4Ps Of Marketing Impact On Supply Chain

📦 4Ps of Marketing Impact on Supply Chain Performance

Overview

This dataset simulates how marketing decisions based on the 4Ps of Marketing (Product, Price, Place, Promotion) influence supply chain performance.

Each row represents one product in one region during one monthly cycle, combining marketing inputs with resulting operational outcomes such as demand, inventory levels, stockouts, costs, and customer satisfaction.

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Context & Inspiration

In real organizations, marketing and supply chain teams are often analyzed separately, even though their decisions are tightly connected:

• Promotions can trigger sudden demand spikes
• Pricing affects demand elasticity and inventory turnover
• Distribution choices influence delivery time and logistics cost
• Supply failures (stockouts, delays) directly impact customer satisfaction

This dataset is designed to help analyze and model these cross-functional dependencies, enabling realistic business and machine learning use cases.

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Use Cases

This dataset can be used for:

• Stockout and backorder prediction using marketing and inventory inputs
• Demand modeling (forecasted vs. actual) based on price, discount, and promotions
• Customer satisfaction analysis using fulfillment rate, lead time, and stockouts
• Cost estimation and optimization, including holding cost, emergency restock cost, and spoilage
• Business analytics linking marketing strategy to operational performance

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What’s in the Data

🅿️ Product Variables

• Product category (FMCG, Electronics, Apparel, Home, Personal Care)
• Product lifecycle stage
• Number of variants
• Shelf life (days)
• Packaging type
• Quality-related return rate

💰 Price Variables

• Base price
• Discount percentage
• Competitor price index
• Price elasticity score
• Price change frequency

📍 Place (Distribution) Variables

• Sales channel (Online, Retail, Distributor)
• Region
• Warehouse distance (km)
• Delivery lead time (days)
• Last-mile delivery cost
• Channel-specific return rate

📣 Promotion Variables

• Promotion type (None, Discount, Bundle, Campaign)
• Promotion duration
• Marketing spend
• Promotion intensity
• Forecasted demand
• Actual demand

🚚 Supply Chain Outcome Variables

• Opening inventory
• Replenishment quantity
• Closing inventory
• Stockout flag
• Backorder quantity
• Emergency restock cost
• Inventory holding cost
• Spoilage units
• Order fulfillment rate
• Customer satisfaction score (1–5)

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Built-In Relationships

The data is generated with realistic business logic, including:

• Demand increases with discounts, promotions, and higher elasticity
• Delivery lead time increases with warehouse distance
• Stockouts occur when demand exceeds available inventory
• Spoilage increases for products with shorter shelf life
• Customer satisfaction decreases with stockouts and delivery delays

These relationships make the dataset suitable for explainable ML and causal analysis, not just random modeling.

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Source

This is synthetic data, generated for analytics, machine learning, and educational purposes.

• No real company data
• No confidential or personal information
• Structure inspired by common CPG, retail, and supply chain KPIs

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Technical Details

• Rows: 25,000
• Columns: 34
• Missing values: None
• Tasks supported:
  • Regression
  • Classification (e.g., stockout prediction)
  • Demand forecasting
  • Customer satisfaction modeling

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Ideal For

• Kaggle notebooks and competitions
• Supply chain analytics projects
• Marketing analytics and pricing studies
• End-to-end business ML portfolios