In today's competitive market, businesses need to have a deep understanding of their customers to stay ahead of the curve. While quantitative data such as sales figures and customer demographics can provide valuable insights, qualitative data provides a more in-depth understanding of customers' needs and preferences.
Qualitative analysis methods can help businesses gather insights into their customers' opinions, emotions, and experiences. In this blog post, we’ll explore these kinds of methods, and why generative analysis tools are helping companies unveil deeper insights in less time.
To create products and services that keep them happy, product and customer experience managers need to understand their customers’ needs and wants. Quantitative data can only go so far—to get a complete picture, you need to dive into qualitative data as well.
Qualitative analysis methods encompass a broad range of techniques that focus on interpreting data such as open-ended interviews, observations, and customer stories. Unlike quantitative methods that deal with numerical data and statistical analysis, qualitative approaches seek to reveal the underlying patterns, themes, and meanings that emerge from social interactions, experiences, and behaviors.
These methods are effective in understanding the "how" and "why" of human behavior and decision-making, as they can generate insight into complex psychological and social constructs that cannot be easily measured with numbers.
There are a range of qualitative analysis methods you can use, and choosing the right ones depends on the unique goals and characteristics of your business.
Here are the top 5 most common methods for qualitative analysis.
Content analysis is a method of analyzing textual data, which can include written documents, transcripts of interviews, or other forms of written communication. The goal of content analysis is to identify patterns and themes in qualitative data. This can involve counting the frequency of certain words or phrases, identifying the main topics discussed in the text, or analyzing the overall tone of the text.
This type of analysis can be incredibly tedious and time-consuming, and most traditional solutions aren’t able to unlock subtleties such as sarcasm. But modern artificial intelligence (AI) tools such as Viable, which are powered by cutting-edge GPT-4 technology, allow you to generate substantive insights from reams of qualitative data in seconds.
If you want to take your content analysis to the next level, try our AI today.
Discourse analysis is a method of analyzing language in social interactions. This can include conversations, interviews, or other forms of spoken communication. The goal of discourse analysis is to understand how people use language to create meaning in social contexts, which can involve analyzing the structure of conversations, identifying common patterns of communication, or exploring the ways in which language is used to construct identities.
For example, imagine you’re a product manager responsible for a mobile fitness app that allows users to track their workouts and set personal goals. You decide to use a generative analysis tool to gather data from user interviews, app store reviews, and an online forum dedicated to discussing fitness apps.
As the AI churns through the data, it highlights that users frequently mention the term "customization" when discussing their ideal fitness app experience. It also alerts you that users often construct their identities around specific fitness goals, such as losing weight, building muscle, or running a marathon. By understanding these patterns, you’re able to identify an opportunity to improve the app by incorporating more customizable workout plans tailored to different fitness goals.
Additionally, the discourse analysis reveals that users who identify themselves as "beginners" often express frustration with the app's current interface, describing it as "overwhelming" or "confusing." In response, you prioritize the development of a more user-friendly interface that caters to beginners, while still offering advanced features for more experienced users.
Grounded theory is a method of analyzing qualitative data that seeks to develop frameworks based on data gathered through observation and analysis of real-world experiences.
By collecting and analyzing data from interviews, surveys, and other qualitative sources, companies can gain a better understanding of their customers’ needs and wants. This can inform changes to products and services that align with what customers are looking for, as well as help to identify new customer segments and markets that might have not been considered before.
To illustrate, let’s pretend you’re managing a meal planning app. You decide to use grounded theory to better understand various user segments and their specific needs. To gather data, you conduct a series of in-depth interviews with app users from different demographic backgrounds and dietary preferences.
After feeding the data into a Our AI gave this consulting firm a deep understanding of their employee experience while saving time & money, and without a single spreadsheet., the tool tells you that there is a significant segment of users with specific dietary restrictions, such as vegans or people with food allergies, who express the need for more personalized meal plans. It also identifies another group of users who are primarily focused on budgeting and saving money on groceries, consistently mentioning the desire for price comparisons and discount alerts.
Based on these insights, you formulate a grounded theory that proposes two distinct user segments with different needs: one seeking personalized meal plans based on dietary preferences and restrictions, and another seeking budget-friendly grocery shopping features. To address these needs, you decide to develop new features for the app, such as customized meal plans for users with dietary restrictions and a price comparison tool for budget-conscious shoppers.
In this example, you’ve used grounded theory to derive a framework based on real-world data, enabling you to make informed decisions about feature development and cater to the diverse needs of your user base.
Narrative analysis is a method of analyzing stories or narratives that people tell about their experiences. This typically involves collecting and analyzing qualitative data such as reviews, social media posts, and other experiences that customers have with your product or brand.
Product managers can use narrative analysis to identify patterns and themes that can inform product development and marketing strategies. They can also use the insights gained from narrative analysis to create customer personas that represent the needs and preferences of different customer segments.
One area in which narrative analysis could be useful is for a customer experience professional that works for a travel booking website. Imagine you’re in this role and decide to use narrative analysis to better understand your customers' experiences and preferences when using your platform. With the help of a sentiment analysis tool powered by AI, you collect reviews from various sources such as your website, app store reviews, and social media posts where customers share their travel stories and experiences with your platform.
The narratives are analyzed by the AI and reveal that customers often talk about the ease of booking trips through your website. However, some users express frustration when they encounter unexpected fees or hidden charges during the booking process. The AI tool also identifies that users who travel for leisure tend to highlight the importance of discovering unique, off-the-beaten-path destinations, while business travelers focus more on the efficiency and reliability of your platform.
With these insights, you develop two customer personas: Leisure Travelers and Business Travelers. For Leisure Travelers, you decide to emphasize features like personalized recommendations for lesser-known destinations and curated travel guides. For Business Travelers, you focus on improving the efficiency of the booking process, providing real-time updates on flights, and ensuring transparent pricing with no hidden fees.
In this case, identifying patterns and themes in customer experiences led to the creation of targeted customer personas. These personas enable you to tailor your product development and marketing strategies to better meet the unique needs and preferences of each customer segment.
Thematic analysis is a method of analyzing qualitative data that involves identifying themes or patterns in the data. This can involve coding the data into different categories, identifying the main themes that emerge from the data, or exploring the relationships between different themes.
By analyzing these themes, companies can gain valuable insights into their customers' motivations, pain points, and expectations.
Let's consider a real-world example to illustrate—imagine you're a product manager for an online language learning platform. You decide to use thematic analysis to better understand your customers' experience when using your platform, and gather data from social media comments and emails to your customer support team.
With the help of an AI-powered analysis tool, several themes start to emerge from the data. The tool reveals that customers often mention the importance of engaging and interactive content, as well as the need for personalized learning paths. Another theme is the desire for a supportive online community where learners can practice their language skills with others.
Based on these insights, you identify three main themes: interactivity, personalization, and community. To address these themes, you decide to enhance your platform by introducing more interactive content, such as gamified lessons and quizzes. You also work on developing adaptive algorithms that create personalized learning paths for each user based on their progress and goals. To foster a sense of community, you integrate features that allow users to connect with each other, such as chat rooms, video calls, and peer-to-peer challenges.
In this case, key themes in customer feedback have informed the development of new features and improvements for your platform. By addressing these themes, you're better equipped to meet your customers' needs and expectations, ultimately enhancing their overall learning experience.
Each of these methods has its own strengths and weaknesses, and most businesses find that using several of them together provide the best results. When used well, these methods can provide a multitude of benefits to your business.
Data-driven decision making has become a crucial ingredient in successful companies. While quantitative analysis has proven to be a valuable tool, qualitative analysis methods are increasingly gaining attention as complementary and essential techniques for understanding and engaging with customers.
Here are some ways they can help your business:
Quantitative data lays out the "what" of customer behavior, while qualitative analysis of your customer experience unveils the "why." By examining customer feedback, conducting interviews, and analyzing online reviews, businesses can unlock the motivations, needs, and preferences that drive customer behavior. This comprehensive understanding of your customers allows your company to build stronger connections, develop targeted marketing campaigns, and create tailor-made products and services that effectively cater to their needs.
Qualitative analysis provides businesses with a lens to discover untapped potential in their customer base. For instance, you may observe patterns in customer feedback that reveal dissatisfaction with specific features or functionalities. By addressing these concerns through product enhancements, your business will not only retain current customers but also attract new ones. Additionally, as you identify and prioritize emerging customer trends, your company can more effectively innovate to stay ahead of the competition.
Incorporating qualitative analysis methods into your product development cycle can help you refine your offerings and create more valuable solutions. Focus groups, user testing, and customer interviews can provide invaluable feedback on the user experience (UX) of your products or services, empowering you to create enhanced and more effective solutions. This methodology fosters an iterative and agile approach to design, development and enhancement, resulting in a more user-centric product pipeline that can outpace your competition.
We’ve established how valuable insights from your qualitative data can be, but doing so is often time-consuming and demanding work for product teams. The good news is AI-powered generative analysis tools like Viable can make your life a lot easier.
Traditional analysis tools that parse through qualitative data often require significant manual work such as tagging and filtering out noise. Plus, these systems often don't pick up nuances in language like sarcasm or double negatives, and struggle with subtleties like word ambiguity.
On the contrary, generative analysis takes nearly all of the legwork out of realizing useful insights from your qualitative data. They leverage cutting-edge AI technology such as GPT-4 to comb through stacks of text exponentially faster than any human can, allowing you to make better decisions in less time.
Ready to see how Viable’s AI-powered tools can transform your qualitative analysis? Give it a try today.