Traditional market research methods, such as surveys and focus groups, often fail to analyze open-ended responses properly and accurately (if at all). Human coding of these responses can be wildly inaccurate and subjective, and the deeper insights behind the comments may not be understood.
Text analytics offers a powerful solution beyond simple word counting and sentiment analysis to truly understand the meaning behind customer feedback. This advanced technique can be integrated into both primary and secondary research, leveraging existing data or complementing custom market research projects.
This article explains the fascinating world of text analytics, exploring its applications, benefits, and real-world examples. Plus, we’ll introduce our Text Science™ solution, a powerful tool for extracting deep insights from qualitative and quantitative data.
Text analytics is a broad term for analyzing unstructured text, with practitioners using extremely different methods for how to do so.
Many methods use some form of Natural Language Processing (NLP) to analyze how to break apart text into “tokens” that carry information about part of speech, relationships and linguistic hierarchy, and general meaning. Some even apply emotional context related to these tokens.
On its surface, this allows for a more nuanced and accurate interpretation of textual data, revealing more profound insights into customer thoughts, motivations, and behaviors.
Human coding of these responses, generally perceived as the gold standard for analyzing text, can be wildly inaccurate and subjective, and the context behind the comments not represented, leading to the loss of deeper insights and knowledge.
Even very skilled human coders may end up with different interpretations in the same data file, leading to different codes being assigned to similar verbatims. This is particularly an issue with large data sets where multiple coders are assigned to the same file.
Different coders = different interpretations = different outcomes.
Even with very skilled human coders and large data sets, especially if more than one is working with the same data, they may end up with different interpretations, leading to different codes being assigned to a verbatim.
Despite its advantages, standard text analytics solutions have limitations. Many rely on basic pre-defined keywords, frequency counts or sentiment scoring, and dictionary definitions. All of these sources can add more noise and bias to a process intended to strip those aspects away, still missing the deeper meaning behind customer language.
Some practitioners would add a layer of interpretation on top of the core text analytics offering that requires language experts, bringing the human bias component back into the process.
That’s where Text Science comes in. 👀
Text Science is a breakthrough in text analytics solutions. Unlike traditional text analytics tools, Text Science goes beyond surface-level insights by leveraging advanced NLP to attain Natural Language Understanding (NLU), enabling a deeper, context-aware interpretation of unstructured text.
This advanced analytics service allows for a more nuanced and accurate interpretation of textual data, revealing more profound insights into customer thoughts, motivations, and behaviors.
How? Because it dissects human language to understand people put words together. In other words, Text Science is able to pick up on slang, technical jargon, and sentiment based on context, not based on pre-defined notions.
Text Science transforms your open-ended data into an easy-to-understand analysis that reveals the relative importance of words and phrases and their strength of association with each other based on language context.
Text Science services can be applied to a wide range of data sources and inputs, including:
Our custom AI model approach eliminates the need for lengthy machine learning buildouts or training. This allows us to begin your project rapidly, often delivering results within a couple of weeks. For example, if you need to analyze customer feedback from a recent product launch, we can quickly process that data and provide insights without a drawn-out setup phase.
Because we create a custom AI environment tailored to your specific project data, you avoid the inherent biases in pre-trained, generic models. These pre-trained models may be skewed by the data they were initially trained on, potentially leading to inaccurate or unfair conclusions. Our approach ensures the analysis is relevant and unbiased.
Our text science solution works with data in any language. Combined with Drive Research’s global reach for survey sampling in over 110 countries, this makes multilingual research and analysis seamless. Imagine surveying customers in multiple countries with multiple languages, streamline it all, and have impactful results in English.
Our service is project-based, so you only pay for what you need. No subscription fees, platform commitments, or worries about underutilized licenses or support issues exist. This is ideal for projects with a defined scope and timeline, such as analyzing feedback from a specific marketing campaign.
Additionally, our research team can audit your data set for feasibility prior to any proposal being designed. It allows our team to get familiar with the dataset and understand workability and outcomes before committing to your project.
Our Text Science solution provides interactive visualizations, like concept clouds, that allow users to explore data dynamically and engagingly. These visualizations make it easy to identify key themes, understand word associations, and drill down into specific areas of interest.
Our AI-powered analysis reveals subtle linguistic nuances and connections that traditional methods often miss. This provides a deeper understanding of customer language, behaviors, and attitudes beyond surface-level interpretations. For example, we can identify recurring themes and sentiments within open-ended survey responses that might be overlooked by manual coding.
Automating the analysis removes the subjectivity inherent in manual coding, ensuring consistent and objective results. This data-driven approach generates actionable insights to inform marketing strategies, product development, and customer experience improvements. For example, we can identify specific areas where customer language reveals dissatisfaction with a product feature, providing clear direction for product development.
While large language models like ChatGPT, Gemini, and Copilot might excel at generating human-like text and summarizing information, Text Science (our powerful text analysis solution) takes a dissective approach.
Instead of relying on vast datasets to predict and generate responses, it focuses on understanding the underlying structure and meaning within the text itself. This makes it particularly valuable for identifying key customer feedback themes, drivers, and patterns.
Unfortunately, in the world of cloud-based AI, no one can 100% trust those sites and platforms that are not using your data and information to advance their models for all users further. Dumping confidential information or data into ChatGPT is a scary concept.
You can trust the dataset input’s reliability, security, and confidentiality in our solution.
Drawbacks to Text Science are related to text volume. The service can be run on very few respondents as long as a few basic conditions are met around the amount of text per respondent in your study, and the respondents leveraged for Text Science are representative of the target population.
The Text Science solution is more expensive than you might be used to with off-the-shelf tools accessible through Qualtrics, text analytics, sentiment platforms, or social monitoring subscriptions. Still, the difference in value and insights is distinguishable.
If you’re wondering how our models are validated, the short answer is they are self-validating on every single study. By looking at the interactive maps and visual tools we provide as part of the Text Science service, our clients can see the raw verbatims tied to each word/phrase in the map, allowing users to see exactly what is connected to each with a click of a button.
To illustrate the power of Text Science, let’s examine a case study involving Amazon’s Echo devices. Text Science is a powerful tool for analyzing unstructured data, whether open ends from your primary research, accurately summarizing your qualitative results, and as shown here, analyzing reviews scraped from the internet.
By applying Text Science to reviews, we were able to uncover the following:
Due to the quantitative nature of Text Science, we can include human language into our machine-learned Driver Pathways® approach to understand how language and closed-ended measures interact with each other and ladder up to a desired outcome, such as Purchase Intent, Star Rating, or any other measure you’d like.
Text Science offers a powerful new way to analyze and understand qualitative feedback from comments and open-ended responses. By leveraging the power of NLP and NLU, businesses can gain deeper insights into customer language, identify key drivers, and make data-driven decisions that improve their products, services, and customer experiences. The ROI and value obtained from Text Science are far beyond what you might be used to with human open-ended coding, text analytics, or sentiment analysis from your platforms and tools.
Text Science is offered in partnership with Marketing Analysts. Drive Research and Marketing Analysts have a history of fueling top brands with insightful and advanced research techniques going above and beyond what basic research services and insights are offered industry-wide. If you want to level up your research, a consultation call with our team can help.
Want to learn more about how Text Science can benefit your business?
Contact our global full-service market research for a consultation.
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