Solving the Manual Feedback Problem: How Aptem Connected Customer Insights to Jira Product Discovery

For product teams already living in Jira and Jira Product Discovery (JPD), the hardest part of discovery usually isn't the backlog, it's everything that happens before an idea ever reaches it. This Q&A breaks down how Aptem, a vocational training and employability software provider, tackled that problem and connected real customer evidence directly to their existing JPD workflow.

What is the "manual feedback problem" in product management?

It's the time and effort product teams burn just collecting customer feedback before they can even start prioritising it. As Larence Caires put it: "the manual nature of having to speak to people to get feedback" is the core issue. Teams end up meeting-heavy — internal stakeholder meetings, customer calls, just to surface information that then has to be written up, copied between tools, and shared before it can influence a decision.

Why doesn't manual feedback collection scale?

Three reasons came up repeatedly in the discussion:

1. It's manual and meeting-heavy: every insight requires a conversation, then notes, then a write-up.
2. It's fragmented: feedback differs depending on who you ask. Sales wants the shiny new feature, support wants existing bugs fixed, and each source carries its own bias.
3. It's hard to find the right people: locating the right internal stakeholder or the right customer to validate an idea is, in itself, time-consuming.

As Larence Caires noted, the consequence is that "lots of product teams think they're making good decisions, but without this insight, you're kind of driving blind." Discovery becomes something teams do on the side of delivery, gets "put on a shelf," and teams end up reacting to the loudest or highest-paying customer rather than the most pressing problem.

What tools were teams using to manage feedback before automating it?

Before introducing Four/Four, Aptem's product team relied on a familiar stack: Notion and Confluence for documentation, spreadsheets to track conversations, meeting transcripts, and Jira (including Jira Product Discovery) to manage the eventual backlog of ideas. As Laurence explained, more mature product organisations might already have "a ProductBoard or a Jira product discovery where you can have ideas, get people internally voting on them, link them to customer calls", useful, but it doesn't remove the manual gathering step that happens upstream.

Any new tool has to work with what's already in place. "You're moving into an existing workflow, like an existing set of data, an existing way of working. You can't just sort of throw that away."

What changed once feedback was centralised?

According to Maddie Alberg, the shift was significant. Before, the team relied on internal colleagues to relay what customers wanted and interpret it accurately, introducing both delay and bias. After centralising feedback by product area, the team could see "what customers had fed back, what area of the product they were in, the trends as they emerged" much faster, with one objective source of truth that sales, customer success, and product could all work from.

How does Jira Product Discovery fit into this workflow?

This is where the story moves from "solving research" to "solving validation and prioritisation," in Laurence's words. Once feedback was centralised, Aptem still needed to connect that evidence to their existing JPD backlog; their ideas, their product strategy, the initiatives already in flight. Recreating those links manually would have just reintroduced the same problem.

The breakthrough was integration: every idea in Jira Product Discovery is automatically matched against the underlying customer insights. Practically, that means each JPD idea is enriched with the number of insights raised against it, how many distinct customers or accounts mentioned it, and any related pipeline or deal information, all without a product manager doing the matching by hand.

What does this look like day to day?

Each idea in Jira Product Discovery gets decorated with volume metrics: how often it comes up, how many customers raised it, and links back to the underlying evidence. Where customer feedback doesn't map to an existing idea, Four/Four surfaces it as a "suggested issue," which can be turned into a new JPD idea directly, closing the loop in both directions.

Maddie described her day-to-day this way: "Whatever large project we're working on at the time, I will go in and see what customers are saying... and there is no more manual collection needed... in real time, if a customer says something, I can now see it and analyze it and attach it to whatever work item is necessary."

Did this change how product managers actually work?

Yes, fewer more targeted meetings. As Larence put it: "We don't have PMs doing as many discovery meetings anymore. That first filter is done with data being pulled into the relevant initiative... we're having fewer, more targeted conversations" with the right people, about the right initiative. It also gave the team an evidence base for prioritisation decisions, rather than relying on whichever stakeholder spoke loudest.

Does this only benefit the product team?

While a product team typically buys the tool, the impact "explodes out into the rest of the company." Maddie pointed to the value for customer-facing teams in particular: having visibility into customer sentiment helps those teams trust that product is listening, and creates a two-way flow, feedback travels from customers to product, and roadmap context travels back out to sales and customer success so they can accurately represent what's coming.

Larence added a people-side benefit that's easy to overlook: better-informed product teams make cross-functional conversations easier. "It's easier to have that sales conversation because you know about that call... it's helped a lot in terms of healthy internal conflict being easier to navigate."

What's the key takeaway for teams already using Jira Product Discovery?

You don't need to replace your existing backlog or process. The most effective approach is to connect what you already have, your JPD ideas, your strategy, your initiatives to a continuously growing body of real customer evidence, so prioritisation is driven by data rather than whoever you happened to speak to last. Identifying who is asking for what may be one of the most valuable signals a product or go-to-market team can act on, for current customers, lost prospects, and churned accounts alike.

Four/Four scales and automates product and customer research, surfacing actionable insights from calls, tickets, and conversations directly into the tools product teams already use, including Jira and Jira Product Discovery.