基于全渠道零售客户购买数据集的满额免运费行为检测问询

Great question—this is a classic threshold effect problem in retail analytics, and there are some practical, data-driven ways to test for this "basket topping-up" behavior in your dataset. Let’s break this down step by step:

First, filter your data to only include online channel orders—since offline purchases don’t have a shipping threshold, they’ll just add noise to your analysis.

If you have access to cart snapshot data (the amount before the user added final items), that’s ideal. If not, you can still work with final order amounts, but the analysis will focus on distribution anomalies instead of direct "added items" behavior.

Split your online orders into logical amount buckets to highlight the threshold effect:

• Group A: Orders with initial/final amount <$40 (far below the $50 free shipping threshold)
• Group B: Orders with initial/final amount $40–$49.99 (the "near-threshold" group we care about)
• Group C: Orders with initial/final amount ≥$50 (already above the threshold)

Focus on two metrics to spot the topping-up behavior:

• Threshold Upgrade Rate: For Groups A and B, calculate the percentage of orders that end up with a final amount ≥$50. If Group B’s rate is significantly higher than Group A’s, that’s a clear sign users are adding items to hit the free shipping mark.
• Average Jump Amount: For orders in Group B that upgraded to ≥$50, calculate the average difference between their final amount and initial cart amount. A small, concentrated jump (e.g., $2–$5 on average) strongly suggests users are adding low-cost items to meet the threshold.

Don’t rely just on raw percentages—use stats to confirm the difference is meaningful:

• Chi-Square Test: Compare the upgrade rates of Group A and Group B to see if the difference is statistically significant (look for a p-value < 0.05).
• Two-Sample T-Test: If you have initial cart data, compare the average jump amount of Group B’s upgraded orders vs. Group A’s upgraded orders. A significant difference here reinforces the threshold-driven behavior.

Charts make the pattern obvious to stakeholders:

• Order Amount Histogram: You’ll likely see a "dip" in orders between $40–$49.99 and a sharp "spike" right at $50—this is the classic signature of threshold topping-up.
• Upgrade Rate Bar Chart: Plot the upgrade rates for Group A and Group B side by side; Group B’s bar should be noticeably taller if the behavior exists.

Here’s a quick snippet to implement this with pandas and scipy:

import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt

# Filter to online orders only
online_orders = your_dataset[your_dataset['channel'] == 'online']

# Create amount groups (using initial cart amount if available)
online_orders['cart_group'] = pd.cut(
    online_orders['initial_cart_amount'],
    bins=[0, 40, 50, float('inf')],
    labels=['<$40', '$40-$49.99', '≥$50']
)

# Calculate upgrade rates
upgrade_rates = online_orders[online_orders['cart_group'] != '≥$50'].groupby('cart_group').apply(
    lambda x: (x['final_amount'] >= 50).mean()
)
print("Upgrade Rates:\n", upgrade_rates)

# Chi-square test for significance
contingency_table = pd.crosstab(
    online_orders[online_orders['cart_group'].isin(['<$40', '$40-$49.99'])]['cart_group'],
    online_orders[online_orders['cart_group'].isin(['<$40', '$40-$49.99'])]['final_amount'] >= 50
)
chi2, p_value, _, _ = stats.chi2_contingency(contingency_table)
print(f"\nChi-Square Value: {chi2:.2f}, P-Value: {p_value:.4f}")

# Plot histogram of final amounts
plt.hist(online_orders['final_amount'], bins=30, edgecolor='black')
plt.axvline(x=50, color='red', linestyle='--', label='$50 Free Shipping Threshold')
plt.xlabel('Final Order Amount ($)')
plt.ylabel('Number of Orders')
plt.legend()
plt.show()

If your dataset includes more details, you can dig further:

• Look at the type of items users add to hit $50—are they low-cost, impulse buys (like snacks or travel-sized products)?
• Segment by user type: Do new users top up more often than repeat customers?
• Check time trends: Is the behavior more common during sales or holiday periods?

内容的提问来源于stack exchange，提问作者John legend2