Making Better Product Management Decisions with Customer Feedback Analytics

While companies become increasingly both customer-centric and product-centric, PMs are still sitting in a gold mine of underused customer knowledge. How can we leverage customer feedback to make better product decisions?

As Product Managers, we make decisions every day. It’s the core of our job. For every decision we make, it’s important to have a clear problem and objective (what do we want to achieve?), defined success criteria, and a good understanding of the context of that decision.

Understanding the context is probably where we should spend the majority of our time as Product Managers, and that's why Product Discovery has become such a hot topic in the industry nowadays.

With that in mind, Product Management professionals are using different techniques & sources of data to support their understanding of the context and mitigate as many risks as possible, especially for harder-to-make decisions (the ones that will have a higher impact on our problem, metrics, and objectives). Companies can, for example, look at market trends & industry reports, competitors' benchmarks, financial analyses, product analytics data, customer research & interviews, customer feedback, and much more.

Customer-related data has been trending and becoming more important for companies, especially as the amount of feedback generated grows exponentially (with public review websites - like G2, Capterra, Amazon, etc., social media, NPS surveys, customer service data, interviews, and more) and companies become increasingly both customer-centric and product-centric, thus more concerned about using customer perception as a key source for product prioritization and innovation.

And that is where things start to become more interesting.

More than a decade ago, companies like Amplitude and Mixpanel revolutionized how we use customer behavior to make product decisions, creating a category called Product Analytics. Today, these tools are a must-have in every Product Manager’s tech stack. When we talk about customer perception, though, things get different, and we are still in the stone age: PMs are sitting in a gold mine of customer knowledge that we are underusing.

Customers' inputs that go beyond numbers - qualitative feedback - come from all sorts of sources, from support tickets, win/lost reasons, and NPS surveys to reviews and research, and it is impossible to read every customer feedback or talk to the majority of your customers.

What happens then is that Product Managers significantly slow down their capacity to improve product discovery and decision-making velocity, simply because they don’t have a way of centralizing and analyzing all that information.

So the question is: how can we leverage customer feedback to make better product decisions? Enters Feedback Analytics.

Why Feedback Analytics for Product teams

After interviewing almost 100 product professionals from different organizations, we at confirmed that the #1 pain for the majority of PMs is categorizing and analyzing customer feedback, and we found a great opportunity to facilitate how PMs can leverage customer opinions to make better and faster decisions, even in more complex scenarios.

In a context where qualitative data from feedback is increasingly growing - just like quantitative behavioral data did in the past - Product teams will soon need to add to their basic tech stack something we call Feedback Analytics. Feedback Analytics solutions help teams process different types of customer feedback from multiple sources to quantify opinions and generate insights about their products.

That’s exactly what Birdie does: we are a . Our platform was built by Product Managers, to Product Managers, and combines leading-edge technology (AI & NLP) and human expertise to help companies create effective product strategies.

But you don’t need a tool like ours to start leveraging customer feedback to make better product decisions. Based on our research and experience, you can start simple and do it with 3 key steps:

1. Create a Feedback River

A Feedback River, according to Sachin Rekhi (Reforge’s co-founder), is a single source of truth for multiple sources of customer feedback. In the beginning, this central repository could be a Slack channel, Google Sheets, Jira, Trello, etc. The important thing here is to centralize feedback coming from support tickets, NPS surveys, discussion forums, win/lost reasons, survey forms, or any other relevant feedback source, to a single place, where the product team will be able to read, learn and share customer perceptions.

2. Establish a process to categorize feedback

Once you have a place to store all that feedback, you need to make sure that you’re not only reading but also categorizing the feedback correctly. If you don’t do that, you will still miss the big picture and let trending aspects, issues, or opportunities slip. With organized feedback, it's easier to generate new hypotheses, evaluate ideas and suggestions, prioritize features, and better select customers for interviews.

3. Share these findings with other areas

The organized learnings acquired thru customer feedback analysis are important for the whole organization. You will be able to, for instance, easily get some customer quotes and volumes to exemplify and justify why your team is making a specific decision. Having a way of sharing and discussing that information will increase collaboration, customer empathy, and creativity among teams, fostering a truly customer-centric and product-centric culture among other areas. Marketing and Product Marketing teams will be able to use the feedback to review key selling points and positioning, and CX will have a better understanding of customer satisfaction with other aspects of the product and user experience.

The best part of establishing a process like this is that you will see yourself spending less time trying to make decisions and moving from guesswork to fact-based conversations, which will also make it much easier to get the buy-in from other team members. Together with quantitative data analysis, customer feedback works pretty well to get stakeholders' buy-in and reduce the HiPPO (Highest Paid Person’s Opinion) problem.

Our solution allows you to consolidate customer feedback from multiple sources and uses Machine Learning and NLP to automate the process of categorizing and analyzing customer feedback, something that will help Product teams save countless hours of manual work while getting all the benefits discussed here.

If you’re interested in testing the tool for free, you can .