How do you collect, analyze and share your customer feedback?

This is a big question so bear with the long answer! If you are on the research team, product team, or product marketing team, it is your job to synthesize your research. And, it’s even better if you can consolidate it with, or tie it to, another team’s complementary work (this can be a cross-functional and collaborative effort). I’ll answer your question two parts, since you’ll need to do both of these things: Formatting: Tactically speaking, my personal style is to make a visual deck on the to ...Read More

Ah, the classic “feedback scavenger hunt”! We’ve all been there, trying to piece together customer insights from a labyrinth of surveys, spreadsheets, and snippets in Slack (though I do believe Slack can be used strategically). Keep in mind, all feedback is good feedback. It's better to have MORE than less! Of course, the goal is to turn that feedback chaos into a streamlined, insightful narrative. Here’s some best practices I would recommend to collect, analyze, and share customer feedback like ...Read More

Managing scattered customer feedback can indeed be challenging. Thinking about a single account/users to connect the feedback is needed often and systems may not be set up for that. For basics, explore and play with AI - even chatGPT and tools that allow chaining of feedback will enable summary analysis and insight extraction Some approaches that maybe helpful are - Customer Feedback Platforms: Qualtrics - has solutions based on use-cases such as NPS, VoC, CX and EX Salesforce - Often under-util ...Read More

There are multiple parts to this question so I'll try to address this in 3 parts. For collecting customer feedback I like to break it into two approaches, one is "always on customer listening" and the other is "project or program related research." 1. Always-on is something PMMs should be doing constantly by talking to sales and CS, listening to Gong calls, attending conferences, running customer surveys etc. 2. Project or program related is when you have a specific goal in mind. It could be dev ...Read More

As someone immersed in this problem space for the past six months, the struggle of collecting and synthesizing large and disparate data sets is very real, but there are some promising developments. When trained correctly, generative AI can help uncover key themes in different data types. From summarizing calls and documents to pulling out emergent themes in larger data sets, some exciting new technologies can help make sense of various data sets quicker than any human can. Full disclosure - Dove ...Read More