require different analytical approaches. For instance, understanding overall customer satisfaction might involve quantitative analysis of survey scores, while identifying specific pain points could require qualitative analysis of open-ended responses.
• Combine quantitative and qualitative analysis: Don’t rely solely on numbers. While quantitative data provides measurable trends, qualitative data often reveals the ‘why’ behind those trends.
• Leverage advanced analytics when appropriate: Consider using techniques like sentiment analysis to gauge emotional tone in customer feedback or predictive analytics to forecast future customer behavior based on historical data. However, only choose these if they truly add value to your decision-making process.
• Segment your data: Always try to analyze feedback across different customer groups. You might find that satisfaction varies significantly between new and long-term customers or across different demographic segments. This granular view can lead to smarter decisions and more targeted improvements.
• Consider real-time analysis: Implement real-time or near-real-time analysis for critical metrics to allow for rapid response to emerging issues or opportunities.
• Get enough data: Always ensure that your sample size is large enough to be representative. Avoid making sweeping changes based on feedback from just a handful of customers.
• Automate where possible: Unless you have an army of statisticians on hand, you’ll likely be better off letting machines do the analysis for you. Using AI and machine learning tools will speed up your insight generation and leave less room for error.

6. Audit the VoC Tools You’re Using

The goal of any successful VoC analytics strategy is to get your team accurate insights that answer your most pressing business questions as quickly as possible. 

Not long ago, “as quickly as possible” was measured in months. It took a long time to create surveys, distribute them, analyze the data, and formulate insights.

Luckily, today, there is a better way.

With a tool like Chatmeter, your team can automate the process of collecting and analyzing data so they always have access to the answers they’re looking for.

Chatmeter does this by making it easier than ever to learn what your customers are thinking. First, we collect data from multiple sources. This can include:

• Social media chatter about your brand
• Reviews from top online directories
• Surveys uploaded into Chatmeter or created on our system 
• Data from in-store feedback or customer service portals

We can then run this data through our advanced AI analytical models to extract the customer insights you’re looking for. You can get these in a traditional dashboard with information like customer sentiment and review numbers, all segmented by location, demographic, and more. 

However, you can also just ask the questions you want answered. Are you curious which features are at the top of your customers’ most-wanted list? Ask our Signals AI feature — it’ll look through all of your customer data and give you the answer instantly.

This means that by investing in a new VoC tool, your team can quickly and easily improve their VoC analytics.

Learn What Your Customers are Really Thinking Right Now

For most businesses, learning what your customers are thinking about your product or business nine months from now simply isn’t good enough. 

In that time, people might move on, look at a competitor, or leave the market entirely. That’s why you need a VoC program that is ready to give you the insights you need right away.

If you’re interested in learning more about how Chatmeter can address the most pressing business questions your company has with its advanced VoC tools, you can schedule a demo today.

Or, if you’re interested in seeing what our program looks like, you can also check out our interactive demo below.

Take a tour of Chatmeter