Have you ever wondered what's the key to creating and running a product successfully?
These are things like building what your users need, hearing their feedback, and analyzing and addressing their issues. All these come under 'qualitative analysis', which is quite different from 'quantitative analysis' metrics like NPS and CSAT, as qualitative metrics can be measured on a scale like you would do with things like NPS.
In this guide, we'll talk about 'qualitative analysis', see how it's different from quantitative analysis, and explain why you should keep track of various qualitative metrics in your organization.
If you decide to stick until the end — you'll also get to see a way to automate all these.
Table Of Content
What Is Qualitative Analysis?
Qualitative analytics refers to anything that can't be quantified in terms of number, scale, or graph. A good example of qualitative data would be user feedback, which can be analyzed as good or bad but can’t be rated on a scale of 1-10.
Qualitative analytics is primarily used for analyzing a specific subject based on its behavior, characteristics, and non-numerical indications to identify its overall sentiment, quality, value, or any other aspect.
It is subjective and inductive, and it can help you analyze your business's value or prospects. Ask why, where, and when questions in order to understand feelings, thoughts, opinions, motivation, and other quantitative factors.
Quantitative analytics, on the other hand, is based on the subject's numerical value and can be qualified, counted, or measured.
Why Should Product Managers Care?
As a product manager, you may wonder why qualitative analysis should be a significant part of your job. Well, the answer lies in its transformative ability to provide deep insights beyond plain numbers. It's this analysis that empowers you to understand the 'why' behind user behaviors, their motivations, and preferences. While quantitative data can show you 'what' is happening, it's the qualitative analysis that uncovers the invaluable 'why'. It gives a more comprehensive picture of user experiences, feelings, and expectations, thus enabling you to make informed product decisions and strategies. So, for a product manager seeking to deliver a user-centric product, qualitative analysis isn't just an option - it's a must.
For a deeper understanding of both, let's look at the differences between qualitative and quantitative analytics.
Qualitative Analytics vs Quantitative Analytics
Qualitative analytics differs from quantitative analytics in several key ways.
Qualitative analytics is subjective and descriptive, expressed using words, and asks open-ended questions to uncover why and how. It relies on content analysis, coding, and thematic analysis for data interpretation.
On the other hand, quantitative analytics is objective and numerical, expressed using graphs and numbers, and asks close-ended or multiple-choice questions to determine what and how many. It relies on statistical tools and software for data analysis.
Key differences
| Qualitative Analytics | Quantitative Analytics |
|---|---|
| Subjective and descriptive. | Objective and numerical. |
| Expressed using words. | Expressed using graphs and numbers. |
| Ask open-ended questions. | Ask close-ended or multiple-choice questions. |
| Seeks answers to why and how questions. | Seeks answers to what and how many questions. |
| Relies on content analysis, coding, and thematic analysis. | Relies on statistical tools and software. |
| Analyzed by summarising, categorizing, and interpreting. | Analyzed by math and statistical analysis. |
| Data collected: Gender, language, qualifications, religion, etc. | Data collected: Age, height, income, weight, etc. |
How to Use Qualitative Analytics?
Qualitative analysis uses subjective judgment based on non-quantifiable data that can be helpful in market research, interviews, surveys, feedback, and other areas. The data gathered is then analyzed to get useful information and valuable insights to better understand your customers and business.
There are numerous methods for analyzing data, such as:
Sentiment Analysis
Sentiment analysis helps determine the presence of positive or negative sentiment in qualitative data. It will assist you in determining the emotional tone of the message and help you better understand your customers. You can make smarter decisions by analyzing the user's opinions or sentiments related to your business in general or any product or service you offer.
Content Analysis
Content analysis helps evaluate the presence of specific words, topics, or concepts within qualitative data. This allows you to draw reliable conclusions about what your users think about your brand or company and how you can change how they think.
Thematic Analysis
Thematic analysis helps identify and analyze recurring patterns or themes by reading through data to identify their underlying meaning. It is useful when looking for subjective experiences and opinions and has a huge amount of data that can then be further divided and categorized to make it easier to process.
Analyzing qualitative data is the best way to gain a better understanding of human behavior, whether for research, feedback collection, customer experiences, or to gain awareness of the social environment.
Qualitative analytics can help your company better understand its customers and their problems, as well as provide them with the best possible experience they can have with your business. In order to improve customer satisfaction and turn them into loyal customers.
How to Capture and Analyze Qualitative Data?
One of the biggest challenges in terms of qualitative data is collecting and analyzing it. Qualitative data can be found in several places like Intercom, Twitter or even in your support emails and bringing them all together is a big hassle.
For a long time, these things were done manually by a Product Manager, but lately, there have been several tools to help you with this, one such tool being Olvy.
Olvy is one of the best qualitative analytics solutions for collecting and analyzing user feedback. It gives you useful information that will allow you to make data-driven decisions faster.
Let's look at how you can use Olvy to collect and analyze qualitative data.
Data Collection
The first and most important step for qualitative analysis is getting the data from various sources. Most tools rely on manual data transfer or require you to use a proprietary feedback tool. Olvy, on the other hand, integrate with platforms like Zendesk, Intercom, email and others to pull all the necessary data, which can used and analyzed within its dashboard.
Olvy can easily integrate with platforms such as Twitter, Slack, Discord, or wherever your customers are to collect feedback and ensure that none of your customers goes unnoticed.
You can collect feedback from multiple sections of your app in a single spot by adding Olvy's feedback widgets to any page.
Olvy's feedback widget makes it simple for your customers to provide feedback at the right place and time. It allows your users to add ratings, respond to a custom inquiry, and upload files. They can also simply capture screens and submit them with video recordings or screen captures.
Data Analysis
The next step is the analyze the data that's been collected from various sources.
When using Olvy, this is yet another easy step, as all the feedback is automatically analyzed for you at the click of a button. With Olvy's GPT-4-powered AI, you can summarize thousands of user feedback with a single click. You can use sentiment analysis to determine your consumers' emotional tone.
This can help you identify people who are not satisfied or have had a positive experience with your company, allowing you to reach out to them directly and optimize your strategies for an improved customer experience.
It also saves you time by categorizing your feedback data, allowing you to focus on the main problems.
You can learn more about what your users are talking about with its keyword extraction, and it can also discover common phrases and group them for you to filter.
With a few clicks, you can automate your entire workflow and make decisions based on important customer insights that can assist you in building stronger relationships with them.
Feedback Loop
Last but not least, Olvy also closes the feedback loop. This means that all the feedback & issues are responded to directly at the source. Let's understand the flow with a quick example.
Think a user called Sam mentioned a bug on your Discord channel. Organizations usually reply by email or take the time to respond back on Discord. With Olvy, you can directly pass on the issue to your development team via Jira or Linear integration, and when fixed, Olvy's bot (name and logo are editable) responds to all users like Sam directly on the Discord channel.
Conclusion
To summarize, leveraging analytics tools like Olvy to collect and analyze qualitative data can help you streamline the entire process. You can make informed decisions and adjust your strategies to provide a better customer experience and grow your business's reach.
