Imagine if you could wave a magic wand and perfectly understand your customer’s needs, wants, and issues. You would know exactly what to say to get them to buy from you.
That’s the power of verbatim analysis.
Verbatim analysis is a way to quantify qualitative data — such as open-ended survey responses, social media comments, online reviews, and customer support interactions — to better understand your customers.
In this article, you will learn:
What verbatim analysis is
Tools & techniques to make analyzing verbatim easier, faster, and more useful
How to use the data gathered from your analysis to find real insights and grow your business
Let’s dive in.
What is Verbatim Analysis?
Verbatim analysis is the process of examining open-ended text responses (aka “verbatim”) from employee or customer feedback, transforming raw text data into structured, meaningful insights. Verbatim refers to word-for-word customer comments, whether that’s a survey response or customer support ticket.
In other words, verbatim analysis is a way to understand your audience’s thoughts, preferences, and experiences so you can create a better product, improve your marketing, and grow your business.
Here’s how it works:
You take open-ended survey data, such as from a customer satisfaction survey, create a codebook, then code the data using codes from your codebook. This is called verbatim coding.
These “codes” are like themes that you tag the unstructured data with to turn qualitative data into quantitative insights.
Why is this useful? Let’s look at an example.
A restaurant wants to improve their service, so they hire a customer insights analyst to run a survey and analyze the verbatim. Their primary open-ended question is “What do you think about the new menu?”
One of the verbatim comments was “I like the items on the menu, but I feel like it has too many options. I’d also like to see more photos of the food so I can choose based on an image rather than text.”
The analyst might assign the following codes to this verbatim comment:
“Like the food”
“Menu is too long”
“Needs more photos”
To gain a clear understanding of what matters most to customers, the analyst repeats this coding process across all feedback, categorizing similar themes and concerns. By doing so, they can quantify how often each theme appears, providing insights into the prevalence of each issue or preference.
This quantification helps the restaurant prioritize changes with the greatest impact on customer satisfaction. For example, based on the overall survey results, they might decide to add photos to their menu and reduce the total number of items to improve the dining experience.
This is how verbatim analysis helps businesses: it provides actionable data that can boost customer loyalty, meet customer expectations, and enhance satisfaction.
In the realm of market research, verbatim analysis is a cornerstone for turning those qualitative answers into quantitative data to capture the nuances, emotions, and context behind customer feedback.
It bridges the gap between numbers and sentiment, offering a holistic view of the customer journey so you can understand the WHY behind the numbers.
Let’s look at how it’s done:
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Traditionally, verbatim analysis was a manual process, with researchers categorizing responses into themes one by one, often using Excel spreadsheets or even pen and paper.
While this manual method of verbatim analysis can be accurate, it is extremely time-consuming and prone to bias, making it challenging and expensive to manage large datasets effectively.
Not to mention, if you’re trying to analyze data that is coming in every day — such as customer reviews or customer support responses — it can be nearly impossible to keep up with.
Some researchers use keyword-based verbatim coding software to speed up this process. This software quickly identifies recurring keywords within open-ended responses, providing an overview of what most customers are discussing.
Researchers often use this keyword data to create visualizations, like word clouds, to highlight the most frequently mentioned terms in customer feedback:
The problem with word clouds is that they are often meaningless. Just because the word “service” appears in most responses, what does that say ABOUT the service? Was the service great, or was it awful? You can’t tell from a word cloud alone. Without context and sentiment analysis, these keyword-based softwares are just not good enough and can't replace manual coding.
The bottom line is that, in many cases, companies invest massive time and effort into coding responses to open ended questions, consuming valuable time that researchers could otherwise spend on deeper analysis and strategic work.
Or worse; they just avoid verbatim analysis because it’s too demanding, and miss out on the insights from collecting and analyzing open text feedback.
Rather than simply detecting keywords, it leverages advanced text analytics to uncover patterns and customer sentiments quickly and accurately.
Our data analysis software uses semantic coding, which (unlike traditional keyword-based coding) uses AI to understand the meaning and context behind words.
This means:
No need for manual keyword collection, making it suitable even for smaller datasets.
The ability to differentiate based on context (e.g., distinguishing between “the service was great” vs. “the service was awful”).
Flexibility in handling different expressions, spelling errors, slang, and even cynicism.
Cross-language capabilities, allowing codebooks in one language to be applied to another.
Easy customization of codebooks, enabling quick adjustments to code names and descriptions.
AI-powered verbatim analysis software is the future of market research. It’s the fastest, easiest, and most accurate way to uncover customer pain points and make customer feedback more useful.
Verbatim coding is an essential process for analyzing qualitative data in market research, such as open-ended survey responses and textual customer feedback. Think of it like this — your surveys and customer reviews contain hidden treasures about how to improve your business, and the process of coding verbatim is like drawing the treasure map.
Learn the best practices for coding open-ended questions in qualitative research. Discover tools & techniques for analyzing data to gain actionable insights.
