In 2024, nearly three out of four French people purchased a second-hand product, up from 64% in 2023 according to the Novascope study—a rise of several percentage points in three years. The second-hand market is now worth around 7 billion euros in France, and the DIY sector is following the same trend as small appliances and gardening equipment. Recommerce is no longer a niche purchase: it is a deliberate economic choice for consumers and an opportunity for any retailer (Castorama, Leroy Merlin, and other market players are already positioning themselves). For a DIY retailer, second-hand goods are becoming a driver for foot traffic, margins, and brand loyalty. However, a returned product is more complex to process than a new one: condition and quality must be assessed, accessories may be missing, and factors like safety, seasonality, and pricing must be determined before the item is put back on sale through the right channel. The question is not whether second-hand is profitable, but how to industrialize it without increasing the workload for store teams. This is where AI plays a precise role, provided it remains a tool to support the process. Our guide to recommerce and the circular economy for retailers lays the foundation for this approach.
Why is second-hand DIY difficult to industrialize?
Second-hand DIY is difficult to industrialize because every returned product is unique: drills, lawnmowers, returned items, and accessories each have their own condition, value, and lifecycle. Without a shared process between the store, inventory, and e-commerce, returns lead to manual data entry and inconsistent decision-making from one point of sale to another.
A DIY retailer handles very diverse product families:
power tools, gardening equipment, carpentry items, building materials, returned products, display items, and accessories. Unlike clothing, a drill or a lawnmower combines residual usage value, safety risks, and potentially missing parts. Assessing this condition and quality requires a technical eye, and that perspective varies depending on the salesperson and the store.
The difficulty also lies in the origin of the products.
A return can come from an individual bringing a tool into the store, a trade-in for a voucher, or a customer return. Each channel feeds the same second-hand stock, but with highly inconsistent levels of information. Without a common framework, every return starts from scratch: manual entry of the product sheet, "gut feeling" estimates, and locally decided pricing. The product then remains stuck in the back room due to a lack of usable references for e-commerce. The real challenge is not volume; it is the decision-making chain between the counter, the stockroom, and online sales across the entire company.
Before launching a large-scale initiative, retailers must define their operational model: return, sorting, inspection, pricing, resale, and management.
Qualifying returned products: the first concrete contribution of AI
AI primarily helps with qualification: it guides the salesperson through the evaluation grid, standardizes criteria across stores, identifies missing information (accessories, references, serial numbers), and prepares a usable product sheet. The decision to accept or reject remains human, but it is supported by a unified framework.
Qualification is the stage where recommerce most often goes off track. Two salespeople looking at the same second-hand drill might reach two opposite verdicts. Here, AI acts as a co-pilot: based on the category and a few answers, it suggests the appropriate condition grid and asks the right questions, rather than letting everyone improvise.
Its primary benefit is consistency. The acceptance criteria for a reconditionable product become identical from one store to another, which is essential for managing performance across the network. AI also spots missing information: a drill presented without a charger, a lawnmower with an unknown year, or a returned product with damaged packaging. It flags the gap in the product sheet before the item hits the shelf with an incomplete description.
At the end of the process, the retailer obtains a clean product sheet: qualified condition, listed accessories, technical information, and the necessary disclaimers for resale. This reliable qualification is the starting point for everything else, as accurate pricing and rapid resale depend directly on it. It also secures the service provided to the end customer, who buys a second-hand product that is correctly described. The goal is not to multiply use cases, but to make this first building block reliable.
Repricing: setting the right price without leaving it to individual stores
AI-driven repricing recommends a price range based on the original price, product condition, age, seasonality, target margin, and expected resale velocity. The brand stays in control: the AI proposes, and business rules decide. This framework prevents inconsistent pricing from one store to another.
Pricing a trade-in is a multi-variable challenge. A price that is too high means the product sits in the back room; a price that is too low destroys the margin that second-hand sales are meant to generate. Letting each store decide on its own creates visible and detrimental discrepancies as soon as the inventory is listed online.
A relevant price recommendation factors in several criteria:
● the original reference price, as an anchor point;
● the actual condition of the product, based on its assessment;
● its age and the obsolescence of the product line;
● seasonality (a lawnmower doesn't sell for the same price in October as it does in April);
● the target margin set by the brand;
● the expected resale velocity for that category.
The key takeaway is the division of roles: the AI calculates a consistent and updatable range, but pricing rules remain driven by the brand. Commercial policy, margin floors, and discount tiers remain business decisions. The AI accelerates and standardizes execution; it does not replace pricing strategy.
Fast resale: the decisive lever for profitability
The profitability of recommerce depends on the time it takes to get items back on the market. The faster a traded-in product becomes an available SKU, published on the right channel and visible both in-store and online, the faster it generates revenue. This is the role of unified commerce: connecting trade-ins, unit inventory, and e-commerce without manual re-entry.
A traded-in product that waits three weeks for a listing and a price is a product that ties up space and capital. Every day saved between trade-in and shelf placement directly improves the system's return on investment. Rapid resale requires frictionless product creation, using the data already captured during the initial assessment.
Second-hand retail requires a unit-based inventory logic: each item is a unique SKU with its own condition, value, and history, unlike standardized new products. This granularity prevents allocation errors and extends the product's lifecycle. Each piece can then be published on the most relevant channel (aisle, dedicated corner, e-commerce site) and remain visible everywhere without double entry. The second-hand customer journey becomes as seamless as the new product journey, and the experience remains consistent both online and in-store.
To avoid manual re-entry and make traded-in products visible on the right channels, an omnichannel second-hand solution connects trade-ins, inventory, retail stores, and e-commerce. Openbravo Second-Hand manages each product as a unique SKU and displays the second-hand catalog online, leveraging standards already deployed in over 7,700 points of sale. Orisha's recommerce modules have already enabled the reuse of 3.7 million products, proving that the model scales effectively.
How can you avoid adding complexity for your teams?
To avoid burdening teams, second-hand operations must be integrated into existing tools (POS, inventory, e-commerce) rather than adding standalone software. A guided workflow that eliminates manual data entry, uses simple rules, and leverages AI support allows sales staff to process trade-ins and restock items as part of their standard routine.
Store teams have a legitimate fear of being saddled with yet another tool. A disconnected second-hand module forces them to re-enter data, juggle screens, and memorize fragmented rules. This achieves the exact opposite of the intended goal: trade-ins become a chore, and the initiative loses momentum.
The key is to integrate second-hand operations into the tools teams are already using. Orisha Commerce solutions for home and DIY retailers allow you to design these journeys with a business-first approach: trade-ins via POS, guided qualification, recommended pricing, unit-level inventory, and e-commerce publishing, all within a single chain.
Which KPIs should you track to manage profitability?
Five indicators are enough to manage recommerce profitability: trade-in rate, acceptance rate, time-to-resale, sell-through rate, and net second-hand margin. Tracked together, they reveal where the process is stalling, from the trade-in counter to the final sale.
- Trade-in rate > Share of customers bringing in an eligible product
- Acceptance rate > Trade-ins actually accepted after qualification
- Time-to-resale > Time elapsed between trade-in and availability on the shelf or online
- Sell-through rate > Share of traded-in products that are actually resold
- Net second-hand margin > Actual margin after sorting, inspection, and refurbishment
These five KPIs should be viewed as a chain. A high trade-in rate combined with a long time-to-resale indicates a bottleneck in qualification or reference creation.
Second-hand in the DIY sector is not primarily a CSR issue; it is a process project. Its profitability depends on the ability to standardize field decisions, from trade-in to resale. AI adds value when it simplifies these three moments (qualifying, repricing, and restocking) within a unified workflow, without imposing an extra tool on teams. All that remains is to define your operational model and choose the foundation that connects your POS, unit inventory, and e-commerce.
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FAQ
How does AI help determine the price of a second-hand product?
The AI recommends a price range by cross-referencing the reference new price, the product's qualified condition, its age, seasonality, the retailer's target margin, and the expected resale speed. It proposes a consistent and updatable value, but the retailer retains control over business rules: margin floors, markdowns, and commercial policy remain internal decisions. The same logic applies to the trade-in value offered to the customer, often paid out in vouchers or gift cards.
Which DIY products are suitable for the second-hand market?
Power tools (drills, screwdrivers, sanders), garden equipment like lawnmowers, carpentry items, returned products, and accessories that are complete or can be refurbished are well-suited for trade-in. The key is a reliable assessment of the product's condition and accessories: an incomplete or out-of-season product can still be relevant if it is correctly described and repriced. Compared to marketplaces and C2C platforms like Leboncoin, DIY retailers differentiate themselves through the selection, warranty, and traceability they provide for every second-hand product.
Why connect second-hand, inventory, and e-commerce?
Connecting these three components eliminates manual data entry, speeds up the resale process, and ensures the offer is visible on the right channels. Each traded-in product becomes a unique SKU available both in-store and online, without the need for double entry. This is what a unified commerce solution enables: turning dormant stock into revenue, at the right price and in the right place, as quickly as possible.
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