The more senior your customers are in their profession, the harder it is to get them to talk to your UX researchers. Fortunately, these customers are already communicating with your company via other avenues and constantly feed insights to your sales team, customer-success managers, and marketing specialists.
Businesses receive a lot of exploratory feedback through all these channels: customers report their problems and blockers, make requests, ask questions about sales demos, and express their doubts during business-development qualification calls. All of this is valuable information, but without a robust system in place, businesses fail to capture and use it effectively.
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To ensure that you can capitalize on the feedback that you receive from customers, both implicitly and explicitly, you can create a semi-automatic system for feedback processing that requires minimal effort from all departments. Your product team can track the wants and needs that your prospects and customers express in real time. Let’s look at how you can do this using some common Web applications.
Part 1: The Slack Report
When your colleagues on customer-facing teams encounter customers’ problems, you’ll typically receive a report from a business-development representative, a sales rep, or a customer-success manager, detailing either the difficulties a customer is encountering within a particular scenario or a functionality request from a prospect. That colleague could then go to a customer-feedback channel in Slack and quickly fill in a form similar to that shown in Figure 1.
Figure 1—Feedback form in Slack
What this feedback form should include would vary depending on your organization’s specific requirements. However, when creating your own form, it’s important to remember that context is key—even if it sometimes complicates things.
Accepting direct requests and recommendations from customers can create a situation in which customers suggest different means of solving what are ultimately similar problems. If you took action on every specific request from your customers, your product team would end up chasing multiple targets unnecessarily.
However, just writing down the problems won’t help your team to understand what your customers want from your product or what the ideal scenario would be for them.
That’s why capturing this level of detail about what a customer is proposing as a solution is extremely useful. Not only does it help you to troubleshoot existing problems but these solutions can provide an additional source of inspiration for your UX designers.
I recommend your adding a severity score. This helps you to assess problems and prioritize projects to address them according to the scale and impact of the problem users are encountering.
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.
Figure 2—Generating a spreadsheet for your feedback
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.
Figure 3—Workflow from Google Sheets to your feedback database
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.
Figure 4—A discussion on Slack
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.
Figure 5—Workflow
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.
Figure 6—Feedback 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.
Figure 7—Workstreams
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.
Figure 8—Tagging your insights
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.
Aleksander leads user research at Juro, the creator of an all-in-one, contract-automation platform for fast-growing businesses that helps visionary legal counsel and their clients to agree on and manage contracts using a single unified workspace. He has created a workflow for assembling product feedback and supporting evidence-based decision making. He has previously held a variety of senior user research roles. Aleksander studied psychology at Lomonosov Moscow State University and was a PhD student in the Russian-British Behavioral Genetics Lab, at the Psychological Institute of the Russian Academy of Education. He delivers university lectures on UX research methodology. Read More
