Across our weekly markdown and promotions working sessions, one theme stands out: teams know something is off, but they don’t know when to act or how aggressive to be. The issues are timing, confidence, and consistency.
Scenario 1: “We waited too long and now the markdown has to be deeper.”
What we hear:
A category looks fine at a high level. Total sales are acceptable. Weeks later, size curves are broken, sell-through stalls, and the only lever left is a deep markdown.
The pain points
- Slow movers are identified late because reporting lags reality
- Markdown decisions become reactive and quickly erode margin
What changes with clarity
- Daily/weekly sell-through trend alerts surface risk earlier—well before velocity collapses
- Weeks of supply (WOS) thresholds highlight overexposure at style/size/store, not just category rollups
- Predictive risk signals (aging × WOS × forecast) prompt measured, earlier markdowns instead of last resort cuts
Outcome
Earlier intervention, fewer fire drills.
Impact
- Fewer 40–50% markdowns (more first hit, lighter actions)
- Maintained margins improve through earlier, surgical corrections
- Cleaner exits without flooding the floor or overburdening associates
Scenario 2: “We don’t know if it’s the price or the product.”
What we hear
A style underperforms in some stores but sells clean in others. Teams debate whether the issue is pricing, allocation, or local demand. Decisions stall.
The pain points
- Inconsistent store level visibility obscures where price is the real friction
- No clear signal on local price sensitivity, so chainwide actions become the default
What changes with clarity
- Side by side store comparisons of sell through, WOS, exposure, and price elasticity spotlight where the problem is price, not product
- Targeted micro tests (e.g., 30–50 test stores) replace chainwide markdowns, validating sensitivity before scaling
- Localized pricing aligns to value vs. premium shopper clusters without penalizing strong stores
Outcome
Pricing decisions become faster, more precise, and market right.
Impact
- Reduced unnecessary markdowns in high demand stores
- Better alignment of price to local demand profiles
- Higher full price sell-through where it still exists
Scenario 3: “Markdowns feel subjective and inconsistent.”
What we hear
Different merchants and regions make different calls on similar inventory. Without shared logic, forecasting and planning get harder.
The pain points
- Markdown logic lives in spreadsheets and tribal knowledge—hard to scale or explain
- Inconsistency undermines forecast accuracy and erodes trust
What changes with clarity
- Trusted, organization-wide metrics (sell-through, aging, exposure, forecast) become the common language
- Governed, explainable AI guardrails establish consistent triggers while preserving judgment
Outcome
Markdown discipline with transparency—not black boxes.
Impact
- More predictable margin performance
- Faster alignment in weekly reviews, less rework
- Repeatable plays that scale across regions and banners
Scenario 4: “We know markdowns are coming, but we can’t forecast the impact.”
What we hear
Teams plan markdowns without a clear sense of how much volume they will unlock or how much margin they will give up.
The pain points
- No way to simulate tradeoffs before committing the price move
- Fear of under or over reacting leads to missed windows
What changes with clarity
- Historical price/volume baselines combined with regression-based response curves estimate lift and margin impact by style/store
- Scenario planning compares first hit %, timing, and depth to pick the least cost path to exit targets
Outcome
Markdowns shift from guesswork to informed planning.
Impact
- Controlled exits with fewer last-minute escalations
- Higher confidence presenting pricing plans cross functionally
- Better balance of sell through, margin, and working capital
From Visibility to Precision
Inventory clarity is the foundation. Precision is the revolution.
In 2026, AI powered inventory and pricing capabilities have moved from pilots to the operating backbone—turning fragmented data into actionable, weekof action guidance for merchants.
Retailers using AI driven forecasting and optimization outperform peers on turnover, service levels, and margin—because they identify risk sooner and actwith confidence.
Yet what separates winners now isn’t “more AI”—it’s explainable signals, strong data governance, and clear decision rights that merchants trust.
How 100ENT Helps Merchandising Teams Win
We meet teams where they are:
- Trust the data (fast). We unify sell through, WOS, aging, allocation,and price histories into a single, real time view merchants can use in theweek—not at month end.
- Progress to precision. We layer explainable regression/forecasting torecommend first hit % and timing by style/store—supporting precision markdowns,not blanket discounts.
- Operate with confidence. We codify guardrails and triggers thatstandardize decisions while preserving merchant judgment and brand strategy.
The goal is confidence.
Confidence to act earlier.
Confidence to protect margin.
Confidence to explain decisions clearly across the organization.
