The Simple Neckline Error That Will Be Increasing Your Returns

In fashion retail, the devil’s not just in the details – it is the details. When a category is as dynamic and emotionally charged as fashion, integrity in your data structure matters. Inaccurate or missing product attributes don’t just harm personalisation – they quietly chip away at profit.

Take necklines, for example.

We track over two dozen neckline attributes at Mapp Fashion. On the surface, a crew, round, and scoop neck may seem nearly interchangeable. But if you’re selling to women with a larger bust (40% of your audience), that crew neck is 5% more likely to be returned compared to its round or scoop counterparts.

Now, not every product has a neckline, and not every customer has the same fit needs. But for the cohorts where it does matter, this subtle product feature has a measurable impact on returns and that has knock-on effects across demand forecasting, inventory allocation, and customer experience.

Structured Data: The Hidden Driver of Smarter Forecasting

Fashion forecasting has long leaned on historical sales and buyer intuition. But true precision comes from a granular understanding of product components.

Take two seemingly similar mini dresses:

• One has puff sleeves, a high neckline, and a stiff fabric.
• The other has a drapey fit, scoop neck, and subtle ruching

They may share a price point and product category, but they’ll perform completely differently across regions, body types, and use cases. AI can only recognise and respond to these patterns if the underlying data captures the nuance.

Impact: Retailers that enrich their product data down to the attribute level see up to 8% improvement in margin through better forecasting, fewer stockouts, and smarter reorders.

Use Case: Smarter Forecasting

What changes: Demand forecasts shift from vague estimates to SKU-level confidence scores.
Result: Less fragmentation, fewer stockouts, smarter reorders.
Typical Impact: Retailers see up to 8% margin improvement from reduced overstock and markdowns.

When Attributes Drive Profit

Not all product features carry equal weight. The neckline example is proof. While certain features (like style, fabric, and colour) may influence ad engagement, it’s other attributes – neckline, fit, stretch – that determine how a customer feels in an item. And that, ultimately, dictates what customer will return.

This is the logic behind our Fashion Taxonomy Engine. We enrich product catalogues with features that drive decision-making – not just filtering.

Example: Necklines by Return Rate

• Crew Neck: +5% returns
• Round Neck: Neutral
• Scoop Neck: -2% returns (Among women with a larger bust – regardless of age)

High-Quality Structured Data: The Fashion AI Foundation

AI is only as good as the data you feed it. Yet most product datasets are built for internal processes (like warehousing), not for fashion customers or the AI tools serving them.

Best Practices:

• Prioritise Precision: Invest in clean, structured product data with attributes that map to how shoppers think (vibe, occasion, mood).
• Build a Living Taxonomy: Update continuously – fashion is fluid. Your data structure should be too.
• Fuse Data Sources: Merge sales data with return reasons, style feedback, and search patterns for richer insights.
• Govern for Consistency: Ensure product attributes are applied uniformly across all teams and platforms.
• Optimise Continuously: Let feedback loops fuel AI learning – what attributes actually predict satisfaction?

Real Outcomes from Retailers Who Get This Right

Retailers who reframe their approach – from channel-led to data-led – are pulling ahead.
Here’s what’s possible when your product data starts to speak the language of fashion:

1. Incremental Revenue: +8%
2. Unsold Inventory: -12%
3. Retention Rates: +15%
4. Overall Profitability: +10%
5. Advertising Efficiency: +20%

Closing Thought: Are You Describing Products the Way Customers Think?

It’s not about having more data. It’s about having the right data – structured in a way that AI can read, and customers can feel. Fashion’s biggest opportunity isn’t more tech – it’s making your tech fluent in fashion. Let’s stop optimising in isolation. Let’s start connecting the dots – from neckline to net margin.