What are the common pitfalls and biases of relying too much on quantitative data?

Qualitative data uncovers valuable insights into our users' experiences, preferences, and expectations. This deeper level of understanding enables us to craft strategies that directly address customer needs and exceed expectations. By analyzing qualitative data, product managers are able to craft tailored solutions that resonate with the target audience, leading to increased customer satisfaction and product success, in addition to KPI’s like NPM and CSAT.

Collecting and getting qualitative data will also promote customer interviews and discovery. When things get busy, it’s often easy to skip this part — so when you take time to really prioritize it, it can really promote a stronger product discovery process.

“Quality is more important than quantity. One home run is much better than two doubles.” – Steve Jobs It is very important to deep dive into the ocean of data to find valuable pearls of insights. That is why the quality of the data is more important than the quantity of the data for a product manager. Qualitative data analysis is the most powerful tool for a product manager to understand the hidden truth behind user behavior, needs, and preferences. Qualitative data evoke the Emotional Quotient of a product manager to unravel the emotions, motivations, pain points, and expectations which is difficult with quantitative data. Empathy is the ultimate tool for a product manager to create a product that users love.

Qualitative data analysis plays a vital role in helping product managers understand the "why" and "how" behind user behavior, needs, and preferences. By uncovering qualitative insights and exploring the nuances of user experiences, product managers can generate ideas, test hypotheses, and validate solutions. The empathetic understanding gained through qualitative data analysis allows product managers to create products that effectively address user needs and deliver meaningful value.

On my team, we often say "data is perfect when the data is perfect" -- but the problem is that the data is rarely perfect. Often, we are too focused on measuring one thing, and can miss the broader ecosystem around that measurement. For example, if we report on the value of a new user, we may miss the additional value from other users they connect to. When using Quantitative data, make sure you understand the business questions you're facing, and exactly what your question your data is answering. If your question is "what would happen if we added 1 million users", using the data from your first 100 users may not be valid, as the audience quality may differ as you scale up.

Navigating unstructured and voluminous data demands intricate organization and interpretation skills. Reliance on judgment and intuition can introduce potential biases, clouding objective analysis. The subjective nature of qualitative data can be influenced by the user's context or the product manager's preconceptions. Addressing biases requires diversifying data sources and employing various analytical methods to ensure balanced insights.

When it comes to quantitative data, what has helped me make sense of it and arrive to meaningful insights, is laying the data points across the different stages of the user-journey. This has allowed me to better understand WHAT is happening, WHERE it's happening, and WHY it is happening, from the perspective of the user. Example: - If bounce back rate is high on the landing screen, we can make an assumption that our value prop is not clear. However, if the bounce back rate is at the point of conversion/payment flow, we can assume that the cause for it is pricing. Data is always great, but it's more useful when aligned with the user and its experience!

My favorite thing about good quantitative data? It’s a great neutralizer and even an equalizer. In a data driven company, quantitative data is an argument supporter — Can’t get alignment with stakeholders? Quantitative data Need to influence the team, customers or even leadership? Quantitative data. While it runs the risk of bias, done correctly? It sets direction and neutralizes what may, otherwise, be decisions driven by gut and agendas.

While data can be extremely useful, it's important to keep in mind that it only provides a retrospective view. Utilizing it effectively can help us make refinements and understand past performance.

if you think qualitative data can be biased, quantitative data is worse. When a customer is giving you their perspective, a good product manager will step back and listen for what they hear and don’t hear. Taking yourself out of it. In qualitative, you have to rely on user trends here and can’t get as stuck on single points— and using a single interview is harder to get buy in on. Quantitative data, on the other hand, is easily manipulated. It’s heavily influenced by the person who is using it to paint a picture. Decisions can be made by taking a single point of reference because it supports a stakeholder agenda and may veer the product. It’s really important to use it but from an objective, always testing place.

Analyzing quantitative metrics alone often leaves you guessing the underlying reasons behind user actions. For instance, if conversion rate drops at a particular point in the user journey, usage analytics would inform about the drop, but it wouldn't reveal the reasons why the user took that path. Recent advancements in qualitative feedback analytics quantify vast amounts of qualitative feedback with AI for a deeper understanding (in aggregate) of our customers' expectations for our product rather than inferring what should change with a best guess.

Combining the strengths of quantitative and qualitative data analysis allows us to gain a holistic perspective rather than a numbers-only approach. By understanding and quantifying the 'why' behind user behavior, we can make more informed decisions, optimize our strategies, and deliver products that truly satisfy our customers.

Useful discussion, thanks! I oft observe three challenges. It's quantitative, so it must be: 1. Accurate - the ground truth 2. The best representation of any topic, issue, or domain of concern 2. All that's needed - pretty much, for decision making

For a startup, it’s easy for rely only on quantitative data because you don’t have time to get to qualitative. Valuing one or the other more is dangerous because you may find that what you thought was the users problem; was really something else. Always make time to get a process in place for both — from the beginning.