At Style Arcade, this is one of the most common questions, closely followed by: "What should I actually do with my product data?"

This guide answers that — and gives you the tools to become a commercial decision-maker, not just a reporter of results.

Here you'll find a clear list of metrics to check, what the data is telling you, and — most importantly — the actions to take. Whether you’re reviewing weekly trade, building a seasonal strategy, or troubleshooting performance, this guide will help you turn insights into results.

💡 The highest-performing planners don’t just observe trade — they steer it! They know where to look, how to interpret what they see, and what to do next.

Start by Thinking in Three Layers

Trade analysis is most effective when you look at it from multiple angles:

1. Big Picture – Strategic Overview

Are we up or down vs. plan or last year? What trends or shifts are driving performance?

2. Key Actions – Tactical Priorities

Which levers — like promos, pricing, or allocation — can we adjust to move the needle?

3. Detailed Analysis – Execution Level

Which products, SKUs, stores or weeks are driving the result? What’s working, what’s not?

💡 Remember these stages aren’t always linear! Often, digging into the detail first reveals the underlying issue — which then shapes your broader strategy and response.

Across all three levels, your job is to:

Identify what’s performing well — and what isn’t
Spot early wins or issues before they escalate
Turn analysis into action — fast

Taking Action

Once you’ve identified what’s working (or not), here are some key tactical in-season levers you can pull to respond with impact.

🔲 Visual Merchandising & Campaigns

Review how products are displayed and promoted to boost sales
Feature full-price items to reduce discounting reliance
Plan marketing support for key products to drive sell-through

💰 Pricing & Margin

Check for unnecessary margin loss from discounting
Prioritise higher-margin styles
Tailor offers by sales channels where appropriate

📦 Replenishment & Stock

Reorder or fast-track bestsellers
Delay or cancel slow movers
Rebalance stock between stores and online based on demand

🧵 Product Lifecycle & Range Planning

Flag quality or fit issues to QA or design
Decide whether to repeat, rebalance, or exit a style
Reduce or delay buys for overstocked lines
Set up smart buys for next season based on performance

Execution-Level Metrics & Actions

“Use product data to move faster and smarter — not just to report performance.”

Here’s a practical breakdown of what to track, what it tells you, and examples of what to do next (in line with the options above):

🧾 Core Performance Metrics

Metric	What You're Looking For	Indicators & Examples	What To Do Then Examples
Sales $	Under or over performance	🔺TY $ > LY/Plan: Strong demand 🔻TY $ < LY/Plan: Potential issue with range, traffic, or conversion	Investigate all product metrics to check any areas of opportunity Prioritise replenishment or bring forward stock for high performers; review VM or offer on underperformers
Sell Through %	How quickly stock is selling	🔺>70% early in season = selling fast / missed opportunity 🔻<30% mid-season = slow mover / risk of carryover	Reorder/extend for high ST; markdown or rebalance stock for low ST
Sales units	Sales volume movement	🔺High units but low $ = low ASP or deep discounting 🔻Low units = weak demand or stock issue	Back in key volume drivers with clean margin; review range or promo for slow sellers Check availability and consider marketing support
Sell Price	Margin and Sell Price health	🔻ASP < Plan = deeper discounts 🔺ASP > LY = strong full price sell-through	Review if Sell Price drop is planned or margin-eroding Identify Full Price styles — keep visibility high on site; reduce discount dependency
Gross Profit %	Discount impact and COGS health	🔻Gross Margin % = Margin erosion due to pricing, markdowns or cost issue	Review markdown strategy, cost prices and pricing architecture Shift focus to higher-margin categories; pull back on deeper markdowns or rebalance product mix
Cover	Supply vs sales rate	🔺>10 weeks = overstock risk 🔻<3 weeks = OOS risk Note cover indicators depend on your lead times - if short, you need less cover, long and you need more cover. As a general rule of thumb (if keeping stock lean) your target cover should = your lead time + 30%. The planned life-cycle of the product also plays a factor!	Review future purchases and product plans to plan cover Chase core styles with low cover; Reduce or delay intake if possible on any refills planned on overstocked core lines Pull forward markdowns or promotions for overstocked seasonal lines
Stock units	Stock distribution	🔻Stock flat week-on-week = poor ST 🔻Stock down, sales flat = potential shrinkage	Prioritise allocation review or stock movement; investigate sell-down blockers in store or online
Markdown & Discount %	Discount depth	🔺>40% of sales on markdown = aggressive promo TY markdown % > LY = margin impact	Evaluate if markdowns are driving volume or just margin erosion; pull back or reframe promo if appropriate
Return Rate %	Product or fit issue	🔺>15% return rate = warning flag Common reason = “Too small” = sizing issue	Remove from repeat buys or rebuy strategy; call out to design/product for fit or quality improvement
Customer Reviews	Fit, quality, or style issues	Multiple 1-2 star reviews mentioning “quality” or “fit”	Flag to QA or design, investigate factory batch. Pause repeats until issue is resolved.

📆 Time-Based Metrics

Metric	What You're Looking For	Indicators & Examples	What To Do Then Examples
Sales by Week vs LY	Trend shifts and trade spikes	TY sales spike early = early promo impact Flat post-event = lost momentum	Shift promo timing next season, reforecast peak periods
Sales Around Key Events	Promo or marketing effectiveness	Boxing Day 🔺TY vs LY = improved timing or offer Click Frenzy 🔻TY = offer didn’t resonate	Analyse offer structure, review timing, uplift store/online support

📍 Channel/Location Specific Metrics

Metric	What You're Looking For	Indicators & Examples	What To Do Then Examples
Sales by Channel	Channel-specific performance	Online TY < LY = conversion issue Wholesale flat = range or service issue	Tailor channel assortment and promo tactics; adjust buy depth
Sales by Store / Region	Localised performance gaps	Store X sales 🔻20% vs LY = local issue Regional spikes = potential events	Investigate local events, staffing, VM, or weather
Traffic & Conversion	Demand vs execution	🔺High traffic, low conversion = product or staff issue Low traffic, low sales = marketing/centre issue	Adjust marketing or support store teams accordingly

🧵 Range Health Metrics

Metric	What You're Looking For	Indicators & Examples	What To Do Then Examples
Performance by Category / Sub-Category	Range depth and allocation	Jackets 🔺TY vs LY = trend hit Pants 🔻TY vs LY = missed opportunity	Review individual product performance. If category trend, adjust OTB for upcoming drops; revise markdown strategy
Style Ranking / Top Sellers	What’s driving sales	Top 10 = majority of category sales = over-reliance risk Same style repeats = core performer	Chase hero lines, ensure in-stock, leverage in marketing
Style Age / Lifecycle	Style seasonality health	Style aged X weeks with low ST = underperformer Week 2 style at 80% ST = missed demand	Clear slow aged stock; chase and extend lifecycle for high performance styles
Range Width & Depth	Assortment balance	Too many styles, low units = low efficiency Small number of styles delivering majority of volume = over-reliance	Rationalise assortment or expand based on data and lifecycle stage

Congratulations! You’re now on your way to bridging data and action, and driving margin, sell-through, and inventory efficiency across seasons and channels 🚀

Our most common FAQ; "Where Should I Start?"