In the healthcare industry, collecting timely, accurate feedback is critical — whether you're improving patient care, assessing health risks, or building better services. Survey software empowers healthcare organizations to connect with patients, caregivers, and members across multiple channels, ensuring no voice goes unheard.

Here are three powerful ways healthcare organizations are using survey software today:

1. Healthcare Call Centers: Improving Patient and Member Outreach

For healthcare providers, patient engagement doesn’t stop at the clinic or hospital doors.
Ongoing outreach is essential to improve satisfaction, strengthen relationships, and drive better health outcomes.

Phone surveys remain one of the most effective ways to reach patients and members, especially when combined with the right technology.
Robust CATI (Computer-Assisted Telephone Interviewing) software, paired with integrated IVR systems and dialers, helps healthcare call centers boost productivity, allowing interviewers to make more calls, reach more respondents, and collect more data — faster.

Common healthcare call center surveys include:

• New member or patient welcome calls
  
  
• Health risk assessments (HRA)
  
  
• Discharge follow-up calls
  
  
• Appointment reminders
  
  
• Custom outreach campaigns
  
  

Many healthcare organizations trust Voxco to power their call centers, making it easier to manage high call volumes while delivering a seamless experience for both interviewers and patients.

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2. Face-to-Face Evaluations for Senior and Patient Care

As populations age, supporting independence and quality of life becomes more important — and more challenging.
Healthcare researchers, home care agencies, and government programs often rely on face-to-face surveys to assess the needs of seniors and patients in their homes.

Using tablet-enabled survey software, healthcare professionals can conduct in-home evaluations even without internet access.
This offline survey capability ensures that data is collected securely and accurately, no matter the location.

Typical face-to-face evaluations include:

• Needs assessments for home care services
  
  
• Health evaluations for elderly or disabled individuals
  
  
• In-person caregiver interviews
  
  

By conducting Computer-Assisted Personal Interviews (CAPI), healthcare workers can create personalized care plans based on real data — while also gathering broader insights to enhance service delivery.

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3. Patient Research Surveys: Understanding Experiences and Needs

Patient-centered research is critical to improving healthcare services and understanding how they are perceived.
Survey software enables researchers to gather honest, actionable feedback through a multichannel approach — combining phone, online, and offline surveys to reach diverse patient populations.

Healthcare researchers use survey platforms like Voxco to:

• Measure patient satisfaction
  
  
• Understand patient needs and sentiments
  
  
• Identify gaps in care delivery
  
  
• Support academic and clinical studies
  
  

Because healthcare research often involves sensitive data, many organizations choose on-premise hosting options for added security and control — a feature easily supported by Voxco’s flexible survey platform.

By bridging the gap between patients and providers, healthcare research surveys play a key role in building a more responsive, patient-first healthcare system.

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Conclusion

In healthcare, every piece of feedback counts. Whether through phone outreach, in-person assessments, or patient research surveys, survey software helps healthcare organizations collect the insights they need to deliver better care, engage patients, and drive innovation.

Ready to streamline your healthcare surveys across phone, online, and face-to-face channels? Book a demo to see how Voxco can help.

One Click to Actionable Insights

Unlocking the true potential of open-end comments has never been easier. CX Inspector with Theme Extractor uncovers descriptive, actionable insights from open ends instantly, a significant leap forward in text analysis technology.

• Extracts descriptive themes with one click. Upload your data set and immediately charts and tables appear which highlight theme-based insights and visualize the results.
• Analyze any size data set in minutes. From several hundred to several thousand responses, CX Inspector can analyze the data set quickly.

• Dive deep into the data. Group key themes together, apply filters and sentiment, examine co-occurrences and generate trend reports and crosstabs with t-tests to understand both broad trends and granular details. Also, click through a theme to see the individual responses.
• Over 50 languages. CX Inspector analyzes responses in multiple languages and shows results in your choice of over 50 languages.
• X-Score measures customer satisfaction. X-Score, Ascribe’s proprietary measure, identifies the key drivers that will increase customer satisfaction and loyalty directly from open-end responses.
• Save and restore projects. Don’t let your work go to waste! Save your projects to restore for future use.
• Easily export data tables and charts. Seamlessly integrate results into reports and presentations.

Unleash the power of CX Inspector with Theme Extractor today and revolutionize how you uncover insights from open-ends to enable data-based decisions that drive customer satisfaction, loyalty and business success. Connect with us today at CX Inspector to learn more and request a free demo with your data set.

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Do you benefit from using AI for verbatim analysis?

Most of the valuable insights come from qualitative or textual data. The feedback customers share in their own words fuels the eureka-moment to innovate your business strategy.However, the challenge is that text feedback is difficult to analyze. Most data analysis software is engineered to analyze numeric or quantitative data.So, how can you make sense of verbatim responses?Artificial Intelligence such as Sentiment Analytics can help you solve the problem.The feedback, reviews, or comments customers leave for your brand have some underlying emotions. Using AI for verbatim analysis, you can extract keywords that resonate with customer experience, identify emotions and cognitive responses across all touchpoints and thus create a Customer-Focused framework.

• After a positive experience, 85% of customers purchase more from a brand.
• A negative experience results in 70% of customers purchasing less.
• After a positive experience, shoppers are likely to spend 140% more with your brand.

Having insight into customers’ emotions towards your brand can be a game-changer and help you make better CX decisions to boost loyalty and drive revenue.

How does Sentiment Analytics work?

• Sentiment Analytics reads through customer feedback and detects emotions based on the language used. The AI uses NLP and machine learning algorithms to split customers’ emotions into positive, neutral, or negative.
  • Combined with CSAT, NPS^®, or CES surveys, AI can capture verbatims and emotions to obtain profound insights shaping your short-term and long-term strategies to retain customers.
  • It can analyze a large volume of feedback in real-time to recognize which customer needs more attention and who is satisfied.
  • You can utilize AI to predict whether customers are satisfied or have complaints.
  Regardless of your target customers, you can win over your customers' hearts using sentiment analytics.[Related: Read about How Sentiment Analytics can be conducted? in detail]Benefits of using AI for feedback analysis:Customers remember how you make them feel:To offer customers positive memories, you need to determine the root cause of their concerns.Use AI to understand what is causing the issue. Sentiment Analytics can help you track recurring themes and keywords from customer feedback that can highlight what troubles your customers the most.Segment these customers and follow up with them to further understand their concerns. Generate insights to take decisive actions, offer outstanding experiences, and mend your customer relationship.Discover what’s missing:Customers can tell you themselves what they want and where you fail to deliver. Often, the touchpoints brands expect to impact CX are not the ones customers truly care about.AI can show what customers want from your business, where they need help, and what you miss out on. Using sentiment analytics, you can detect the pain points and use them as an opportunity for improvement.Be it an aspect of product or customer journey, uncover the urgent issues and factors critical to customers to prioritize them.

Happy! Sad! Annoyed! Content!

Discover customer emotions around your brand.

Book a demo

Capture customers’ perception of Brand:

Brand reputation is critical in acquiring new customers - 95% of shoppers read customer reviews before buying.With sentiment analytics, you can capture how customers feel in real-time. Tracking positive and negative mentions of your brand, you can identify what is influencing their attitude towards your brand.Capturing customer sentiment in real-time is the key to improving brand reputation.Customers’ emotions are likely to dissipate over time, and they may move to other brands. Capture customer reviews and direct them to the relevant team so they can proactively resolve the issue.Brands that reply at least 25% of the time to customer reviews are likely to earn 35% more revenue.[Related read: Significance of sentiment analysis]

Increase ROI with innovative strategies:

You can gather intelligent insights by analyzing customer feedback to innovate marketing strategies that appeal to your target audience.Brands can design personalized campaigns by segmenting their customer base using NPS^® & qualitative feedback. It can help you recognize whether the “satisfied” customers are delighted. Often the customers who give high scores for satisfaction are at the risk of deflecting. The insights you gather by analyzing customer feedback can alert you to the risk of loss in such scenarios.You can design personalized campaigns and services that deliver better value to the customers.Escorts Group fuels digital transformation & drives lead conversion with Voxco Intelligence.Read the full story.

Eliminate what harms CX:

The ultimate benefit of using AI for verbatim analysis is to prevent a crisis by prioritizing actions that deliver exceptional customer experience.You can analyze customer emotions to discover the factors causing dissatisfaction among customers. Sentiment analysis of survey responses can help you understand what is frustrating the customers or what delights them.Be it a certain marketing campaign or payment process; you can take decisive action and intervene at the right time.

Wrapping Up;

Every customer response or feedback is a goldmine to accelerate business growth. Brands can now leverage AI to monitor every aspect of customer experience in real-time and provide effective resolution promptly.Happy – Neutral – Sad, always stay in tune with your customers' feelings about your brand.

Every comment, review, and post carries more than just words — it carries a feeling.

Sentiment analysis taps into this emotional layer, using natural language processing (NLP) and machine learning to detect and interpret what people are really feeling. Whether it’s measuring public opinion, monitoring brand health, or finding insights hidden in customer feedback, sentiment analysis has become an essential tool for organizations today.

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What is Sentiment Analysis?

Sentiment analysis is a method of analyzing text to determine whether the emotional tone behind it is positive, negative, or neutral.

It’s commonly applied to:

• Customer reviews
  
  
• Social media posts
  
  
• Survey responses
  
  
• Support tickets
  
  
• News articles
  
  
• Forums and blogs
  
  

By automatically evaluating large volumes of open-ended feedback, sentiment analysis provides businesses with a scalable way to understand public perception.

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Why Is Sentiment Analysis Important?

Here are a few key reasons why sentiment analysis matters:

1. Understand Customer Opinions at Scale

Manually reading through thousands of customer comments isn't realistic. Sentiment analysis automates this process, helping you efficiently analyze large datasets and uncover trends in customer opinions.

2. Improve Customer Experience

By identifying common pain points, frustrations, and areas of satisfaction, companies can proactively address issues and enhance the overall customer experience.

3. Strengthen Brand Reputation Management

Sentiment analysis enables real-time monitoring of brand mentions across channels. You can quickly spot negative sentiment and intervene early to protect your brand’s image.

4. Support Better Decision-Making

Understanding how customers truly feel helps guide strategic decisions — from product improvements to marketing campaigns — based on real-world data, not assumptions.

5. Enhance Market Research Insights

Traditional survey analysis often focuses on quantitative results. Sentiment analysis adds a qualitative layer, offering a deeper look into the emotions and motivations behind customer behavior.

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How Sentiment Analysis Works

Typically, sentiment analysis uses natural language processing (NLP) algorithms to process text data. Here’s how it works at a basic level:

1. Data Collection: Gathering text from sources like surveys, social media, or customer reviews.
   
   
2. Text Preprocessing: Cleaning the data by removing noise (punctuation, stop words, etc.).
   
   
3. Sentiment Detection: Classifying the text as positive, negative, or neutral.
   
   
4. Analysis and Reporting: Aggregating the sentiment scores to generate insights and trends.
   
   

Modern sentiment analysis tools, like those using AI and machine learning, can also detect nuances such as sarcasm, context, and mixed emotions — providing even more accurate insights.

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Applications of Sentiment Analysis

Sentiment analysis can be applied across a wide range of industries, including:

• Retail: Measure customer satisfaction and refine product offerings.
  
  
• Finance: Monitor market sentiment and predict trends.
  
  
• Healthcare: Analyze patient feedback for service improvements.
  
  
• Hospitality: Understand guest experiences and improve services.
  
  
• Politics: Gauge public opinion during campaigns.

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Conclusion

In a world where opinions are shared instantly across digital platforms, sentiment analysis offers organizations a powerful way to tap into the emotions behind customer feedback.

By investing in sentiment analysis, companies can make smarter decisions, improve customer experiences, and stay ahead in an increasingly competitive landscape. Ready to dig deeper into customer sentiment? Book a demo to find out how Ascribe by Voxco can help.

Month-over-Month (MoM) is the lowest unit of measurement used to objectively reflect the pace of growth in a firm. This statistic scales up to Quarter on Quarter and Year on Year growth tracking to give us an understanding of rates of increase across varying time intervals. It's most typically utilized by early-stage organizations, such as San Francisco startup entrepreneurs, for predictions.

The basic MoM formula is applicable to anything from users to customers to revenue. Having a hold on our growth statistics is a task that should be implemented across all departments in our company, not simply the product and finance teams.

It's worth noting that MoM data are rather detailed, and they should be utilized to ladder up to Quarterly and Annual growth metrics for a more high-level view.

IMPORTANCE OF MONTH OVER MONTH GROWTH RATE

• Illuminates the ROI achieved by our sales and marketing team.
• Calculates the entire value our organization receives from each client in relation to the cost invested to acquire that new customer.
• Assists us in finding the middle ground by keeping this ratio within a reasonable range, neither too high nor too low.

Start Creating Descriptive Surveys Now

WHAT METRICS COMPLIMENT MONTH OVER MONTH GROWTH RATE?

Tech firms, particularly those with recurring income, such as SaaS, have their own set of KPIs that supplement MoM numbers. For good reason, the following are generally the ones who receive the most attention:

• ARR and MRR (Monthly and Annually Recurring Revenue)

How much money will we generate from our present customer base in the coming year? This might be computed monthly, quarterly, or annually.

• Churn

How many clients are canceling their subscriptions?

• LTV (LifeTime Value)

How much money will a single client produce before canceling their subscription?

• CAC is an abbreviation for the acronym (Customer Acquisition Cost)

How much does it cost to bring on a single new customer?

• TROI (Time to Return on Investment)

How long will it take for us to see a return on our marketing investment?

Outside of the SaaS model, some overlapping metrics rely on or supplement MoM growth calculations for any type of company seeking venture capital or angel investment.

These are some examples:

• MRR (Monthly / Annually Recurring Revenue) laddering up to ARR (Annually Recurring Revenue)
• churn (Rate of Customer Loss)
• CAC (Customer Acquisition Cost) month-over-month increase may also be used to assess team expansion and staffing, for example, by businesses attempting to determine whether their staffing matches their ticket or customer growth. Companies frequently use staff augmentation or other scaled recruiting strategies in response to abrupt changes in the month-over-month growth rate.

Again, following these indicators will help us to iterate on our exponential business solutions and correct issue areas that are impacting growth and make more reliable projections even if your company is not reliant on or seeking outside money.

HOW TO CALCULATE MONTH OVER MONTH GROWTH RATE?

Fortunately, there are plug-and-play formulas for MoM computations, so we don't have to continually call our management accountant for assistance.

The formula version for obtaining a percentage output for month-specific data is as follows:

(Month 2 - Month 1) / Month 1 * 100 = % growth (or decrease).

If we choose a more simplistic approach, we may do it like this:

x = (y - z) / (y - z) * 100

All we need to do is enter our monthly data into the appropriate variable in the calculation, and we're done.

HOW TO CALCULATE MONTH OVER MONTH GROWTH RATES FOR MULTIPLE MONTHS?

When working with numerous months of data, we'll need to "flatten" it to get an overall Month-over-Month growth rate.

CMGR, or compounded monthly growth rate, is the most commonly used statistic in this context. Basically, we'll need to compare our starting and finishing month data and figure out what percentage monthly rise would cause the starting figure to expand to the ending figure.

It's worth noting that this can be deceptive because we're disregarding fluctuation in monthly growth rates and "flattening" it to a single compounded figure each month.

This is beneficial if we are seeing compounding growth (like a retirement account, which increases faster when we have more money).

It's useless if we're just witnessing linear progress (like our income, which stays the same amount each month regardless of how much we have stocked up). If our growth curve is linear, this is a misleading figure and should not be used for projecting future growth.

COMPOUND MONTHLY GROWTH RATE FORMULA

CMGR reflects our growth rate over a certain time period, assuming that growth occurs at a steady pace every month throughout that time period. Assume that your active users increased in the following manner:

To determine your CMGR, enter the following numbers into the following formula:

For example, here:

Even though it changes from month to month, CMGR averages 20% for the whole time. For example, the MoM growth rate from January to February is just 10%, but it increases to 36% from February to March. With CMGR, we assume that from January to June, we will increase at a steady monthly pace. In our case, this entails the following:

Let's go on to the next phase. We calculated the CMGR for a historical time period above. Assume we want to build a five-year business plan and forecast how our company will appear in five years. We'll have 500,000 active users by December 2022.

TRACKING SHORT TERM GROWTH CAN SET UP FOR LONG TERM SUCCESS

When used to properly estimate present performance as well as measure and forecast success, month-over-month growth is the gift that keeps on giving. It conveys the sense that we know what we're doing and are devoted to our company's long-term success.

• Earn the trust of investors

Most likely, this isn't our investors' first rodeo. Investors and startups often have long-term relationships, so be honest and accountable, especially in your pitch deck. This is true for growth data: If we misrepresent your MoM, even if it is unintentional, it might ruin our image in the eyes of some of our most essential stakeholders.

Regardless of the nature of our growth statistics, how we show it is a chance to gain the respect of investors. Even if our growth data isn't rocketing at a 35 percent MoM pace, we may amaze investors by delving into why that is and giving practical insights to remedy the issue.

• Keep our long-term goals in mind

Month-over-month growth gives only a snapshot of what's going on with growth by comparing what happened this month to last. However, it does not reveal the entire tale. Remember to zoom out every now and then to connect monthly patterns to our company's KPIs and long-term strategy. This can assist us in being goal-oriented by determining whether we're on track to reach broader goals such as YoY benchmarks, as well as quarterly or yearly KPIs.

• Don't presume success straight away

While flat or declining growth rates may appear discouraging, keep in mind that there is value in all of our data—even if it isn't the data we want to see. Even for unicorns, exponential growth does not occur overnight, and particularly not on its own. To seek success aggressively, adopt the principle of being brutally honest with ourself, and pay attention to what our development rates are telling us. That will almost certainly assist us in identifying fresh areas to improve.

• Quality output starts with quality input

“Create a better forecast by focusing on inputs, not outputs.”

—Andrew Chen, General Partner at Andreessen Horowitz

The quality of our output is only as excellent as the quality of our intake. Rather of concentrating all of our efforts on the figures we want to see at the end, make a commitment to getting the initial procedures correct.

When we're continually sorting through low-quality data, it becomes far more difficult to effectively evaluate critical indicators like MoM. Before we delve too far into the weeds of doing any analysis, we'll need to first build a solid data system—our minimal viable instrumentation (MVI). This will assist us in determining the exact data procedures we should employ in order to achieve our business and analytics objectives.

Begin by defining two terms:

• Important phrases like "daily active users"
• Our precise business objectives

COMMON MISTAKES MADE WITH MONTH OVER MONTH GROWTH RATE

1. Absolute tiny numbers represented by growth

It is quite simple to demonstrate impressive growth stats with tiny numbers. Any change will be significantly more dramatic if it is based on a tiny absolute foundation.

It may be tempting to leverage MoM growth in the early stages of our firm, but try to avoid doing so. It is preferable to be realistic and provide absolute numbers. Instead of displaying a 20% increase on 100 users, show this as 20 new users in a month.

Once our company reaches a certain size, it will be difficult to sustain those first artificially high growth rates, and who wants to indicate that their growth rates are steadily decreasing?

If we have received or are attempting to get investment from venture capital or angel investors, it is a better strategy to only introduce growth figures once we have scaled past a certain size.

Until then stick to absolute figures so that we don't set ourself up for a downward growth graph and some unnecessary explaining.

2. Inconsistent growth rates

So, we know that compound growth rates flatten our monthly increase over a certain time period into a fixed percentage. If our monthly growth rates fluctuate a lot, we might want to express our compound growth rate as a range to be more accurate when reporting to investors or our board.

3. Using compound growth rates to describe declining linear growth rates

The compound growth rate for the months in the table below is 12%. That is a deceptive statement because the growth rate is lowering on a monthly basis. It decreased from 20% to 17%, then to 14% and finally to 12%. Don't make the mistake of inflating your growth stats, even if it's unintentional. It constantly makes an appearance in the end.

Adding 1,000 per month is a linear growth, meaning that the percentage increase Month over Month is decreasing in each period.

If we notice that our growth is decreasing in a linear fashion rather than exponentially, use this information to investigate why this is happening and to devise a better growth model.

4. Vanity Metrics

Don't bother with vanity metrics that aren't important to our investors, accounting staff, or board of directors. Depending on our company strategy, we will have many key performance indicators (KPIs) that are directly tied to revenue and growth. Maintain our focus on the measures that are important.

Growth numbers for measures like traffic or bounce rates may be relevant at the top of the funnel or at the campaign level, but they have no direct influence on our business's performance. The following are some metric blunders to avoid:

• To demonstrate a larger number, we are focusing on page views rather than active users
• Reporting on newsletter subscribers rather than subscribers
• Showing growth of total users instead of active users

5. CMGR is used to calculate the month-over-month growth rate

Unfortunately, this one does not work backwards. So, we know that we can include MoM data into our CMGR formula, which shows the growth that has occurred over a specific time period and flattens the oscillations between months.

However, if we try to go backward to deduce the growth difference of a certain month or months based on our CMGR, chances are we'll get a huge discrepancy between what we get and what the original individual MoM was.See why 450+ clients trust Voxco!

What is the customer first strategy?

When we look at the word “customer first”, we instantly think about an organization where everyone focuses on serving the customer in a better way and keeping customer experience at the center of everything. Although it sounds cool and considerate in theory, when it comes to actually implementing the customer first approach, you’ll realize it is much more than that.Customer first strategy is adopted by the customer-centric organizations to make their business decisions with not products as their center but the customers. Meaning, these organizations will develop their products and services with respect to customer interests, aiming to satisfy their needs and wants.Generally, with customer-centric organizations, customer first is basically their business purpose. These organizations build their business products and services with a purpose to serve the customer with their demands. Hence, the customer first approach is nothing but a business purpose for them that they work for every minute. The way customers use their products, give their reviews and their relationships with the organization is what drives the customer first approach in an organization.

Customer first approach for business success

Customer first organizations have one goal and that is to deliver the best customer experience, be it with the products/services or customer service and support. So, it is safe to say that the biggest benefit customer first approach will have on your business is it will make your customers build loyal relationships with your brand. Apart from loyalty, customer first strategy also turns these loyal customers to your business promoters. Customers will feel free to refer your brand to their friends and family which is called “word-of-mouth” promotion.

Example of customer first approach is a Japanese retailer Uniqlo, who have reported their increased revenue profit in 2021. Uniqlo’s CEO Tadashi Yanai gives half the credit to customer first mindset saying, “Meet customer needs, and create new customers.”Apart from this, the customer first approach changes the way customers look at your brand. Good customer experience helps an organization gain loyal customers who decide to stay with the brand regardless of the factors in products that might need them to go out of their way. Furthermore, customers also help the organization in return by referring the products and services to their circle. This gives an organization an upper hand in the market when it has a strong loyal customer base.

Tips to become a successful customer first organization

• Customer perspective 

The best way to ace the customer first approach is to understand what they think of you as a brand. Example, a famous sports brand Nike identifies itself as a brand that motivates the athletes to do better with their sports accessories. Hence, they can focus more on the customer perspective of them and help them understand the product purpose.

• Know your customers 

Knowing your customer is not limited to knowing what they buy. It goes beyond that and focuses also on what their buying behavior is, their purchase history, their issues, goals and emotions too. To be able to develop valuable products and services that serve the customer needs in the right way needs co-creating. Both the brand and customer have to play an equal role in developing the best product.

• Proactive customer experiences 

Customer experience is adversely affected by the quality of customer service you provide to your customers. Delivering a proactive customer experience to your customers will help you gain a competitive advantage in the market as customers tend to stick to a brand for a longer period. Whereas, customers tend to leave the company just after one bad experience.

• Customer first as organization goal

It is easy to say that you are a customer first organization, but it is easier said than done. For your organization to be customer centric, you need to regulate the customer first approach in all the functional teams across the organization. The employees who directly interact with the customers, and even the ones who don’t, need to adhere to the customer first approach. Organizations make customer first approach as their business goal.

Improve Customer Loyalty with Voxco

We have developed a guide which gives you an all around view of customer loyaltyDownload NowNet Promoter^®, NPS^®, NPS Prism^®, and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., Satmetrix Systems, Inc., and Fred Reichheld. Net Promoter Score℠ and Net Promoter System℠ are service marks of Bain & Company, Inc., Satmetrix Systems, Inc., and Fred Reichheld.

Survey coding is an invaluable tool for researchers aiming to analyze the open-ended responses in surveys. This method involves categorizing and labeling textual data from responses to questions that allow participants to express themselves freely, beyond the constraints of predefined choices.

In this post, we'll explore what survey coding is, why it's essential, and how it can transform unstructured open-ended responses into actionable, quantitative data. We'll walk you through the process of creating a comprehensive codebook, discuss the best practices for ensuring consistency and accuracy, and highlight some of the common challenges you might encounter along the way.

What Is Survey Coding?

Survey coding of open-ended responses involves organizing and categorizing textual data gathered from survey questions to make it easier to analyze. Here's a detailed breakdown of the process:

• Collection of Responses: In surveys, alongside multiple-choice questions, there are often open-ended questions where respondents can provide their answers in their own words.
• Initial Review: The responses are first reviewed to understand the range of answers provided and the different ways respondents interpret the question.
• Development of Codebook: A codebook is created which defines categories or themes that the responses can be sorted into. This involves identifying common themes, patterns, or recurring phrases within the responses.
• Coding the Responses: Each response is read and assigned one or more codes based on its content. This coding process can be done manually by researchers or with the aid of text analysis software which can help to automate some parts of the process.
• Refinement of Codes: As coding progresses, some codes might be split, combined, or refined to better capture the nuances of the responses. This is an iterative process that may require going back to previously coded responses and reassigning them under the new scheme.
• Analysis: Once coding is completed, the coded data can be analyzed quantitatively (e.g., calculating the frequency of each code) or qualitatively (e.g., examining the context around certain codes to understand deeper meanings).
• Reporting: The results are then compiled into a report, providing insights such as common themes, unusual opinions, or general sentiment about the surveyed topics.

Survey coding is essential for effectively using open-ended responses, as it transforms qualitative text into quantifiable data, allowing for a more structured analysis that can complement the statistical findings from closed-ended questions.

Benefits Of Quality Survey Coding

Survey coding, especially of open-ended responses, offers several important benefits that enhance the value of survey data for research, decision-making, and strategy development. Here are some key advantages:

1. Rich Insights: Open-ended responses can provide depth and context that closed-ended questions might miss. Coding these responses helps in extracting these nuanced insights systematically, allowing for a more comprehensive understanding of participants' opinions and experiences.
2. Quantifiable Data from Qualitative Responses: By categorizing qualitative responses into predefined codes, researchers can quantify this data. This quantification makes it easier to perform statistical analysis, such as identifying trends or comparing subgroups within the data.
3. Identification of Themes and Patterns: Coding helps in identifying common themes and patterns that may not be immediately apparent. This can be especially useful in exploratory research where the range of possible responses is not well known beforehand.
4. Enhanced Data Management: Coded data are easier to manage, store, and retrieve. Researchers can quickly access and analyze large volumes of data without needing to sift through each individual response repeatedly.
5. Improved Reliability and Consistency: A well-defined coding scheme ensures that data is processed consistently, reducing the variability introduced by different researchers’ interpretations. This enhances the reliability of the data, making the findings more robust.
6. Facilitates Comparison and Tracking Over Time: Coded data can be compared across different groups or tracked over time more easily than raw textual data. This is particularly useful for longitudinal studies or when comparing responses across different demographics.
7. Supports Mixed-Methods Research: Coding allows for the integration of qualitative data into predominantly quantitative studies, supporting mixed-methods approaches that can provide both breadth and depth in research findings.
8. Feedback for Future Surveys: Insights derived from coded responses can inform the development of future surveys, such as by helping to refine questions, adjust response options, or identify new areas of interest that require exploration.

Overall, survey coding is a powerful tool that transforms text data into highly-precise structured, actionable information, providing a deeper understanding of the research subject and enhancing the impact of the findings.

When Do You Use Survey Coding?

Survey coding is used in several specific situations during research and data analysis, particularly when dealing with qualitative data from surveys. Here are some common scenarios where survey coding is especially useful:

1. Analyzing Open-Ended Survey Responses: Whenever surveys include open-ended questions where respondents can write their answers freely, coding is used to organize these textual responses into quantifiable categories. This allows for systematic analysis alongside the quantitative data from closed-ended questions.
2. Exploratory Research: In early stages of research, where the aim is to understand broad themes and sentiments about a topic, coding helps identify and categorize these themes from survey responses. This is useful for shaping further research or developing hypotheses.
3. Market Research: Companies often use survey coding to analyze customer feedback on products, services, or experiences. Coding helps identify common complaints, suggestions, or praises, guiding business improvements and product development.
4. Academic Studies: Researchers in fields like sociology, psychology, and health often use survey coding to analyze data collected through questionnaires. It helps them understand patterns, relationships, and influences among variables based on participants’ textual responses.
5. Customer Satisfaction and Feedback Analysis: To gauge customer satisfaction and gather actionable feedback, businesses code responses from satisfaction surveys. This can inform customer service policies, product improvements, and overall business strategies.
6. Policy and Public Opinion Research: In policy-making and public opinion surveys, coding is used to categorize responses to open-ended questions about laws, regulations, or political issues. This helps in understanding public sentiment and informing policy decisions.
7. Longitudinal Studies: In studies that track changes over time, coding allows researchers to consistently categorize responses across different time points. This is crucial for accurately measuring how opinions, behaviors, or experiences change.
8. Content Analysis: Coding is used in content analysis where the content of text data—such as responses to an open question about media usage or preferences—is categorized into defined codes to analyze trends and patterns.
9. Qualitative Data Integration: In research, where both quantitative and qualitative data are collected, coding qualitative responses allows for integration with quantitative data, providing a richer, more comprehensive analysis.

In all these scenarios, survey coding is an effective solution for transforming unstructured comments into structured data that can be analyzed statistically.

Survey Coding Best Practices

Adhering to best practices in survey coding ensures that the data derived from open-ended responses is reliable, consistent, and useful for analysis. Here are some key best practices to follow when coding survey responses:

1. Develop a Comprehensive Codebook: Start by creating a detailed codebook that clearly defines each code, including descriptions and examples. This serves as a guideline for coders to apply the codes consistently. It should also include rules on how to handle ambiguous or unclear responses.
2. Train Coders Thoroughly: Ensure that all coders are thoroughly trained on the codebook and understand the objectives of the coding process. Regular training sessions can help maintain consistency, especially as the codebook might evolve over the course of a project.
3. Ensure Inter-Coder Reliability: Use multiple coders for the same set of responses initially to check for inter-coder reliability, which is the level of agreement among different coders. This helps identify any ambiguities in the codebook and ensures that the coding is reliable and consistent.
4. Use Pilot Testing: Before full-scale coding, conduct a pilot test with a sample of responses. This helps in refining the codebook by identifying new themes or issues that weren’t initially apparent. Adjust the codebook based on the findings.
5. Iterative Process: Be prepared to revisit and revise the codes as you process the responses. As you dive deeper into the data, new themes might emerge or existing codes might need refinement.
6. Maintain Coding Consistency: Regularly review the coding work to ensure consistency over time, especially for large projects or long-term studies. This might involve periodic retraining sessions or recalibrations of the coding rules.
7. Automate When Appropriate: Consider using software tools for coding if the volume of data is large. Many tools offer features like text parsing, pattern recognition, and preliminary coding suggestions, which can increase efficiency. However, human oversight is crucial to handle nuances and context that the software might miss.
8. Document All Processes: Keep detailed records of all coding decisions, changes to the codebook, and any issues encountered during the coding process. This documentation is vital for the credibility and replicability of the research.
9. Analyze Coded Data Critically: When analyzing the coded data, be critical of the codes themselves and the potential for bias or error. Analysis should consider not just the frequency of codes but also their context and the interrelations between different themes.
10. Ensure Ethical Standards: Respect the confidentiality and anonymity of survey respondents, especially when handling sensitive information. Ensure that all data handling and coding practices comply with ethical guidelines and legal requirements.

By following these best practices, you can maximize the accuracy and utility of the coding process, thereby enhancing the quality of data derived from open-ended survey responses.

Differences in Using Survey Coding vs Text Analysis To Analyze Open-End Survey Responses

Survey coding and text analytics are both methods used to process and analyze text data, but they have different focuses and methodologies. Understanding their distinctions can help in choosing the right approach for a given research need.

Survey Coding

Survey coding primarily deals with categorizing and tagging open-ended responses collected from surveys. It involves interpreting responses based on a predefined set of categories or themes that researchers develop to capture the essence of the text data.

Methodology:

• Manual or Semi-Automated: Coding can be done manually by researchers or semi-automatically using software that assists in categorizing responses.
• Developing a Codebook: Researchers create a codebook that defines each category or code. This includes descriptions of what type of response fits each category.
• Application: Codes are applied to each response to summarize and categorize the data, making it easier to analyze statistically.

Survey Coding Use Cases

It is commonly used in market research, social science research, customer feedback analysis, and anywhere qualitative data needs to be quantitatively analyzed.

Text Analytics

Text analytics involves a broader set of techniques designed to extract information and insights from text data. It uses algorithms and natural language processing (NLP) techniques to uncover patterns and insights within large volumes of text.

Methodology:

• Automated Tools: Text analytics is typically performed using software and algorithms that can process large datasets more efficiently.
• Techniques: This includes sentiment analysis, keyword extraction, topic modeling, and more. These techniques automatically identify and quantify various elements within the text without needing a predefined codebook.
• Natural Language Processing (NLP): Text analytics heavily relies on NLP to understand the grammar, structure, and even the sentiment of the text.

Text Analytics Use Cases

Text analytics is used in a wide array of applications like business intelligence, market analysis, customer service improvements, and sentiment analysis across various types of text sources like social media, customer reviews, and news articles.

Key Differences Between Survey Coding and Text Analytics

• Scope: Survey coding is more specific in scope, focusing on categorizing survey responses into predefined themes. Text analytics is broader, applying various computational techniques to extract insights from text responses.
• Automation: Survey coding can be manual or semi-automated, while text analytics is highly automated, leveraging complex algorithms and machine learning.
• Purpose: Coding is primarily about simplifying and structuring text for analysis, often in academic or formal research contexts. Text analytics is about discovering patterns and insights in text data, used across many industries for various business and research purposes.

In essence, while both methods aim to derive meaningful information from text, they do so in different ways and are suited to different types of analysis and data volumes.

FAQs

What is survey coding?

Survey coding is the process of categorizing and labeling open-ended responses collected from surveys. This process involves defining a set of codes, which are thematic or categorical labels, and applying them to the responses to organize the data into meaningful groups. This makes it easier to analyze qualitative data quantitatively.

Why is coding important in survey research?

Coding is essential in survey research because it transforms raw, open end comments into structured, analyzable form. This allows researchers to perform statistical analysis, identify trends, and draw significant conclusions from the data. Coding also ensures that data interpretation is systematic and consistent, improving the reliability of the research findings.

What are the differences between manual and automated coding?

Manual coding involves researchers applying codes to survey responses by hand, which can be time-consuming but allows for nuanced understanding. Automated coding uses software to apply predefined codes to text data. While faster and more consistent, it may not handle nuances as effectively as a human coder. The choice between manual and automated coding depends on the project's scale, complexity, and available resources.

Contact the Survey Coding Experts at Ascribe

Survey coding is an essential practice for transforming unstructured, open-ended responses into structured, actionable data. If you are seeking survey coding capabilities, Ascribe, with over 25 years of experience and having processed over 6 billion responses for the top global market research firms and corporations, offers cutting-edge open end analysis solutions. Ascribe Coder is the leading coding survey platform designed for high efficiency and precision, and CX Inspector is the premier text analytics solution equipped with advanced tools to decipher and illuminate the underlying sentiments and insights in textual data.

For a deeper dive into how Coder and CX Inspector can transform your data analysis process and significantly enhance your research outcomes, we invite you to schedule a demo and let us show you what we can do using your own dataset.

What is an Engagement Survey?

Engagement survey, also known as an employee engagement survey is used to determine the level of employee engagement within an organization. Now, how exactly do we define the term “engagement”? Employee engagement refers to a person’s sense of connection to a company. They focus on how dedicated they are towards their work and whether or not they will stay with an organization on a long-term basis.Employees are the core of an organization. Therefore, it is important to understand how they feel about the organization’s work environment. Engagement survey is the best way to gauge the extent to which employees feel valued and respected at their workplace.Engagement surveys enable an organization to focus on specific issues being faced by the employees and transform the workplace. Employees who work in a pleasant environment are happier, which promotes customer satisfaction.

Why conduct an Engagement Survey?

An employer with a large workforce will not be able to have a one to one conversation with every employee. This is where organizations can rely on an engagement survey.

• Engagement surveys provide a platform to the employees to share their feedback revolving around the organization policies, work environment, management, etc. 
• It gives organizations an insight into the key issues they may not even be aware of. Organizations can use the results of engagement surveys to make well-informed decisions.
• Engagement surveys can be filled by employees anonymously. This enables them to express honest opinions.
• To assess if there have been any improvements, the responses to the same questions can be compared to past responses. Organizations can establish a baseline against which they can measure their progress year after year.
• Engagement surveys give organizations a sense of accountability. The organizations recognise that employees at all levels contribute to the success of an organization. 

What makes a good engagement survey question?

The ideal engagement survey questions focus on the following elements:

• Questions that give a better understanding of the employees’ emotions and attitude.
• How motivated the employees are to work within the company.
• Suggestions on ways to boost staff productivity.
• Questions that are clear, concise and use neutral wordings.
• Questions that will actually be able to draw actionable results.Engagement survey questions to ask

It is important to ask the right engagement survey questions that will provide impactful results. A few questions that could be a part of your survey are listed below:

Employee satisfaction

• How do you feel about your job?
• Are you satisfied with the salary and benefits given to you?
• Would you recommend our organization to a friend or relative?
• Are you happy with the work you've been given?
• Do you enjoy working with your team?
• Do you look forward to going to work?

Career goals

• Does the organization provide opportunities for growth?
• Does the organization inspire you to give your best?
• Is there an alignment between your goals and the company objectives?
• Do you have the tools to work efficiently?
• Does your manager and coworkers support your career goals?

Team Culture

• Do you and your coworkers share ideas and work in collaboration?
• Do you spend time thinking about ways to improve your team’s work process?
• Does your manager listen to your concerns with patience?
• Does your team encourage you to complete your tasks?
• Do you feel recognized by your team for your contribution in work?

Psychological well being

• Are your team members able to openly discuss tough issues?
• Do you know whom to talk to if you are facing issues that are not under your manager’s control?
• Do you feel comfortable sharing your opinions in team meetings?
• Does the organization support a healthy work-life balance?
• Are you able to dedicate time to your family?

Effective communication

• Are you informed effectively about any refinement in policies and procedures?
• Do you feel there is a two-way communication with the organization?
• Does the management communicate all information with transparency?
• Do you feel managers and coworkers communicate information clearly?

Open-ended questions

• Are there any improvements to be made in the work culture?
• What policies or practices do we need to change?
• According to you, how can we improve employee engagement?
• Would you like to share anything about your experience at our organization?
• Is there anything more we should have asked about in this survey?

How to make the most of engagement surveys?

• Begin by determining why the survey is being conducted. Decide whether you are addressing specific concerns or seeking feedback on a recent policy change. This will enable the survey creator to ask the right questions in a clear and concise manner. 
• Engagement surveys can be paired with pulse surveys. Engagement surveys are usually conducted annually. However, these are not enough. In order to address the issues being faced by employees at a specific instant of time, pulse surveys can be conducted on a frequent basis.
• A mix of open and closed-ended questions should be included. 
• Conducting a survey is not enough. Organizations should be able to transform the results into actions. It's critical to recognise and acknowledge the employees' responses.

See Voxco survey software in action with a Free demo.Book a Free Demo

Final words

Employee engagement is complex, so an engagement survey should include a variety of factors. Use the engagement survey questions listed above to put together a survey that will bring you closer to your objectives.

When customers feel heard, they feel valued. This develops a sense of trust and loyalty towards the brand. By actively gathering feedback and acting upon it, you can demonstrate your commitment to improving and delivering a great experience at every interaction.

VoC strategy can help you nurture long-lasting relationships with customers and thrive in the competitive marketplace. In this article, we’ll look into several Voice of Customer examples and some common mistakes you should avoid maximizing the impact of the VoC program.

What is Voice of Customer ?

Voice of Customer, or VoC, is the process of gathering customer feedback in an attempt to identify any existing gaps between customer experience and customer expectations. The data gathered in VoC allows businesses to identify where customer grievances lie so that such gaps can be filled.

VoC programs are conducted differently in different organizations and there isn’t one correct way on how to conduct them. To reflect this, we’ve provided four examples to reflect how different organizations leverage their Voice of Customer program in a distinct way to reap the most benefits.

Examples of Voice of Customer

In order to better understand the concept of Voice of Customer, let’s take a look at a few examples of how different industries organizations in different industries leverage VoC to understand their customers and their expectations.

1. VoC in Subscription-First Ecommerce: Subbly

Subbly is a subscription-first e-commerce platform that successfully used VoC for product refinement. Subbly did so by creating an online page that is dedicated to gathering feedback from their customers. They often run surveys on pricing to understand how customers feel about their subscription plans. By constantly gathering data to hear their customers’ voices, Subbly is able to shape their product roadmap and decide which features to release.

2. VoC in Banking: Johannesburg Bank

The banking industry is a highly competitive space and banks must constantly change to keep up with customer expectations and rival banks. In this sector, it is very hard for organizations to keep their churn rate low due to competing organizations constantly introducing new and innovative ways to poach customers.

In order to remain competitive and reduce their customer churn rate, Johannesburg bank decided to use VoC and perform a comprehensive analysis on customer feedback across all their official channels, as well as social media. To do so, they used a hashtag-based campaign to gather feedback from customers regarding what they loved and hated about their bank. This campaign was able to generate two million pieces of feedback, which was sifted through to create custom categories that divided the feedback based on what bank service they were regarding (mobile banking, fees and charges, etc). Named Entity Recognition, or NER, was then used to conduct sentiment analysis by allocating sentiment scores to the different comments. These scores allowed the bank to identify which areas and issues needed their attention most.

3. VoC in Primary Healthcare: Nahdi Medical

Nahdi Medical is a major healthcare provider in Saudi Arabia. They leveraged a VoC program using survey documents and the feedback gathered from online forums. Once the data was gathered, sentiment analysis and text analytics were carried out to identify the different categories of feedback. After the feedback was clustered into different categories (by quality, convenience, price, etc), the result acquired was a highly detailed picture of Nahdi’s performance. This allowed Nahdi medical to identify gaps in CX and customer pain points so that these could be addressed effectively.

4. VoC in Insurance: Lawley

New York-based insurance firm Lawley used a Voice of Customer program by collecting customer feedback through NPS^® (Net Promoter Score^®) surveys. They used relationship NPS^® surveys to measure customers’ overall perception of their brand, which enabled them to benchmark their NPS^® score across different regions. By doing so, the organization was able to identify many different issues within the customer journey. They used this information to identify pain points and follow up with clients on their grievances before they could lose a large number of accounts. VoC helped Lawley improve customer service and prevent customer churn.

Voice of Customer: 6 mistakes to avoid

VoC strategy can be incredibly valuable for your business, but there are common mistakes that you should be aware of. We have listed down six such errors you should avoid to maximize the effectiveness of the Voice of Customer initiative.

1. Lacking clear objective:

Most often, brands don’t have a clear objective for their VoC program. A clear goal helps you guide the strategy effectively and efficiently, to align every step to the ultimate goal. Without a clear grasp of what you want to achieve by gathering customer feedback, you won’t be able to gather meaningful insights.

2. Not segmenting customers:

You might serve a broader audience, but ignoring customer segments can be a fatal mistake. You should leverage the feedback to segment customers based on preferences, needs, demographics, purchase behavior, etc. This will enable you to tailor surveys and feedback mechanisms to cater to specific segments. You can ensure that all the initiatives address the diverse needs of the customer base.

3. Relying on only a single feedback channel:

You should not over-rely on a single feedback channel to gather customer insights. This can limit your knowledge of customers and the overall population. Conduct focus groups to engage customers in discussions and gather their feedback via mobile offline surveys. Use phone survey software to conduct interviews. Leverage data from customer support interactions.

Make sure to use multiple channels to gather customer feedback and data to get a comprehensive understanding of the Voice of Customers.

4. Not closing the feedback loop:

This step involves communicating with the respondents how you plan to use their feedback and what actions to be taken. Brands often overlook this step. However, closing the feedback loop helps demonstrate your commitment to making meaningful changes.

It’s a best practice to share improvements, resolutions, and updates with the survey respondents and keep the communication transparent.

5. Not acting on the feedback:

You need to show customers how their feedback has made a difference. Customers put effort and dedicate their time to offer you their insights, not taking action based on those feedback will show them that you don’t care about their opinion. Ignoring taking action can lead to a loss of trust.

Use survey software that enables you to analyze data, uncover insights, and act on them in a timely manner.

6. Failing to involve employees:

You need to share the voice of customers across the entire workforce and not just among the leaders. Every employee can benefit from customer insights and optimize decision-making to drive customer-centric initiatives.

Not involving or engaging employees in your VoC program will hinder the organization’s ability to align internal and external business processes.

Avoid these mistakes to improve customer satisfaction and drive business growth using a robust VoC strategy. Listen, act, and engage your audience and boost loyalty.

Takeaways

VoC analysis can be leveraged by organizations in many different ways, using many different techniques, to understand customer sentiment. When conducted effectively, Voice of Customer analysis can provide organizations with actionable insights that can be used to meet customer expectations and effectively reduce churn.  

It is a strategy that offers a direct channel for customers to express their opinions and for you to listen to their unfiltered experiences. By leveraging survey software and data analysis tools, you can gather and analyze vast amounts of customer feedback to align your offerings with customer preferences and deliver a great experience.

FAQs on Voice of Customer

What is the voice of the customer?

The VoC process or Voice of the Customer refers to the strategy of gathering customers’ feedback and opinion regarding a product, service, or brand. The strategy involves actively listening to what customers are saying and using the insight to take effective initiatives that improve customer satisfaction.

What are examples of VoC products?

Some examples of VoC products or methods are:

• Surveys
• Interviews
• Focus groups
• Customer feedback
• Customer journey mapping

What are the 4 types of customers?

The four types of customers you may come across in the business are:

• Loyal customers – Those who choose your brand consistently.
• Price-sensitive customers – The customers who make purchase decisions based on the price.
• Impulsive customers – Buyers who make spontaneous purchases.
• Nedd-based customers – Consumers who purchase depending on their needs or problems they want to address.

What are the 4 main customer needs?

The four main customer needs generally include the following:

• Functional need – Buyers need products/services that resolve a problem or meet a specific function.
• Emotional need – Consumers seek products that evoke positive emotions.
• Social need – They desire services and products that help them fit in and meet social expectations.
• Personalization need – Customers want their experience to cater to their unique needs and preferences.

What is the purpose of VoC?

The purpose of the Voice of Customers program is to listen, understand and meet customer expectations. The strategy enables you to make informed decisions and prioritize improvements based on customer feedback. This allows you to develop customer-centric strategies that boost customer satisfaction, drive loyalty, and gain competitive advantage.

What is a Voice of Customer interview?

A Voice of Customer interview refers to a one-on-one conversation between a company rep. And a customer. The aim is to gather detailed insights about the customers’ experience with the brand and its offerings.

The interview provides and opportunity to dig deep into the concerned topics, understand customers motivations and pain points that can help you share your business strategies.

What is the voice of customer analysis?

Voice of customer refers to the feedback your customer gives with regard to your brand, product, and services. Whereas its capturing and analysis are known as the voice of customer analysis.

As we know, brands are used to getting feedback from customers through various channels and methods like surveys, interviews, social media, etc. The real struggle starts when you have to make sense out of it. This is called analysis of the voice of customers, and it is a market research technique that gives brands an understanding of their customer’s needs and wants.

Voice of customer analysis will capture customers’ expectations, opinions, and preferences. Brands will work on these factors, analyze them and find insights to work upon which will give them a competitive advantage in the market.

Shape your brand with the customer’s voice.

Voxco helps you gather customer insights to help you get started in improving your brand awareness.

Why is it important to analyze the voice of customers?

• Voice of customer analysis will boost your marketing efficiency.
• It allows you to consider every customer’s feedback and analyze it for improvements.
• Voice of customer analysis gives you an idea about what causes the customers to stay or leave your brand.
• It helps you by understanding customers’ purchase decisions and choices.
• Voice of customer analysis will draw a picture for you about who are the most profitable customers and how you can gain new target customers to buy your products.

How to perform the voice of customer analysis?

You, as a brand, will have an abundance of data to deal with after gathering customer feedback. How your analysis turns out highly depends on the voice of customer tools you use to make insights out of the gathered data.

We will see in this section how to perform the voice of customer analysis on your voice of the customer:

Start with a question

This is the very first step. You might have a goal or a purpose before starting your Voice of Customer program. So even before you start your analysis, be firm and clear about your question. WHY are you analyzing your data for?

The analysis question can look something like this:

• To understand what the customers like more.
• To understand what the customers hate.
• To understand the new trends in the market.
• To get areas where the products and services can be improved.

Apart from this, you need to be sure about HOW you will gather your data, WHICH analysis tools you will use, and WHAT TYPES of analysis you will perform.

Gather your data

The second and pretty obvious step is to gather your data. It starts by selecting your sample customer or targets customer to feature as your voice of customers.

Make use of methods like surveys, interviews, focus groups, social media, etc., to have your data through customer feedback. Make sure to frame your surveys and questionnaires which are totally focused on your goal or survey topic and question. And then comes the voice of customer analysis.

Sentiment analysis

Here comes the analysis of data. The capabilities of AI mostly do this job. The AI basically identifies the sentiments in the feedback depending on whether it is positive feedback, negative feedback, or neutral. Further on this analysis, you can divide your path of progress into two major categories:

• Increase what people like.
• Improve what people hate.

Well, it does sound easy to label feedback as positive, negative, and neutral. But this is when you are doing it manually, which is, of course, not possible. Remember you are using an AI tool to measure these factors. So the system has been able to understand sarcasm, indirect criticisms, and twisted comments.

But once you start to analyze the data according to your use case, the system will get used to the analysis plan and work better towards the goal.

Natural language processing

Natural language processing, or NLP, is an essential way of processing your natural language. Voice of customer analysis needs the analyzing system to read through all the data and be able to make sense of the information. It will draw out the trends, preferences, topics, and issues by analyzing the words.

Plotting of words with their hidden trends and issues looks something like this:

This method gives you one label to represent a huge amount of data. Automated segmentation is the best way to cut your time-consuming analysis process.

Data correlation

Raw data makes no sense. You will have a lot of random data from customers, meaning god knows what. In every data analysis process, the raw data is useful only when the information is drawn from them. The same goes for the voice of customer analysis.

Let’s say you have your brand’s NPS score on the social media reviews of your product, only then will you have an idea of what conversations are influencing your NPS.

In case you are concerned about negative reviews, you can analyze the product reviews under shopping websites and social media to address those issues.

Draw conclusions

After you have labeled and segregated your data, it is time to make information out of it that will actually help you get to certain conclusions and insights to work on.

In this phase, you will have a clear understanding of how relevant your data, information, and conclusions are with respect to your targeted voice of customer goals.

Initiate improvements

Apparently, just getting to conclusions isn’t enough, right? The way you act upon the analysis and make improvements in your branding weighs a lot on the customers’ perception of you. Customers know they have given their feedback, and now they will wait for the corresponding changes in the brand.

As a brand, it is your responsibility to prove to your customers that you consider their opinions valuable and are willing to improve for them. This is the act that contributes most to the voice of customer analysis. For instance, if customers voiced their concern about finding it difficult to reach your support team via phone, consider adding a dedicated customer support business phone number to your website and social media profiles.

FAQs

What is the voice of customer analysis?

Voice Of Customer analysis refers to the process of extracting actionable insights into customers’ perspectives toward a brand, product, or service. It helps organizations to make decisions that are aligned with their customer’s expectations and ultimately helps improve customer satisfaction.

How to analyze the voice of customers?

Analyzing the voice of customers is an ongoing process and involves understanding and interpreting customer feedback, opinions, and preferences to gain insights into their needs and expectations.

Here are the key steps that go into analyzing the voice of customers:

1. Gather customer feedback
2. Categorize and organize feedback
3. Use sentiment analysis
4. Identify key issues and trends
5. Quantify the feedback
6. Prioritize improvement areas
7. Create customer personas
8. Share insights across the organization
9. Take action and monitor progress

What is customer analysis?

Customer analysis refers to the process of gathering and analyzing data and information about customers to gain insights into their characteristics, behaviors, preferences, and needs. It involves studying customer demographics, purchasing patterns, interactions, and other relevant data points to understand and segment the customer base.

How to do a customer analysis?

Customer analysis is done by gathering customer data (mostly with the help of online surveys), analyzing customer data, and then generating actionable insights from that data. It is a process of understanding customer characteristics, behaviors, preferences, and needs.