Analysis overview and configuration
| Parameter | Value | _row |
|---|---|---|
| agg_period | monthly | agg_period |
| outlier_threshold | 0.99 | outlier_threshold |
| min_orders_trend | 2 | min_orders_trend |
This analysis examines Average Order Value (AOV) trends across time periods and product categories for a Shopify store. The objective is to understand how customer spending patterns vary by month and vendor, providing foundational insights into revenue drivers and order composition. This descriptive analysis establishes baseline metrics for monitoring store performance.
The store demonstrates healthy order composition with strong multi-item attachment, yet faces a concerning downward AOV trajectory. The 19.8% decline across four months suggests
Data preprocessing and column mapping
| Metric | Value |
|---|---|
| Initial Rows | 80 |
| Final Rows | 41 |
| Rows Removed | 39 |
| Retention Rate | 51.2% |
This section documents the data transformation from raw Shopify export format to analysis-ready order-level data. The 51.2% retention rate reflects the aggregation of multi-row line-item records into 41 unique orders, which is essential for accurate Average Order Value (AOV) calculation and trend analysis.
The preprocessing successfully transformed Shopify's line-item export format into order-level aggregates required for AOV analysis. The 51.2% reduction is a natural consequence of consolidating multi-item orders, not a data quality problem. This approach preserves all revenue and order information while enabling accurate calculation of metrics like overall AOV ($305.16) and vendor-level performance across the complete order population.
The assumptions document confirms this transformation logic: "Total column populated only
| Metric | Value |
|---|---|
| Mean AOV | $305.16 |
| Median AOV | $313.2 |
| Total Orders | 41 |
| Total Revenue | $12,511.53 |
| Multi-Item Order Rate | 65.9% |
| AOV Growth (period) | -19.8% |
| Highest AOV Vendor | Hoka ($398.52) |
| Order Range | $115.83 - $528.12 |
This analysis evaluates Average Order Value (AOV) trends across a 41-order dataset to assess revenue optimization opportunities and product category performance. Understanding AOV patterns is critical for identifying pricing strategies, bundle opportunities, and vendor-specific growth levers that directly impact profitability.
The store achieves a respectable AOV of $305, but the declining trend signals potential demand softening or customer mix shift. The strong correlation between item count and AOV ($153 → $476) reveals that multi
Monthly Average Order Value trend across 6 period(s)
This section tracks how average basket size evolves across time periods to reveal whether customers are purchasing higher-value items or shifting toward lower-priced products. Understanding AOV trends is critical for identifying whether revenue growth stems from increased transaction volume, larger baskets, or both—directly informing pricing and product strategy decisions.
The declining AOV paired with surging order volume reveals a shift in customer purchasing behavior toward smaller, more frequent transactions. While total revenue grew to $12,511.53 across 41 orders, this growth masks a concerning pattern: customers are buying less per order. This may reflect increased discounting, a product mix shift toward lower-priced items, or reduced upsell effectiveness
AOV comparison across 8 product Vendor(s)/brand(s)
This section identifies which vendor brands drive the highest and lowest average order values, revealing premium versus value-oriented customer segments. Understanding AOV variation across vendors is critical for the overall analysis objective of optimizing revenue per transaction and identifying high-value product categories.
The 8-vendor portfolio exhibits substantial AOV stratification, with three brands commanding premium positioning above $350. Nike leads revenue share (17.2%) while maintaining above-average AOV ($359.10), suggesting strong volume-value balance. Conversely, Lululemon generates only 7.7% of revenue despite 5 orders, indicating lower basket values. This distribution reflects distinct customer purchasing behaviors by brand—athletic footwear
Distribution of order values showing spread from $115.83 to $528.12
This section reveals how order values are distributed across your customer base and identifies concentration patterns. Understanding order spread is critical for AOV analysis because it shows whether revenue comes from consistent mid-range purchases or is driven by occasional high-value orders—directly informing pricing strategy and customer segmentation decisions.
The distribution exhibits a right tail (positive skew of 0.95 in bin frequencies), typical of e-commerce where a minority of high-value orders disproportionately contribute to total revenue. The 90th percentile at $501.12 shows that top-performing orders cluster near the upper bound. With half of orders above mean AOV, your customer base demonstrates
Average Order Value by number of line items per order. 65.9% of orders have multiple items.
This section isolates the relationship between order composition (single vs. multi-item) and average order value. It reveals whether the store's overall AOV of $305.16 is driven by customers buying multiple products together, which directly informs inventory, merchandising, and pricing strategy effectiveness.
The data demonstrates that nearly two-thirds of orders already exhibit bundled purchasing behavior, and each additional item compounds revenue significantly. The progression from $153 (1 item) → $310 (2 items) → $476 (3 items) shows consistent, accelerating value capture. This pattern suggests customers perceive product combinations as valuable, and the store's vendor mix (athletic/outdoor brands) naturally supports complementary purchases.
The analysis captures only orders in this dataset; seasonal or promotional effects on bundling rates are not visible.