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How many PS5s can you buy before you blow through your whole salary?
How many emails are in your inbox right now (including the one from me)?
What’s the age of your favourite barber?
Notice a trend in the type of questions above? All of them have a set answer. A hard number, a way to measure to come to a conclusion/result. Although your barber might not be happy with you for revealing his age, we all know that he is 52. It’s objectively true. A fact!
This is what quantitative analysis deals with. Information that can be measured and given a numerical value is called quantitative data.
Now, you might not want to know the answers to the questions above. However, you might be dealing with some of the below questions daily-
You put quantitative data through statistical analysis to make sense of the numbers. Since numbers are at the core of maths, it is easier to digest and understand them that way.
Has this post been a fun read so far?
What’s your opinion on the latest Marvel's Spider-Man 2 game?
Why do you go to a barber who is 10 miles away over the one across your street?
A stark contrast from quantitative analysis that deals with absolutes, qualitative deals with described language. As you can see, none of the above questions can be expressed in numbers. Rather, one might have a strong and clear (although subjective) opinion.
The data received from such questions cannot be applied to a large population. In such cases, it’s usually a small sample set that is used to gather information & conclusions are often extrapolations.
Some questions that often come up in a business context in such analysis could be as follows -
With qualitative analysis, the responses to these questions can vary from person to person. For instance, if out of a group of 25 individuals, 13 favor the pastel-green UI (meaning over 50% prefer that color), it doesn't guarantee that a similar proportion in a larger group will share the same view.
Let’s talk about products, shall we? Well, you don’t have much of a choice since that’s what this whole section is about.
If you want to build a great product, knowing everything about the product you’ve already built might be the best place to start. That’s exactly why analytics exist. Product analytics is arguably the most important part of your product’s success.
Product analytics is the data related to your product’s usage and its performance. It is also the understanding of your customers’ experience and their engagement with your product. The collection, analyzation and interpretation of data about your users’ behavior when using your product will help with -
The data required to analyze your product can come from various sources like analytics tools, customer relationship management (CRM) systems, user feedback, surveys, and more. The data collected can encompass a wide range of metrics and events, such as user actions, user demographics, and usage patterns etc.
In this broad spectrum of analysis of data, we’re going to be focusing on what makes qualitative analysis so important, on why quantitative analysis is crucial and on what you should choose for your product. So, let the qualitative vs quantitative analysis battle begin!
There’s a lot of quantifiable data you’ll generate when you start tracking your product, its users and their engagement properly. While product analytics provides a holistic understanding of how users engage with a product and the insights to enhance it, the quantitative aspect of product analytics offers a more numbers-driven perspective. Quantitative analytics is characterized by hard data, key metrics, and statistical analysis.
The foundational block of any kind of analytics is the data. It’s the same with quantitative analytics. The data in this case is the concrete information about user interactions and behaviors. Think of it as the ingredients in a recipe. This data includes user actions, page views, clicks, and much more. It's like the raw material that needs to be refined. By collecting and analyzing hard data, you can learn a lot about your product and the improvements needed.
Once you have the data, you’d also need a high-level view of your product’s health. Like a compass for navigation, product managers rely on these high-level views of key metrics to steer their product strategy. These metrics can include user acquisition, engagement, retention, and revenue-related numbers. They help you understand if you're on the right course or if you need to adjust your sails.
Having all the data & a view of the whole land might sound good enough, but whats crucial is the use the data to develop insights. Statistical analysis is the process that’ll get you to those insights. It allows you to identify patterns, anomalies, and correlations within your data. It helps you answer complex questions, like why some users drop off at a specific point in the user journey or how changes to a feature impact user behavior.
While quantitative product analytics excels in numbers and statistical precision, it often fails to address a key aspect in the shadows - the 'why' behind the data. This is where qualitative product analytics become important, as it focuses on understanding the human experience within your product.
Even though I might know that I can get about three PS5s with a month’s worth of salary, comprehending why anyone would make such a seemingly extravagant choice is a whole new puzzle. Many times, that 'why' holds the key to whether the decision is sensible or frivolous.
For instance, consider this: if the purchase is pledged to brighten the lives of children in an orphanage, the decision seems altruistic and logical. But if it's solely for personal indulgence, you might suggest a second job to fund such a desire.
Vain examples aside, qualitative analytics introduces you to the voices and stories behind the data points. It's akin to having personal conversations with your users to learn their perceptions, preferences, and pain points. It's all about grasping the motivations and opinions that drive their actions. Within your product's context, this path opens the door to questions such as:
About time we move on to the meat of the matter. What’s the difference between quantitative product analytics and qualitative product analytics? Why should you choose one or the other? What is involved in the process? How do they help?
Since we have a bunch of things to address, let’s break it down -
Quantitative product analytics is like having a treasure trove of user data at your fingertips. It revolves around the collection, measurement, and analysis of numerical data points related to user interactions with your product. This could include metrics, statistics, and key performance indicators (KPIs). These are numbers that’ll tell you more about your product and user experience than words ever could.
Qualitative analytics - the magnifying glass for user experiences. It’s like the friendly chat you have with someone over a cup of coffee. It's all about understanding the human side of your product's story. You’d want to know why the person you’re chatting with chose the cafe over the fast food joint nearby - a qualitative analysis on the matter reveals the backstory and motivations of a person. It uncovers the motivations, opinions, and emotions driving user actions on your product.
Hope you’re ready for some more weird analogies.
Let’s get to it then, shall we? How do you decide which one is better for your product? Should you focus on the “how many” or “how often” type of questions or the “why” type of questions?
Qualitative vs quantitative product analytics is an age-old question. Both are really powerful tools you’d want to have at your disposal when looking to enhance your product's performance. Both methods provide valuable insights, but choosing the right one for your specific needs is crucial.
The best person to choose between the two is probably you, my dear reader. However, since you’re this far into this guide, it would be rude of me to not tell you how you can figure it out. If you’ve enjoyed all the bullet point lists so far, you’re in for a treat. Because that’s exactly what follows next.
The answer is - NO. If you didn’t spot this next bit coming your way, I have some serious questions. We’re evolved enough as a species (by species I mean product managers) to be able to get the best of both worlds.
The debate surrounding the juxtaposition of "qualitative vs. quantitative" has long existed in the field of product analytics. As we’ve seen throughout the previous 3000 words of this article, each approach brings its own unique strengths to the metaphoric table. We've extensively explored these differences and their respective benefits. HOWEVER… what if the secret to unlocking the full potential of product analytics lies in blending both qualitative and quantitative methodologies? I don’t know why that was framed as a question when the next part is all about telling you that it is! The hybrid approach can provide a well-rounded, comprehensive solution to the challenges that every product manager faces.
Qualitative product analytics offers a ton of insights into user experiences. “Profound understanding of user needs” - is the way I would describe it. As discussed previously, qualitative analysis decodes the elusive 'why' behind user actions. Below you will find some forced examples to illustrate how this approach can complement quantitative analysis:
Qualitative data helps improve user experience. It helps identify where users encounter obstacles, experience confusion, or face roadblocks within your product. Qualitative analytics allows you to make targeted improvements to provide users with a smoother, more enjoyable experience.
Forced example #1: Let's consider ExAm’s mobile banking app. User session replays highlight a recurring issue where customers struggle to locate the "Forgot Password" link. This is qualitative data that points to a specific aspect of the user experience that needs change. The quantitative side of the hybrid approach can further support these findings by revealing how many users drop off at this stage due to password-related issues.
By pairing session replays with quantitative analysis, the product owner at ExAm can pinpoint precisely where users are getting stuck in the user flow, leading to more effective and data-backed improvements.
Qualitative data also makes feature prioritization more accurate and aligned with user preferences. Listening to user opinions and identifying pain points enables product teams to prioritize feature development based on what users want the most.
Forced example #2: Imagine you own TapDown - a project management software. Through qualitative analysis, you discover that the majority of users find the current task assignment feature cumbersome and inefficient. This qualitative insight can guide the decision to prioritize the revamping of this feature. Quantitative analysis can complement this by measuring the subsequent increase in user engagement after the revamp and the reduction in support requests related to task assignment. This fusion of 'what' and 'why’ in the hybrid approach ensures that feature development aligns with user expectations and their needs.
Qualitative analytics takes center stage in the world of content personalization. Understanding user interests and preferences allows you to deliver tailored content, recommendations, and experiences that resonate with your audience.
Forced example #3: Consider a news aggregation platform InversePlank that relies on a hybrid approach to customize its content. Qualitative data reveals that users often express a preference for receiving more science-related news articles. This insight, gained with session replays and user surveys along with quantitative data about user retention rated demonstrates the user's engagement pattern.
Qualitative analysis excels in revealing the 'why' behind user behavior - we know that. (I hope you do too aif you’re this far into the article). Quantitative analytics serves as the compass that guides product strategy. When it is combined with qualitative data, it creates a more comprehensive and well-informed approach to product analytics.
Quantitative analysis provides a broader perspective on Product Performance Evaluation. It offers insights into key metrics such as user engagement and retention rates that tell a story about what's working and what's not. This approach provides a big-picture view of your product's overall health.
Forced example #4: Imagine you're responsible for a language learning app - TrioLango. The combination of session replays and user feedback exposes that users are frequently experiencing app crashes when they attempt to record their spoken responses. This qualitative data highlights a specific pain point in the user experience.
However, to fully understand the extent of this issue and its impact is when the quantitative analysis kicks in. Quantitative analysis might reveal that the increased rate of app crashes correlates with a decline in user engagement and a higher churn rate. With both quantitative and qualitative insights available, you can now prioritize resolving the technical issues while addressing the user needs. Talk about a holistic solution, eh?
One more thing that really becomes potent with a hybrid approach? User Flow Optimization. Quantitative analysis, often through the use of conversion funnels, points to the exact steps where users tend to get lost or drop off. This allows product teams to make targeted improvements to the user flow, ensuring a seamless journey.
Forced example #5: Imagine managing an e-commerce website - Sundaland. Now imagine that the qualitative feedback and session replays bring to light a recurring user issue during the checkout process - users struggle to apply discount codes. This qualitative data points to a specific aspect of the user experience that requires improvement. By integrating quantitative insights, you can determine precisely where users abandon their carts due to this issue, revealing the quantitative impact of the problem - the drop-offs and loss in revenue.
The result of insights gained from a hybrid approach in this case would be a user-centric optimization of the checkout process that aligns with the 'why' behind the issue, improving user retention and increasing conversion rates.
In essence, it's not just a matter of "qualitative vs. quantitative" anymore; it's the power of "qualitative and quantitative” - working in tandem. This dynamic alliance allows you to understand potential of your product and endure its ongoing success. The ideal mix, when utilized correctly, offers a balanced, comprehensive solution to the daily challenges in the life of a product manager.
The “Ideal Mix” just sounds so cool that only the coolest of the coolest platforms might be able to offer it to you. Maybe a pioneer in the world of product analytics perhaps?
In this era of bustling technologies, a robust understanding of user behavior is essential for developers, marketers, and product managers. Traditionally, this has been approached through two distinct lenses: qualitative vs quantitative data. However, like I indicated just two paragraphs ago, recent developments have shown that a hybrid approach that combines both types of analysis is far more powerful and insightful. Enter our very own - Zipy, a platform that seamlessly combines qualitative and quantitative data to provide a holistic view of user behavior and the best of product analytics.
So, how can these two seemingly disparate data types coexist? The answer is that - they can not only coexist, but also create a powerful synergy when used together. Below I’ll demonstrate how Zipy has managed to create said synergy. So, if you don’t want to be sold to, here’s your trigger warning -
“The following list aims to show how Zipy is an awesome platform that offers qualitative and quantitative analytics together to form a comprehensive solution. If you don’t like feeling that you’re being sold to, we’d recommend skipping to the end of this article.”
Comprehensive insights: Zipy offers a single, unified dashboard that combines qualitative and quantitative data. This means you don't need to switch between different tools or platforms to gain a complete view of user behavior. By having the quantitative data and qualitative data through session replays in one place, Zipy makes it easier to analyze and correlate data points effectively.
Cohesive user journey mapping: User journeys in Zipy provide a unified perspective by showcasing both the qualitative and quantitative aspects of user interactions. For instance, you can view a session recording of a user navigating your app while simultaneously analyzing quantitative metrics related to their actions. This approach increases your understanding of the user experiences, as you can directly observe user pain points and assess their impact.
Heatmaps: Heatmaps are an excellent example of how Zipy marries qualitative and quantitative data. These visual representations allow you to track where users click, tap, and scroll (quantitative data) while also understanding the significance of these actions through session recordings (qualitative data). This ensures that every interaction is analyzed really well.
Contextual event tracking: Quantitative event tracking is enhanced by qualitative insights in Zipy. For example, when you receive alerts about unusual user behavior or spikes in error rates (quantitative data), you can dive deeper into session recordings to understand the context and reasons behind these anomalies (qualitative data).
Correlation of data points: Zipy allows you to correlate data points between qualitative and quantitative data sets. This means you can assess how specific user behaviors (qualitative data) influence key performance metrics (quantitative data). For example, you can study session recordings to identify why certain users abandon a shopping cart and then evaluate the quantitative impact on your conversion rate.
Contextualized issue resolution: Issues detected through quantitative data, such as an increase in product slowness, can be addressed with more context when combined with qualitative data. Session recordings offer insights into how users experience these issues and their resulting frustrations. This contextual information empowers developers to resolve problems more effectively.
User segmentation with context: User segmentation within Zipy is enriched by qualitative data. When you categorize users into distinct groups based on their behavior, you gain insights that extend beyond mere numerical attributes. These segments are humanized by the context provided by qualitative data, enabling you to create personalized experiences that resonate with each segment.
Contextual search: Zipy's powerful search capabilities allow you to explore your data by conducting searches based on specific keywords or actions. You can filter and find session recordings, or quantitative data with ease. This contextual search unifies your entire data pool, enhancing the ability to track and resolve issues quickly.
Did anyone say - “The Future of Product Analytics?” Maybe. Or maybe it is the present reality.
I could start this conclusion with “In conclusion,” but I won’t. Instead, here’s something a little different -
As we've journeyed through this exploration of the intricate world of analytics, I hope you’ve seen the benefits of the amalgamation of these two (qualitative vs quantitative) seemingly different kinds of analytics. And at the forefront of this transformative paradigm shift stands Zipy, along with a handful of others. The new age products need a holistic approach that seamlessly marries the 'why' and 'what' of user behavior, resulting in a comprehensive solution that empowers you to make informed decisions. It is no longer a matter of choosing one data type over the other. It's about harnessing the power of both.
The past of product analytics is marked by divisive debates. However, future of product analytics belongs to those who choose to embrace the combination of qualitative and quantitative analysis. In a world that demands a comprehensive understanding of user behavior, Zipy emerges as the bridge that connects these two data types. Gone are the days of the limitations of traditional approaches. This new approach is the testament to our ability to evolve, adapt, and harness the full potential of the data at our fingertips.
If you have any more questions feel free to reach out to us at email@example.com.
Qualitative Analysis: Qualitative analysis deals with non-numeric data and focuses on understanding the human experience. It involves descriptive language and explores motivations, opinions, and emotions behind actions.Quantitative Analysis: Quantitative analysis deals with numeric data and provides a more numbers-driven perspective. It involves measuring and assigning numerical values to information, allowing for statistical analysis.
Quantitative Metrics: These metrics involve numerical data and can be measured objectively. Examples include user sign-ups, conversion rates, and user engagement. They provide a quantitative, statistical perspective on product performance.Qualitative Metrics: These metrics involve non-numeric data and focus on subjective insights. Examples include user feedback, sentiments, and opinions. They provide a deeper understanding of user experiences and motivations.
Qualitative Examples: Examples of qualitative analysis include understanding why users choose certain features, exploring user opinions on a product's UI, and gathering insights from user interviews or feedback.Quantitative Examples: Examples of quantitative analysis include measuring user engagement through metrics like page views, analyzing conversion funnels to track user journeys, and conducting A/B testing to compare the effectiveness of different product versions.
Qualitative Data: Qualitative data is non-numeric and focuses on descriptive information, opinions, and motivations. It is often obtained through methods like user interviews, feedback, and usability testing.Quantitative Data: Quantitative data is numeric and involves measurable information. It includes metrics such as user actions, conversion rates, and engagement statistics. It is obtained through tools like analytics platforms and statistical analysis.
Zipy provides you with full customer visibility without multiple back and forths between Customers, Customer Support and your Engineering teams.