Why 70% of retail markdown decisions are still manual (and expensive)

In most chains, the scene has become almost ritual. Every Monday morning, a merchandising team opens its Excel file, cross-references last week's sales, looks at average stock age, glances at store performance, and decides: -30% on crewneck sweaters, -20% on boots, we'll see about the rest next week. The whole thing in under two hours, sometimes with a quick meeting, and often with that mix of experience, intuition and budget pressure that characterizes end-of-season decisions.

The problem? This scene, nearly identical from one retailer to the next, still accounts for an overwhelming majority of markdown decisions in the sector. According to field feedback cross-referenced with public retail-intelligence benchmarks, roughly 70% of markdown decisions are still made manually, from spreadsheets, on weekly cycles, with business rules frozen in time. And this, even as the industry now has tools capable of modeling demand at the SKU/store level in near real-time.

This gap between what retailers can do and what they actually do is becoming one of the sector's main invisible loss centers. In an environment where large retailers' average net margin caps around 3.2% (Deloitte Top 250 study), every margin point burned by a miscalibrated markdown weighs heavily. And the bill, contrary to common belief, is never just the discount granted.

Let's look in detail at why so many chains still steer their markdowns by hand, what it really costs them, and how some players are progressively shifting to a model where the decision becomes continuous, contextualized and automated.

Markdown: a strategic act disguised as operational routine

Before understanding why manual still dominates, you have to remember what a markdown decision really is. From a distance, it's a simple price adjustment. Up close, it's a permanent trade-off between four partly contradictory objectives:

1. Liquidate the stock before it loses all its value (end of season, obsolescence, fast fashion).
2. Preserve the margin by avoiding cutting prices on products that would have sold at full price with a bit more patience.
3. Maintain the chain's price image, without training customers to wait for sales to buy.
4. Free up cash and shelf space for new collections or high-rotation products.

Each of these objectives depends on variables that evolve continuously: actual sell velocity per store, weather, stock cover, product price elasticity, competitor actions, promotional calendar, upcoming stockouts on next collection, even in-store location performance. Per public sector benchmarks, 15 to 30% of a retailer's assortment gets marked down during a normal season — up to 50% in the most seasonal categories like fashion.

In other words, markdown isn't a one-off act: it's a continuous decision flow, spanning tens of thousands of SKU/store pairs and which should be recalculated at every significant environment shift. Yet the exact opposite happens in most organizations.

Why manual still resists in 2026

If the industry largely recognizes the limits of manual steering, why are 70% of decisions still made that way? Several structural reasons that stack up.

1. Fragmented, unreconciled data

This is reason number one. In an average chain, sales data lives in the POS, stock in the ERP, in-transit orders in the WMS, online sales in the e-commerce platform, supplier feedback in another tool, and store performance in often-disconnected BI dashboards. For a markdown decision to be truly optimal, you have to cross all these sources, in near real-time, at the right granularity.

In practice, merchandising teams spend 50 to 70% of their time rebuilding this unified view — copy-pasting from extracts, Excel processing, consistency checks — before even starting to think about the decision itself. Consequence: the brain time available for trade-off is ridiculously short, and the decision necessarily gets taken at an aggregated level (category, family, sometimes department), never at the SKU/store level where 80% of the value hides.

2. Business rules locked in heads (and Excel files)

In most chains, markdown know-how is concentrated in a handful of experienced people who "know" you don't mark down essentials before W+8, that you protect supplier X, that you never go past -40% on private label, or that a flagship store doesn't apply the same tiers as a suburban one.

These rules rarely exist in a formalized, system-usable way. They live in shared Excel files, committee PowerPoints, collective memory. Result: impossible to replay automatically, impossible to test, impossible to evolve at scale. And the day the person holding them leaves, part of the operational intelligence walks out the door.

3. The (legitimate) fear of blind automation

Many retail leaders have experienced, in the past, automatic pricing or markdown tools that went wrong. Overly aggressive markdowns on products still selling at full price. Recommendations disconnected from store reality. Black boxes impossible to challenge. Decisions that ignore commercial context (new collection launch, competitor promo, local event).

This wariness is perfectly legitimate. A miscalibrated markdown can't be undone: a discounted product stays discounted, and lost margin doesn't come back. Faced with this risk, many teams prefer to stay manual — slower, more imperfect, but perceived as more controllable.

4. Decision cycles that are too long

The fourth obstacle is probably the most underestimated. Even when retailers want to react fast, their internal cycles don't allow it. A weak signal detected on a Monday (rotation dropping in 12 stores on a product family) goes through a meeting Tuesday, merchandising validation Wednesday, ERP update Thursday, and store execution Friday or the following week. Five to ten days between signal and action.

In an environment where, as sector analysts remind us, some marketplaces reprice their products 20 to 40 times per day, this lag isn't just sub-optimal: it's structurally losing.

What manual steering really costs

When people talk about the cost of manual markdowns, the first image that comes to mind is "overly aggressive markdowns." That's the visible part. But the real cost hides elsewhere, in effects classic dashboards don't surface.

Cost #1: the unnecessary markdown

First source of invisible loss. A product that would have sold at full price, but that was marked down "to be safe" or "by calendar rule." At a season's scale, across hundreds of stores, that's millions of euros of gross margin going up in smoke, without anyone being able to precisely measure the shortfall — since, by construction, the sale still happened.

Cost #2: the too-late markdown

The opposite, equally expensive. A product that should have been marked down by week 4 but wasn't touched until week 8, because no one had time to look at that family in detail. Result: you now have to go to -50% instead of -20%, and even so, part will go to clearance then outlet. The longer you wait, the deeper the required discount — and the smaller the recovered margin.

Cost #3: the badly localized markdown

This is the most systematic cost. When you apply a uniform markdown across the network, you ignore the obvious fact that a product doesn't have the same dynamic in Lille, Nice and Strasbourg. Some stores could have sold the stock at full price. Others would have needed a deeper markdown from the start. The uniform markdown is almost always doubly losing: too strong where unnecessary, too weak where harder hits were needed.

Cost #4: non-reallocation

The "hidden hidden" cost. Before even marking down, many problems could have been solved with a simple inter-store reallocation. The sweater dragging in a Paris store sells very well in a Lyon store. Without unified visibility and automation, this reallocation never happens — and the sweater ends up marked down in both stores, when a transfer would have prevented it.

Cost #5: human and organizational cost

Never to forget. Manual steering mobilizes entire teams on very low-value tasks: re-entry, controls, processing, file alignment. These teams could be spending their time on assortment strategy, supplier negotiation, customer experience. Instead, they patch the information system's leaks.

When you add up these five lines, the net margin impact frequently exceeds 1 to 3 points for an average chain. On a retailer generating €500M in revenue at 3% net margin, that's the equivalent of €5 to €15M per year evaporating in sub-optimal decisions, never identified as such because diluted in the mass.

The shift in progress: from one-off decisions to continuous optimization

Part of the sector has started shifting. The most advanced retailers no longer think of markdown as an event ("we set this week's markdowns"), but as a continuous optimization flow, fed by data and executed at scale.

Concretely, this shift rests on three pillars.

First pillar: data unification. Before automating anything, you need one unified, reliable, up-to-date view that reconciles POS, ERP, e-commerce, supply chain and external signals (weather, calendar, competition). It's the non-negotiable condition. Without it, any automation attempt simply reproduces manual biases at greater scale.

Second pillar: formalizing business rules. The goal isn't to replace human know-how, but to make it executable. Constraints (margin floors, minimum durations before markdown, category exclusions, supplier-specific handling) must be codified into an engine that applies them systematically, while leaving the teams free to adjust.

Third pillar: an intelligent decision engine. Once data is clean and rules are formalized, models can compute, for each SKU/store pair, the optimal decision: mark down or not, how deep, when, with what complementary action (reallocation, replenishment, supplier return). And above all, this decision can be recalculated continuously, as actual sales confirm or invalidate the hypotheses.

The result isn't a black box. It's more like a copilot: it proposes, explains, flags anomalies, and the human validates or adjusts. The difference is that instead of thinking through 200 decisions per week, the team can handle 20,000 — focusing only on the cases that deserve a human trade-off.

The first market returns are unambiguous. Where manual steering leaves 15 to 30% of the assortment in markdown, retailers who've industrialized their decision-making report multi-point gains on recovery rate and sell-through, while freeing up considerable time for merchandising teams.

The Solya approach: deciding and executing at network scale

That's precisely the transition Solya supports at retailers who want to step out of fragmented steering. Rather than offering yet another isolated pricing tool, Solya is built as a retail decision and execution platform that bridges data to floor action.

The principle is simple. Solya connects to the chain's existing sources — POS, ERP, e-commerce, supply chain, internal tools — to rebuild a unified, usable view of the network. That data is then turned into operational signals (a product slowing in a store cluster, stock cover drifting, rotation deviating from forecast), then converted into actionable decisions: targeted markdowns, replenishments, inter-store reallocations, supplier actions.

The decision engine embeds the chain's business rules — simple ones (margin floors, minimum calendars) or more subtle ones (brand-specific handling, store cluster protection, logistics constraints). Teams keep the hand: they define guardrails, they arbitrate sensitive cases, they adjust strategy. Solya handles the rest, at scale, continuously, without breakage between decision and execution.

The goal isn't to remove human expertise. It's to free it from the repetitive mechanics so it can focus where it really creates value: assortment strategy, structural trade-offs, customer relationships. And it's to turn retail steering from a fragmented weekly exercise into continuous network-scale optimization.

The real question is no longer if, but when

Manual markdown steering isn't a weakness specific to certain chains: it's the historical state of the sector. But it's also a state whose costs are becoming less and less sustainable, as cycles shorten, margin pressure intensifies, and consumers grow more demanding on price-image consistency.

Retailers shifting today aren't doing it as a fad. They're doing it because they've measured, sometimes painfully, the cumulative cost of manual — and understood that the next generation of performance gains will no longer come from buying optimization or supplier negotiation, but from the quality and speed of operational decisions taken each week across the network.

The question is no longer whether intelligent automation of retail decisions will become standard. It's how many seasons a chain can still afford to wait before crossing over.

---

Want to measure what your current steering actually costs?

At Solya, we offer retail leadership teams a personalized 30-minute diagnostic to identify, on your own data, where the main margin reservoirs lost in your markdown, replenishment and reallocation decisions sit. No generic product pitch: a concrete conversation, grounded in your context, to assess the industrialization potential of your operational decisions.

👉 [Book your Solya diagnostic] — 30 minutes, by video, with one of our retail experts.

You'll walk away with a clear view of:

• The most likely loss sources in your current steering
• The first high-ROI use cases on your network
• The technical and organizational conditions to scale up