If that sentence sounds reasonable to you, you’re in good company. Most enterprise retailers believe deploying your returns portal is the eCommerce equivalent of crossing the finish line. The label in the box is gone. Customers can self-serve. Your friends are waiting with a big self-painted “CONGRATS YOU MADE IT”-sign. Done.
The reality is different. Having a portal might be your first 5k. But the biggest savings – and the biggest CX wins – live in your city’s local half-marathon. However, contrary to marathon training, there’s no TikTok community that will tell you the right methodology for getting there. After setting up their portal, many retailers lack a structured process for discovering what’s actually broken, department by department, and solving it in the right order.
Meet the expert who’s been inside the machine
Thomas Wagener is our Product Owner for Returns at parcelLab. Not only has he been at the company for nearly six years, but he has also built the returns product from the ground up. Tom has been embedded in enterprise returns implementations with retailers like Dyson, Emma Mattress, Bestseller, and Conrad, across multiple markets and geographies. If you want to train for that returns marathon, Thomas is your ideal coach.
One pattern he sees constantly: companies approach him asking for a specific button or feature. His first question is always the same – “What problem are you trying to solve?” The answer usually reveals a completely different solution than the one they requested.
The real problem: Features first, questions never
Here’s how most companies approach returns optimization: They decide it’s time to improve things, list the features their return portal should have, configure them, launch, and close the project.
What’s missing from that sequence is the most valuable step: nobody sat down with each department to ask what their actual pain points are. Inbound logistics has different problems than customer service. Product teams have different data needs than the web team. Fraud has different priorities than CX. But these insights only surface through structured conversations – not through a requirements document.
When Tom asks stakeholders directly what they need, the answers are always more diverse and valuable than any feature list would suggest. Skip this step, and you solve the wrong problems, you miss the highest-ROI opportunities, and you leave departments silently absorbing costs that the returns portal could have eliminated.
The stakeholder audit: ask before you build
Tom’s methodology starts not with configuration but with a single question, posed to each department separately: “What is your biggest pain point around returns?”
Then you quantify, prioritize, and solve – largest financial impact first.
Why this matters becomes clear through a real example. One retailer explained that whenever a customer had a warranty issue, they called customer service, get told to send an email with photos and a description, and then wait for a response. Three touchpoints for one process.
“You had a warranty issue. You needed to call them. When you called them, they told you that you need to write us an email and send pictures and a description of what’s wrong. And then we will let you know how we proceed. So that’s three touch points for one process. Times five euros, whatever, right? We gather that in the first place. So you took two contact points out.”
That warranty flow was costing roughly €15 per case. But it was invisible to anyone not sitting in customer service. The portal consolidated it into one interaction. The optimization only happened because someone asked the right question.
And this isn’t a one-time exercise. Tom treats it as a recurring discovery loop. New pain points surface all the time as the business changes, as volumes shift, and as regulations evolve.
The four-department returns audit
What follows is a step-by-step playbook for discovering and solving your biggest returns pain points. Each section represents one department. For each, we’re covering what to ask, what you’ll typically find, a real use case, and the ROI signal.
Department 1: Inbound logistics and warehouse
What to ask your warehouse team: Where do you lose the most time processing returns? What manual steps could be eliminated? To plan better in the future, what information would you need in advance?
What you’ll typically find: Manual data entry is eating hours. Warehouse workers are typing in order numbers and item conditions by hand when barcode scanning with pre-populated portal data could handle it. There’s no advance visibility into incoming return volumes, which makes staffing optimization impossible. Routing is confused – damaged goods, open-box items, and resellable products all go to the same intake point instead of being pre-sorted.
The math on this is simple, and Tom has seen it play out at scale with a large global retailer.
“If you save five seconds per return, they could save three people. So those people are probably not the most expensive people, but it still might be 50K a year for three people. At least even in Poland. So the ROI is real.”
Barcode scanning with pre-populated data replaced manual entry. Five seconds saved per return. At the brand’s volumes, that could translate to three full-time warehouse positions – roughly €50,000 per year. An additional lever that benefits the brand is return volume forecasts which are typically available 24 hours ahead. This enables warehouse managers to shift staffing to match incoming loads.
Your ROI signal: If your warehouse team is manually entering return data, multiply five seconds by your daily return volume by your hourly labor cost. That’s your baseline savings – before any routing or forecasting optimization.
Department 2: Product and merchandising
What to ask your product team: What returns data do you currently receive? How granular is it? How long does it take to reach you? What would you do differently if you had better data?
What you’ll typically find: Return reasons are captured at the wrong level of granularity. “Too small” tells you almost nothing. “Too short,” “too tight,” or “wrong cut” tells you exactly what to fix. But most portals still use the same flat dropdown for shoes, electronics, and furniture – as if “too small” means the same thing for a pair of sneakers and a bookshelf.
The digital returns portal can ask follow-up questions that a paper form never could. “Too small” becomes “In what way? Is it short? Too tight? Wrong cut?” This feeds directly into product description optimization, sizing guide improvements, and potentially design changes. Category-specific questions matter enormously when finding out what the product team actually wants to know.
“Ask product-specific or category-specific questions. […] I would go to the people who work with the online store. What information will help you? How do you need that? So you can improve the descriptions to get returns down. Identify which are the most critical items and do short term advances.”
The timing dimension is equally critical. Tom mentions a large global brand, where returns data was taking half a year to reach product teams. That means corrective action on product descriptions or sizing guides lagged by two full collection cycles. If product teams are getting actionable return data in real time instead of six months later, they can adjust descriptions, add size recommendations, or flag problematic SKUs within the current season – not two seasons too late.
Your ROI signal: Ask your product team how long it takes to receive returns data today. Every month of delay is a month of preventable returns on products with known issues.
Department 3: Customer service
What to ask your CS team: What are the most common return-related requests you handle? How are they categorized? Which ones are repetitive and low-decision? Which ones require judgment?
What you’ll typically find: A high volume of repetitive, rule-based tasks: issuing labels, explaining return policies, confirming receipt. Those could easily be fully self-served through the portal. Warranty and claims cases that require multi-step back-and-forth can often be consolidated into a single portal interaction.
There’s a lack of segmentation: loyal customers and first-time buyers go through the exact same process. And during peak season, the approval queue becomes a bottleneck that can’t be adjusted dynamically.
Here’s an example from a parcelLab customer’s warranty flow that shows how dramatic the impact can be.
→ Before: the customer calls (touchpoint one, roughly €5-8), gets told to email photos (touchpoint two, roughly €5-8), and waits for a response (touchpoint three, roughly €5-8). Total: approximately €15-24 per warranty case. → After: the portal collects photos, description, and customer declaration in one step. It opens a pre-filled ticket for the CS agent, who has everything needed to make a decision immediately. Two touchpoints eliminated.
Then there’s the organizational complexity. At a large U.S. retailer, an internal conflict broke out between the fraud team and customer service over missing package claims. Fraud needed a signed declaration form. CS protested that the form would increase customer effort and drive up call volume. The platform resolved the conflict. The portal collects the declaration digitally, eliminating the phone call entirely and ensuring the fraud department gets what they need. CS gets fewer contacts. Both departments win.
There’s also a dynamic lever that most companies overlook: approval thresholds can be adjusted by season, risk level, and customer segment. Tom describes it plainly:
“We can segment and say, 50% need to go into approval and 50% do not. And if you want to flip that number to ‘nobody goes into approval because it’s peak season’, then you can just flip the number, right? That’s continuous optimization based on what season you’re in and what you want to do.”
Retailers achieve this level of flexibility by using a platform designed for it.
Department 4: Web, shop, CRM, and commercial teams
What to ask your CRM and commercial teams: How do you currently use post-purchase touchpoints? Are returns integrated into your loyalty or CRM strategy? Do you use return-to-store? Are you tracking which return experiences drive or kill repeat purchases?
What you’ll typically find: Returns exist as a separate silo, disconnected from the CRM and loyalty program. There’s no differentiated treatment based on customer value or history. Return-to-store isn’t offered or incentivized, missing a direct revenue rescue opportunity. At the same time, gift card or store credit options are underused, so there is no top-up incentive to keep revenue in-house.
“You want to make it convenient to the customer. […] If you have stores, you should tell them: Hey, drop it off in store – that’s how you can make revenue because people tend to spend more money if they’re getting to the store.”
In-store drop-off is a revenue lever that’s easy to miss. Redirecting returns to stores rescues revenue that would otherwise be pure loss.
Gift card returns with a top-up bonus – say, the return value plus 10% store credit – keep money in the ecosystem. This is a CRM play, not a logistics play. But it only works if the returns platform actually integrates with the CRM.
Segmentation logic follows the same principle: loyal members get faster refunds, no return fees, and no signature required for claims. First-time buyers or flagged accounts go through standard or enhanced verification. In simple terms, it’s the café owner who lets a regular customer pay tomorrow when they forget their wallet. That level of trust, at scale, is what CRM-integrated returns enable.
Your ROI signal: What percentage of your returns currently result in a full cash refund vs. store credit or exchange? Every percentage point shifted toward store credit or exchange is retained revenue.
Retailers who follow this methodology unlock a fundamentally different operating model.
Warehouse efficiency gains compound at volume. Seconds saved per return translate into FTEs. This scales with every market and warehouse you operate.
Customer service deflection is where the numbers get large fast. Warranty, claims and troubleshooting flows consolidated into self-service. At enterprise volumes, this could reach six figures annually.
Product improvement velocity accelerates dramatically. Return reason data reaches product teams in real time instead of months later – enabling in-season corrections instead of next-season fixes.
Revenue rescue becomes systematic. Return-to-store programs, gift card top-ups, and “keep the item” logic for cases when return shipping exceeds product value keep money in the ecosystem instead of watching it walk out.
Organizational clarity replaces fragmentation. You get one owner, one system, and one feedback loop. Departments stop optimizing in isolation and start building a shared returns infrastructure.
Two use cases show what becomes possible once the audit reveals the real opportunities.
At Emma Mattress, a customer could return a mattress for being “too hard.” Instead of processing a return – expensive, given the bulky item, special logistics, cleaning, and resale uncertainty – CS calls and offers a free topper. If the customer accepts, no return happens. The cost of a topper is a fraction of the cost of a mattress return. The revenue saved per case far exceeds the intervention cost. The most valuable optimization here isn’t faster returns. It’s fewer returns through smarter intervention.
At Dyson, damaged batteries cannot legally be shipped via standard parcel. Before integration, these cases were likely handled ad hoc or ignored entirely. Now the portal identifies damaged-battery returns and routes them automatically to specialist hazardous-goods carriers. That process exists because someone asked: “What edge cases are costing you the most?”
Where this goes next
The methodology is deliberately low-tech at the start. Begin with structured conversations across four departments. Ask one key question in each and prioritize the outcomes by financial impact. No new software required for step one.
The mindset it demands, though, is not low-effort. This is not a quarterly project. It’s a continuous optimization loop. The companies extracting the most value from their returns infrastructure are the ones who never stop asking, “What’s your biggest pain point?”
Tom sees the space moving toward automated, CRM-driven decision-making:
“The future – what I really believe – is not just about segmentation. It’s also gonna be about automatic decision for certain things. Customers will bring their CRM data in. […] There’s going to be way more automation triggered by the returns portal based on customer data.”
Customer data will trigger dynamic approval thresholds – auto-approve for green-light customers, escalate for flagged accounts. AI will validate damage photos, chatbots will handle end-to-end returns in natural language: the customer describes the issue, and a label arrives by email without ever visiting a portal. AI will play a key role in CX. The returns portal becomes invisible infrastructure – the interface between customer intent and operational fulfillment.
This vision might sound like Fiction to you. But the good news? The first step is the simplest. Sit down with your warehouse lead, your CS manager, your product team, and your CRM owner. Ask them one question. The answers will tell you where your money is going.
Your questions, answered
The returns optimization audit is a structured process where you interview each department that touches returns – warehouse, customer service, product, CRM – with one core question: “What’s your biggest pain point around returns?” You then quantify each pain point by financial impact and solve them in order of priority.
Each customer service contact costs approximately €5–8. A well-configured returns portal can eliminate two out of three touchpoints per warranty or claims case. At enterprise volumes, this translates to six-figure annual savings.
For representable ROI start with the simplest metric: time saved per return in your warehouse, multiply daily return volume by hourly labor cost. Then, layer in CS contact reduction, this could be €5–8 per contact, and revenue retained through store credit or exchange programs.
The returns optimization process should be owned by one person with cross-functional mandate, regardless of department. What matters is that they have the authority and willingness to drive conversations across logistics, CS, product, and CRM – and that they treat it as an ongoing responsibility, not a one-time project.
An often missed opportunity are granular return reason taxonomies that feed real-time data to product teams, dynamic approval thresholds adjustable by season and customer segment, return-to-store programs for revenue rescue and self-service troubleshooting flows that resolve issues without generating a return at all.
