How do you balance user feedback and data analytics in product decisions?
Learn from the community’s knowledge. Experts are adding insights into this AI-powered collaborative article, and you could too.
This is a new type of article that we started with the help of AI, and experts are taking it forward by sharing their thoughts directly into each section.
If you’d like to contribute, request an invite by liking or reacting to this article. Learn more
— The LinkedIn Team
As a product manager, you need to make informed and strategic decisions that align with your product vision, goals, and user needs. But how do you find the right balance between user feedback and data analytics, two sources of valuable insights that can sometimes contradict or conflict with each other? In this article, we will explore some of the challenges and best practices of using user feedback and data analytics in product decisions, and how to leverage them to create better products.
User feedback: benefits and limitations
User feedback is the direct input from your customers or potential customers about their experiences, preferences, and expectations of your product. User feedback can help you understand the problems, needs, and motivations of your users, and validate your assumptions and hypotheses. User feedback can also help you generate ideas, prioritize features, and improve usability and satisfaction. However, user feedback also has some limitations that you need to be aware of. For example, user feedback can be biased, subjective, or influenced by external factors. User feedback can also be incomplete, inconsistent, or outdated, and may not reflect the actual behavior or outcomes of your users.
-
I like to think of user feedback like digging for gold. You're going to get a lot of dirt, so you have to inspect. Inspecting means being skeptical. You have to be a skeptic to ask your users, why do you feel that way? Why are you using the product in that way? What will that help you do? This will help you understand what they are actually trying to accomplish, which will help you make the decision of what to build. If you treat all user feedback as gold, then it'll be difficult to prioritize. But if you treat it like dirt, you'll find that bit of gold that moves the needle.
34
-
Diving deep on user feedback is both a critical ongoing pulse check of how users see your product over time, as well as seeing the "whole field" so you can analyze for opportunity spaces. When paired with data, company vision, you can start to see opportunities on that "field" and start to build out a strategic roadmap that you can fine tune. A strong UX research team and facilitator is most critical to help reduce risks like bias and leading the user, which can throw off the insights.
20
Data analytics: benefits and limitations
Data analytics is the process of collecting, measuring, and analyzing quantitative and qualitative data about your product and its users. Data analytics can help you track and evaluate the performance, impact, and value of your product, and identify patterns, trends, and opportunities for improvement. Data analytics can also help you test and compare different solutions, and optimize your product for efficiency and effectiveness. However, data analytics also has some limitations that you need to be aware of. For example, data analytics can be complex, costly, or time-consuming to collect, process, and interpret. Data analytics can also be misleading, inaccurate, or irrelevant, and may not capture the underlying causes or reasons of your users' behavior or outcomes.
-
The biggest challenge with data analytics is that it doesn't necessarily show you the full picture or an objective picture. Just like with ChatGPT, the output of data analytics is: 1) Dependent on how the question was asked or how the dashboard was implemented. According to Chamath Palihapitiya, the AI role of the future will be "prompt engineer". The same is true with Data Analytics - how was the dashboard wired? How was data aggregated / presented? Garbage in, garbage out. 2) The story the interpreter of the data is looking to tell. A former nuclear scientist once told me: "A scientist can tell any story they want off of the same data". Just like any data, including user research, analytics data is subject to interpretation.
18
-
PMs have to be concerned about when not to use data or rely on it. While data helps uncover stories for underlying issues but if over-emphasized too much, it leads to decision fatigue! When that happens, PMs believe that data must back all decisions. Sometimes, the alacrity with which the decision is made is more important than the right decision. The skill to figure out when not to rely on data comes with instinct, backed by experience and risk appetite.
4
How to balance user feedback and data analytics
When it comes to striking a balance between user feedback and data analytics in product decisions, there is no one-size-fits-all formula. However, there are some general principles and tips that can help you make the most of both sources of insights. To begin with, it is essential to define your product goals and metrics, as this will enable you to focus on the most relevant and meaningful feedback and data, while avoiding being overwhelmed or distracted by irrelevant or conflicting information. Additionally, it is important to use both qualitative and quantitative methods, as user feedback and data analytics are not mutually exclusive, but complementary. Qualitative methods, such as interviews, surveys, or user testing, can help you explore the why and how of your users' behavior and outcomes, while quantitative methods, such as analytics, experiments, or benchmarks, can help you measure the what and how much of your users' behavior and outcomes. Furthermore, it is necessary to triangulate and synthesize your insights, and translate them into actionable and testable insights that can inform your product decisions. Finally, you need to iterate and validate your product decisions, and measure their impact and value. By using user feedback and data analytics to monitor and evaluate your product changes, you can learn from your results and solicit and incorporate feedback and data from your stakeholders.
-
Starting with qualitative research helps you create an intuitive understanding of the problem, giving you both depth and empathy on the space. Bringing a quantitative approach as a second layer helps you ensure you are working on meaningful enough problems and create focus. This dual approach works better and faster than either approach independently (boil the ocean with quantitative only, or miss the forest for the trees with qualitative only)
37
-
To understand the big picture and set up the framework: I always start with the broadest possible user research - one that seeks to understand underlying problems, pain points, and alternative solutions people are currently using. This is foundational knowledge for any product developer. That understanding will then inform what data I want to see on the analytics dashboard and what OKRs I want to aim for. To optimize an existing product: User research that zooms into specific issues with the product, usability and features needs to be combined with tactical analytics understanding to get a full picture of what may be going on. Just like a doctor, you need to both ask the patient how they're feeling and look at the lab results.
13