How Akeneo empowered every PM to talk to customers
Akeneo is a global leader in Product Experience Management solutions, helping brands and retailers deliver consistent and compelling product experiences across all channels. As Akeneo scaled its product lines and global footprint, feedback multiplied. Sales calls, support tickets, Slack threads, and research notes piled up—yet it was pretty challenging to have a clear and centralized view of what truly mattered. Valuable customer input was often fragmented, duplicated, or left unused, buried in various tools.
François Esch joined Akeneo after coaching agile at BlaBlaCar and the Banque de France. He brought a systemic view of product operations and a focus on enabling teams to work autonomously helped by Antoine Barbier who came from Adobe and TubeMogul, where he led product teams in fast-paced environments with high data complexity.
At Akeneo, they collaborate to support the company’s transformation: from centralized decision-making to empowered impact teams, from fragmented tools to unified workflows, and from gut-feel prioritization to AI-supported feedback loops.
Challenges
Akeneo faced three critical product operations challenges that were slowing product decision-making and discovery across the organization.
First, feedback was scattered across multiple sources—Slack, JIRA tickets, sales notes, and Dovetail interviews—but there was no structured process to collect, organize, or act on it. PMs, Designers and Engineers often had to hunt for context as insights were lost, or simplified into upvotes without background.
Second, the team’s tool stack was fragmented. Research lived in Dovetail, roadmaps and ideas in Jira and Notion, usage data in Amplitude, Metabase or Datadog. These tools didn’t talk to each other, forcing teams to manually bridge gaps and make decisions without a clear, unified view.
Third, discovery work was overly centralized. Engineers were rarely in direct contact with users, and Product Managers or Researchers had to shoulder all the responsibility for understanding customer problems. This slowed down cycles and left teams disconnected from the impact of their work.
Impact Highlights
- All discovery calls and customer quotes centralized, tagged, and searchable in Cycle
- Research fully integrated—replacing Dovetail and connecting feedback to Jira
- 100% of impact teams now talk to customers weekly, with Engineers leading many calls
- AI enhances visibility across 100+ feedback entries/week, significantly lowering the risk of missed insights
Use case: Centralizing customer input
Akeneo replaced fragmented feedback flows with a centralized system using Cycle. Feedback from Slack, calls, support tickets, and CRMs now lands in one place, where it’s structured and tagged with AI. PMs and Engineers no longer need to chase context. They get the full story, linked to specific feature request each impact team in their own inbox.
- AI helps surface duplicates and group related requests.
- All Impact Team Members can now access and review customer quotes directly.
- Feedback is verified and linked to team-owned areas in Cycle.
This shift eliminated the noise and made feedback usable across teams.
"Previously, we mainly upvoted Feature Requests labeled with customer tags, but often lacked the context needed to truly understand their needs. Now, thanks to Cycle and AI-driven processing, we can analyze and interpret feedback more effectively. The addition of a true “Customers” object, synced with our Salesforce database, is also a game-changer over our previous setup."
– François Esch - Product Ops
Use case: From tool fragmentation to an unified stack
Akeneo’s product organization was weighed down by a disconnected tool ecosystem. Research lived in Dovetail, often isolated from where decisions were being made. Feature ideas were tracked in Jira, with limited traceability back to real user needs.
This led to constant context-switching and manual effort to bridge gaps.
"All the teams were trying to do their own thing, but we didn’t have a common platform."
– François Esch - Product Ops
To address this, Akeneo brought all product customer feedback and research into Cycle. Research calls are now logged, transcribed, and tagged directly in Cycle. Feature requests are tied to real customer quotes and behaviors. Coupled with a data stack that includes Amplitude, Datadog, and Metabase, Impact Teams are now equipped to be more customer-focused than ever.
"In my ideal world, we’d have metrics broken down across more dimensions so we could act on them. That’s what we’re building towards—combining product signals and feedback to make faster, clearer decisions."
– Antoine Barbier - VP Product & Engineering
At Akeneo, Product Teams now rely on a more focused and powerful toolbox — combining a source of truth for qualitative insights with robust quantitative data — to support more confident, customer-driven decisions.
Use case: Centralized discovery for autonomous impact teams
Discovery used to be used to be mostly in the hands of User Researchers and PMs at Akeneo. Engineers were rarely involved, and product decisions often relied on secondhand information or assumptions. That changed with the rollout of impact teams.
Inspired by models like Amazon's, Akeneo shifted from matrix-style alignment to autonomous teams—each with clear ownership. These impact teams are now led by either PMs or Engineers, depending on the context, and are accountable for their own discovery, delivery, and outcomes.
"We told the teams, “I’m not going to tell you what to do—you should figure it out. You’ve got 18 months to create that impact. Start with the customer.”
– Antoine Barbier - VP Product & Engineering
Talking to users became a habit. Engineers now join or even lead customer calls weekly. Everyone captures and tags insights in Cycle, closing the loop between feedback and shipped work. The shift didn’t just increase speed—it built stronger ownership, empathy, and decision-making.
"Some team members are super comfortable talking to users, others less so—but it’s not mandatory. What truly matters is fostering a culture where discovery and understanding customer problems is everyone’s job."
– François Esch - Product Ops
With AI helping triage and summarize large volumes of feedback, teams are equipped to manage the increased flow. Discovery is part of the delivery engine.
