Inside Pledg’s autonomous customer feedback loop
How Pledg keeps every team in sync with customer feedback with an always-on customer intelligence that turns surveys and support conversations into team-ready actions—delivered instantly in Slack.
“The biggest unlock wasn’t just insights—it was speed and distribution. NEXT AI turns customer feedback into updates our teams actually see and act on, automatically.”
– Adrien Bonhomme, Chief Product Officer
Results at a glance
Faster feedback loops: Customer signals move from “buried in tools” to “in the right team’s hands” immediately.
Less manual synthesis: No more recurring reporting cycles—insights are continuously compiled and shipped automatically.
Fewer mis-bets: Teams pressure-test priorities with real customer evidence (themes + volume + verbatims).
Company-wide alignment: A shared intelligence layer distributed to each team where they already work: Slack.
Inside Pledg's rollout
Objective:
Pledg built an always-on system of intelligence for qualitative customer data that:
Unifies surveys and support data into one governed layer
Understands what’s driving outcomes with AI (ranked themes, quantified drivers, supporting quotes)
Activates insights directly into day-to-day execution via Slack DMs and channels, autonomously
Connected sources:
Survey responses
Support tickets & support conversations
Online reviews
Activation:
Automated Slack updates routed to the right channels/owners by team and topic
Governed customer intelligence layer accessible for ad hoc questions
Signature workflows
1) Always-on “feedback distribution” to Slack (from feedback to action)
Goal: Eliminate insight lag by delivering what matters to the right teams—without manual reporting.
How it runs: NEXT continuously processes incoming surveys and support data, clusters the themes, quantifies what’s growing/shrinking, and posts team-ready updates into Slack channels and DMs.
Outcome: Faster decisions, less coordination overhead, and fewer missed signals—because insights show up where work happens.
2) Product + Support loop (stop recurring issues at the source)
Goal: Turn repeated tickets and survey complaints into product fixes—not just support responses.
How it runs: Teams ask questions like: “What are the top recurring reasons customers contact support this month?”, “Which issues are rising fastest—and what’s the evidence?”, or “What changed after the last release last month?”. NEXT AI returns a structured answer with ranked drivers, counts, and representative verbatims—so Product can prioritize fixes with confidence.
Outcome: Less rework, fewer repeated issues, and a tighter loop between what customers experience and what gets built.
3) Decision validation (reduce mis-bets before shipping)
Goal: Pressure-test roadmap bets before investing time and rollout effort.
How it runs: Teams validate hypotheses like “Will the Enterprise customer segment benefit from this feature—and why?” NEXT surfaces supporting and counter-signals with volume + quotes.
Outcome: Better prioritization, fewer wrong bets, and faster iteration with evidence.
Why NEXT AI (and why not just ChatGPT)
Pledg wasn't looking for prompting an AI with snippets. Pledg needed an always-on layer that:
Connects to real sources continuously (surveys + support data), not copy/paste prompts
Combines qual + quant (ranked themes with counts + real customer quotes)
Standardizes workflows teams repeat every week (distribution, triage, validation)
Ships insights automatically into Slack DMs and channels—so people act without chasing
Creates one shared intelligence layer across teams that people could come back to—without manual consolidation
Leadership lessons from Pledg
Distribution beats documentation: Insights only matter if they reach owners in time to act.
Push > pull : The fastest teams don’t “go looking for insights”—insights find them in their flow of work.
Evidence ends debates : Counts + verbatims turn opinions into decisions.
Speed compounds : Shorter feedback loops reduce mis-bets and unlock higher roadmap velocity.
“Speed compounds. Shorter customer feedback loops mean fewer mis-bets, less rework, and a roadmap that moves faster with confidence.”
– Adrien Bonhomme, Chief Product Officer