Customer Story
E-commerce Retailer
Returns & care colleague automates eligibility checks, labels, and status messages—faster help for customers and fewer repetitive tasks for agents.
Location
Munich, Germany
Industry
Retail
52%
less manual work in care and returns.
German E-commerce Retailer — Returns & Support, Streamlined
Client confidentiality. Published with the client’s permission and privacy requirements. We don’t publish brand names, and non-essential identifiers may be generalized. The outcomes and workflow described are real and were reviewed with the client before publication. Additional evidence is available under NDA.
Executive summary
A mid-market German e-commerce brand in home & lifestyle used Cogniforce AI Workspace and a role-based Returns & Care Colleague to tame seasonal spikes, shrink the returns loop, and free its team from inbox grind. In the first 60 days, the company saw a ~52% reduction in manual work hours across customer care and returns operations, a ~35% shorter return-processing lead time, and ~25% fewer “Where is my order?” contacts. Service quality held steady while the team finally had time for proactive outreach and merchandising feedback.
Where they started
The business was growing fast, but the back office felt it more than the top line. Customer care juggled email, chat, and marketplace messages; returns trickled in with incomplete forms; and policies lived in scattered docs. Agents were copy-pasting RMA details, generating labels by hand, and chasing courier statuses across multiple portals. The Head of Operations summed it up: “We’re great with customers—when we can get to them.”
Why Cogniforce
Three needs were clear: put one source of truth behind every answer, automate the repetitive edges of returns and order queries, and prove control to marketplace partners and auditors. AI Workspace ingested policies, product catalogs, size guides, care instructions, carrier SLAs, marketplace rules, and warranty terms. On top of that, we introduced a time-saving Returns & Care Colleague that drafts responses, validates eligibility, prepares RMAs, and posts updates to the order system—always within guardrails that match the brand’s tone and compliance rules.
What we implemented
Rollout took three weeks. Week one organized knowledge and connected systems (shop platform, WMS, ticketing, carriers). Week two brought the Colleague in as a co-pilot—every message and RMA was human-approved. Week three enabled autonomous handling for low-risk requests: fit/size questions, return eligibility checks, label generation, and order-status lookups. Escalation paths stayed human for anything outside policy or tone, and every action left an audit trail.
Two small additions punched above their weight. A Return Reason Normalizer turned free-text reasons into clean categories the merchandising team could analyze. A Care & Fit Snippet Library ensured consistent, on-brand answers to the top 50 pre-purchase questions, pulling live details from the product catalog so agents didn’t have to hunt.
Day-to-day after launch
Mornings stopped starting with a wall of red tickets. The Colleague pre-triaged overnight volume, matching messages to orders, checking delivery scans, and offering ready-to-send drafts. For eligible returns, it verified policy windows, generated labels, created the RMA, and posted the warehouse note with SKU, condition code, and disposition suggestion. When a case fell outside the rules—late returns, damaged goods, partial bundles—it prepared a succinct summary and options for an agent to decide.
Product questions improved too. Instead of vague back-and-forth, the Colleague answered with the correct size chart, material composition, and care steps, all in the brand’s voice. The same snippets fed marketplace replies, keeping SLA performance green.
Measurement approach
We agreed on a simple, defensible method. Manual work hours were sampled for care and returns teams and cross-checked against system logs. Return-processing lead time measured from customer initiation to restock/refund. Contact deflection tracked the share of status inquiries that never reached an agent because the Colleague answered in the first interaction. Quality stayed in the loop via weekly reviews for tone, accuracy, and policy adherence.
Results after 60 days
The numbers settled quickly. Manual hours dropped by ~52% for the tasks in scope—labels, eligibility checks, status lookups, and standard policy replies. Return-processing lead time fell ~35%, largely because forms came in complete and warehouse notes were pre-filled. With ~25% fewer WISMO contacts, agents spent more time on complex cases and proactive goodwill gestures. CSAT held steady, with slightly fewer comments about “waiting for an answer.”
See how this maps to your stack
15-minute walkthrough of the exact playbooks, guardrails, and Colleagues—tailored to your tools and policies.
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The AI Colleague that saved the most time
Returns & Care Colleague became the quiet hero. It never misses a policy nuance, never uses yesterday’s size chart, and never forgets to attach the right form. It prepares clean drafts, generates labels, logs RMA metadata, and pings the warehouse with exactly what they need to know. Agents step in when judgment or empathy is required; otherwise, the routine flows through without friction.
Governance & data protection
Everything runs under least-privilege access with EU data residency. PII in documents is redacted from model training by default. Each action—label created, refund proposed, policy exception flagged—has an immutable log. Marketplace partners appreciated seeing SLA evidence and consistent reply language pulled from the Workspace rather than ad-hoc wording.
What changed for the team
The mood shifted from perpetual triage to steady cadence. Care leads now review quality samples and coach tone instead of firefighting. Returns staff spend more time on disposition accuracy and less on copy-pasting shipping IDs. Merchandising receives cleaner reasons for returns and trends they can act on.
Lessons we’ll keep
Tighten rules weekly, not quarterly—small adjustments to eligibility wording and tone had outsized effects. Treat snippets as living assets owned by brand and CX. Keep a human gate on any refund outside policy; the Colleague is fast, but exceptions define your margins.
What’s next
The team is piloting a Catalog Colleague that suggests improved size/fit copy when a SKU’s return rate spikes, and an Exchange-first Flow that offers smart exchanges before refunds, within policy. Both ride on the same guardrails and knowledge base already in place.
If you run operations or CX for a retail brand and want governed automation that respects your tone and policies, book a tailored demo. We’ll show you the exact playbooks we used here and how to adapt them to your stack.
