Cohort Analysis for Supply Chain Management

Introduction

Cohort analysis is a technique used to analyze customer behavior based on a shared characteristic, such as the time of their first purchase. It allows businesses to track how different groups (cohorts) behave over time. In supply chain management, cohort analysis can be a valuable tool for understanding:

Customer retention: How long customers remain active after their first interaction.
Demand forecasting: Identifying trends in customer behavior to predict future demand.
Supplier performance: Evaluating supplier reliability based on factors like lead times or product quality (by analyzing cohorts based on the supplier).
Inventory management: Optimizing inventory levels for specific products and life cycle stages based on customer segment behavior.

How Cohort Analysis Works

Cohort analysis involves grouping data points based on a shared characteristic, typically the time period (e.g., month, year) in which a customer first interacts with the supply chain (e.g., placing an order). The analyst then tracks how these cohorts behave over time. This can include metrics like:

Retention rate: The percentage of customers from a specific cohort who remain active buyers after a certain period.
Purchase frequency: How often customers from a cohort make purchases.
Average order value: The typical amount spent by customers in a cohort per purchase.

By analyzing these metrics across different cohorts, businesses can identify patterns and trends to make data-driven decisions.

Applications in Supply Chain Management

The following sections delve deeper into specific applications of cohort analysis in supply chain management:

Customer Segmentation

Cohort analysis helps identify different customer segments with distinct buying behaviors. This information can be used to tailor marketing campaigns and promotions to specific customer groups for increased effectiveness.

Demand Forecasting

By analyzing historical purchase patterns of different cohorts, businesses can develop more accurate forecasts of future demand. This can help optimize inventory levels and reduce the risk of stockouts or overstocking.

Supplier Performance

Analyzing cohorts based on the supplier can provide insights into supplier reliability and identify potential issues with lead times or product quality.

Inventory Management

Understanding how different customer segments interact with the supply chain can help businesses optimize inventory levels for specific products and at different stages of a product’s life cycle.

Example: Analyzing Mobile Game Launch Data

This example demonstrates how to perform a basic cohort analysis on a sample online mobile game launch dataset using the cohorts package.

# Install and load required libraries (if not already installed)
if (!require("cohorts")) install.packages("cohorts")
library(tidyverse)
library(cohorts)

# Load the sample game launch data
data(gamelaunch)

# Define cohort by day of first game launch
cohort_data <- gamelaunch %>%
  mutate(cohort_day = day(eventDate)) %>%
  cohort_table_day(userid, eventDate)

# Calculate and visualize retention rate by cohort
retention_rates <- cohort_data %>%
  filter(cohort <= 7) %>%  # Focus on the first 7 cohorts for clarity
  shift_left_pct() %>%
  pivot_longer(-cohort) %>%
  mutate(time = as.numeric(str_remove(name, "t"))) %>%
  filter(time > 0) %>%  # Exclude initial observation (redundant)
  ggplot(aes(time, value, colour = factor(cohort), group = cohort)) +
  geom_line(size = 1) +
  labs(title = "Retention Rate by Cohort (Daily)", x = "Days since First Launch", y = "Retention Rate (%)") +
  theme_minimal()

retention_rates