Mulebuy Spreadsheet Sizing Analysis: Professional Batch Comparison Guide

Rapid Sizing Assessment Process

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Open your Mulebuy spreadsheet and immediately filter for size-related columns. Create three custom columns: 'Batch', 'Seller Variation', and 'Consistency Score'. Sort by item type first, then batch date, then seller. This hierarchy eliminates 90% of noise in your analysis.

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Batch Tracking Method

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Assign batch codes using the date format MMDD (e.g., 0915 for September 15th). Place this in the first custom column. When comparing, group items within two-week batches. Anything older than six months becomes 'legacy data' - use it only for extreme size variations, not current decision making.

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Seller Variation Algorithm

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For each seller-item combination, calculate deviation rates:

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• True to Size: 70-100% positive reviews = Score 3
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• Slightly Off: 40-69% positive reviews = Score 2
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• Significant Deviation: Below 40% = Score 1
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Cross-B seller Comparison Protocol

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Never compare across different item categories. Jeans sizing metrics apply differently than jackets. Establish category-specific baselines. For footwear: focus on EU/JP variations. For upper wear: prioritize chest/arm length ratios.

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Red Flag Indicators

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Watch for these immediate rejection signs:

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• Inconsistent sizing scores (Score fluctuations > 1.5 across 4+ listings from same seller-item combo)
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• Mixed batch data where newer batches show opposite trends of proven ones
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• Poor-quality QC photos accompanying sizing claims
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• Lack of seller photos (critical for fit verification)
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The 48-Hour Rule

\p>\nRecent listings (48 hours old) receive 20% weight reduction. Early reviews often come from beta testers or rushed purchasers. Wait for day 4-7 data to stabilize your size calculations.
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Advanced Consistency Metrics

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Implement a variance calculation: subtract the worst-fit review percentage from the best-fit percentage. Scores below 40% indicate unreliable sizing. This metric works particularly well for complex items like sneakers with multiple sizing systems.

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Seasonal Adjustment Factors

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Account for manufacturing cycles:

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• Summer drops: expect tighter cotton blends
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• Winter releases: allow for thicker material adjustments
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• Limited editions: higher sizing deviation (up to 15%)
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Final Decision Matrix

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Rank options using four factors: Batch Age Score + Seller Consistency Score + Review Sample Size + Seasonal Factor. Weight Batch Age highest (35%), Consistency next (30%), Sample Size (25%), Seasonal (10%). Scores below 60 indicate probable issues above 85% suggest reliability. Execute decisions within the top three ranked options to prevent analysis paralysis.

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Quick Tips for Efficient Analysis

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• Color-code spreadsheet headers: Green for consistent, Yellow for questionable, Red for avoid
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• Maintain personal size profiles for top 10 most frequent purchases
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• Bookmark sellers with 90%+ consistency scores across 3+ consecutive batches
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• Export data monthly; trends reveal themselves longitudinally
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