Inside Action’s customer-led retail engine
Always-on customer intelligence that turns omnichannel feedback into ranked drivers and next-best actions—by store, region, and journey.
“At our scale, CX isn’t a dashboard—it’s execution. NEXT AI helps us turn customer feedback into ranked drivers and store-ready actions in minutes, so teams fix what matters and protect loyalty.”
– Sidhi Elprana, Lead Customer Experience
Results at a glance
Weeks turned into minutes: Teams moved from manual synthesis to self-serve answers backed by counts and real customer verbatims.
Hypothesis answers in <10 minutes: Teams validate initiatives with evidence—fast enough to keep momentum without guessing.
Expanded scope & adoption: From a narrow start to a broader intelligence layer used across CX, Retail Ops, Digital Product, and Marketing.
Operational impact: Insights flow into execution—store playbooks, regional priorities, and digital backlog items that drive measurable improvements.
Inside Action's rollout
Action built an always-on system of intelligence for qualitative customer data that:
Unifies customer voice across channels into one governed layer
Understands what’s driving outcomes with AI (ranked drivers, quantified themes, supporting quotes)
Activates insights into day-to-day execution (especially store/ops playbooks and product backlog/roadmap)
Connected sources:
Mopinion (digital experience surveys)
Q&A Retail (in-store CX surveys)
Uberall (local reviews incl. Google Maps / local listings)
SAP (support tickets / support data)
App stores (app reviews)
Signature workflows
1) Research topics (from questions to decisions)
Goal: Help teams move from “we see a metric moving” to “we know why, and what to do next,” across CX, loyalty, and store performance.
How it runs: Teams pull in customer feedback and business context (store/region performance, category metrics, even a P&L upload) and ask questions like:
“Why did Category X drop 5% this month?”
“What’s driving loyalty in Region Y?”
“Where are we losing customers in-store vs online, and why?”
NEXT AI returns a structured answer with ranked drivers, counts, and customer quotes, cut by store, region, journey, and segment, plus a clear “fix-first” shortlist.
Outcome: Faster diagnosis, better prioritization across teams, and more confident actions that protect revenue, satisfaction, and loyalty.
2) Validation hypothesis (reduce misplaced bets)
Goal: Pressure-test initiatives before investing time, budget, and rollout effort.
How it runs: Teams turn assumptions into testable hypotheses (store layout, signage, process changes, product choices, messaging) and ask: “Is this actually what customers want, and for whom?”
In under 10 mins, NEXT AI responds with evidence—supporting signals, counter-signals, and segment differences—and counts + verbatims to justify the conclusion.
Outcome: Fewer costly mis-bets, faster iteration, and initiatives aligned with real customer expectations.
3) Compare stores, time periods, etc (scale what works)
Goal: Help everyone operate at the speed and quality of the best stores and experiences.
How it runs: Teams compare stores vs stores, regions vs regions, and time periods to surface what top performers do differently—and what’s creating friction elsewhere. NEXT AI highlights the delta in drivers (what’s unique, what’s universal) and produces actionable guidance: what to replicate, what to fix first, and where to focus next.
Outcome: More consistent execution across the network, faster rollout of winning practices, and tighter feedback loops across markets.
Why NEXT AI (and why not just ChatGPT)
Action’s teams needed more than an assistant that answers from pasted context. NEXT AI is purpose-built to:
Connect to real customer sources continuously (not copy/paste prompts)
Offer maximum evidence/feedback coverage (not just a slice limited to LLM context window)
Combine qual + quant: ranked drivers with counts and real verbatims
Enable comparisons at scale across stores, regions, journeys, and time
Push outputs into the flow of work: store playbooks and product backlog/roadmaps
Meet enterprise expectations for governance and privacy across demanding European markets
Leadership lessons from Action
Execution beats measurement: CX becomes a competitive advantage when it drives decisions and owner-assigned actions—not reports.
Speed compounds: Shorter learning loops (minutes, not weeks) let lean teams move faster without lowering the bar for quality.
Evidence builds alignment: Counts + verbatims reduce debates and help teams converge on what matters across functions and markets.
Scale what works: Comparing top-performing stores and journeys is one of the fastest paths to network-wide improvement.
“The biggest shift is speed and confidence: we went from weeks of analysis to answers in minutes. NEXT AI brings real customer evidence—counts and verbatims—into our backlog and roadmap, so we build what customers actually need and avoid costly mis-bets.”
– Head of Digital Product
Towards a more efficient, personalized, and delightful retail experience
As AI and agentic workflows become part of how retailers operate and serve customers, Action is well positioned to use NEXT AI as a customer context layer—strengthening differentiation and leadership at scale.