PM uses StoriesOnBoard, engineering uses Jira, but customer feedback is currently captured in a Google doc per visit. How do you organize & prioritize customer feedback?
This is what ProductBoard was built for. Allows you to aggregate all customer feedback and tie it back to specific feature requests, so you can quantify how often a feature is being asked for and how important it is to the people asking for it. Integrates directly with Jira. As a PM it's my favorite tool I discovered this year.
If you use Intercom, worth checking out Userfeed (https://www.userfeed.io/). I've never used it personally, but it looks like a combination of an old school Idea Exchange + more modern tools that make it easy to follow up with customers through Intercom as feature requests get prioritized.
We're using a kanban board in Notion to organize product feedback at Capiche right now—which perhaps is a bit basic, but works well. Essentially, whenever anyone emails
email@example.com with a feature request or reaches out here or on Twitter, we'll check Notion to see if that feature's been requested yet. If not, we'll add a new card, summarize the feature idea in the title, and add the requestor's email and comment in the description. If someone else requests that feature, we'll add 1 to a points field in the card to essentially vote that feature request up, and also log that new person's email and comment in the card.
Over time, the stuff people want most bubbles to the top if you sort the list by the points field, and we can then use the other kanban columns to plan out the features as we add them.
Another thing we've used is a Google Form for people to vote on a specific feature they want, which shares their votes automatically into Slack via a Zapier integration. We included the Google Form poll in emails via links that vote when you click on them for an easy way to get more focused feedback.
Notion with several boards and ready-made templates.
I am obviously biased but at Chattermill we built a tool exactly for this purpose. Not to just collate and organise, but also use machine learning to turn feedback to insight at scale. We integrate with all major sources of feedback (including APIs for custom ones) and create a customised model to understand trends and patterns in feedback.
As I mentioned in the other discussion, the best way to get feedback is a discussion with your customers. A Google docs can't replace a good conversation. Ideally, you should get multiple opinions from a couple of different customers. This helps you prioritize and refine ideas.
How open are you to feedback and how you follow up are important factors that can make or break your product. Some companies use a public Trello board, but I found that to be messy and off-brand. It can be a good solution if you're on a tight budget, though.
I'm working on a tool called FeedBear (https://www.feedbear.com) that helps companies manage feedback efficiently. Send me a DM on Twitter if I can help with anything feedback-related.
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Very cool, thanks for sharing! Curious since Chattermill is pulling feedback from so many sources and normalizing it: Do you find certain types of feedback are more likely to come in from specific channels versus others? E.g. are social feedback more likely to be very happy or very upset, versus email feedback being more constructive and specific on features people need? Have a suspicion that would be the case but would be fascinating to see if it plays out broadly from the data.
Definitely. NPS-type surveys, ideally via email are probably the most balanced but depends on questions asked. In-app / On-site surveys (eg. Hotjar, Usabilla etc) are good for specifics about a given page, but bad for overall experience insight. Social is very much really bad or really good and typically ~70% garbage in terms of real product insight. Reviews (eg. product reviews on e-commerce sites) are really good for product insight. Post-interaction feedback (CSAT) is good to understand customer service performance.