Part 2: The Automated Workflow
Once a colleague has completed and submitted a feedback form, the Slack workflow sends the results to Google Sheets, as Figure 2 shows.
Zapier also monitors this Google Sheet so, as soon as new feedback lands there, it automatically transfers the feedback to its final destination: your feedback database, a large table in your company’s Notion space, as shown in Figure 3.
The data from the feedback form are also shared automatically with the relevant Slack channel, enabling further discussions and comments among your colleagues. This is great for company-wide visibility and encouraging cross-department collaboration. Figure 4 shows an example of such a discussion.
Part 3: The Feedback Database
Using Daniel Pidcock’s term from atomic research, your feedback database is your facts base. However, you can add your colleagues’ interpretations in the solutions field, too. Thus, the information in your feedback database is a combination of facts and simple interpretations. It doesn’t just state that something has happened, but also draws out the reasons and the workflow behind it. Every piece of feedback is an atomic observation or a request. You can create a single space for all the incoming information, including data from usability tests, product research, and more.
To capture insights from all the available avenues for feedback, you can also extract details about why leads get disqualified and opportunities get lost. This help you to detect feature gaps and areas for improvement. You can automate all of this using Zapier, so the information feeds seamlessly from Salesforce into your database, as shown in Figure 5, eliminating the need for administration by your sales teams.
Your research team can then revisit the feedback database several times a week. Add hyperlinks to your customer-profile pages in the Notion table and attribute every relevant insight to a particular item of feedback. Your whole product team can contribute insights, from product managers to UX designers and developers. The feedback database provides a comprehensive overview of the feedback and the context behind it. Figure 6 shows what a fully tagged item of feedback looks like in the database.
The customer-profile page should already exist in a separate area of your company’s Notion. Plus, Zapier pulls other data directly from Salesforce— some basic company information such as the number of employees, the number of licenses in use, their activity data, and the monthly recurring revenue coming from a specific account.
Part 4: From Feedback to Insights
Next, the most valuable stage of feedback processing involves turning feedback into insights. The product-development team uses insights to communicate user needs and prioritize the development of features accordingly. But what does an insight look like?
An insight defines either a problem, a request, or a recommendation that unites several items of feedback. Of course, there might be thousands of items of feedback. When you have this much information, it is difficult for most businesses to prioritize effectively. Divide your feedback into around 300 actionable insights, then split these insights among four different teams.
Tag each insight by its type. For example, you can usually resolve a UX design problem or a wording issue rapidly through some straightforward fixes. Therefore, you could add such an issue to a stabilization sprint by your design team. However, a scenario problem or a feature request is much more complex, so should be the subject of further investigations and some serious planning.
For each insight, tag the team that is primarily responsible for completing the task under Workstream, as shown in Figure 7. For instance, your integration team would manage an insight whose focus is integrations, the growth team would be accountable for onboarding issues, and the technical-scaling team would be involved if the issue relates to performance issues or glitches. My company also has a primary-bets team that is responsible for our larger, annual objectives. The product-scaling team would usually manage anything that doesn’t fall into any of these other categories.
You can automatically add a lot of additional variables to each insight to help you prioritize your projects more efficiently, by setting up Notion formulas and rollups. The most common variables include the following:
- customer weight—The number of unique customers who have requested a specific feature or reported a specific issue
- average customer severity—A number that represents how urgently a customer needs a specific fix or feature.
- customer impact—A combination of the average customer severity, monthly recurring revenue (MRR), and various other factors
By adding these figures to the feedback-gathering system, you can identify key sales blockers and determine which fixes require the most urgent attention—simply by sorting the Insights table one way or another. Your product managers can firefight critical problems that require immediate resolution—just by sorting by average / maximum severity—and prioritize tasks efficiently. Figure 8 shows what this tagging looks like in practice.
Having detailed lists of all this readily available information helps your UX designers prepare for their investigations, ensuring that they’re not going into these discussions blind. They can group or filter insights by specific companies when setting up user interviews. The possibilities and advantages of using this single table are limitless. You can continuously seek new parameters to help build better dashboards for different purposes.
Part 5: The Problems and the Opportunities
Of course, no method of data organization is flawless. This method naturally requires some manual work on the part of your researchers to tag new feedback. Plus, a good memory is necessary to ensure that you don’t duplicate insights by mistake. It can also be difficult to manage old, now irrelevant insights because they refer to different versions of an ever-changing product. However, with the right resources to maintain the database, this system provides a great way of storing exploratory feedback and making it truly actionable and accessible.