Market basket analysis is a powerful technique for uncovering patterns between items in transactions. Whether you're analyzing products in an online store or content on a media platform, identifying these associations can inform marketing strategies, recommendation systems, and website structure. This post dives into the basics of market basket analysis, how to implement it using R and the arules package, and how to leverage these insights for data-driven decision-making.

What is Market Basket Analysis?

Market basket analysis examines the relationships between items in transactions. The goal is to identify sets of items that frequently co-occur, helping businesses understand how products or content are associated. Common applications include:

• Store layout optimization: Place frequently co-occurring items closer to encourage purchases.
  
  
• Targeted marketing: Promote related products to customers based on past purchases.
  
  
• Recommendation engines: Suggest content or products based on user behavior patterns.
  
  

Key Terminology

• Items: Individual objects analyzed for associations (e.g., products, articles).
  
  
• Transactions: Groups of items that occur together in a single instance (e.g., a purchase, a content session).
  
  
• Rules: Statements in the form {item(s)} => {item(s)} indicating associations.
  
  
• Support: The fraction of transactions containing a specific item or set of items.
  
  
• Confidence: The probability that a transaction containing item(s) on the left-hand side (LHS) also includes item(s) on the right-hand side (RHS).
  
  
• Lift: The ratio of observed support to expected support, indicating the strength of an association.
  
  

Implementing Market Basket Analysis with R

Snowplow users can implement market basket analysis using R and the arules package, which provides the Apriori algorithm for rule mining. Here’s a step-by-step approach:

Step 1: Fetch Transaction Data

Query Snowplow to extract transaction data, structuring it to align with the analysis:

SELECT

  "ti_orderid" AS "transaction_id",

  "ti_name" AS "sku"

FROM

  "events"

WHERE

  "event" = 'transaction_item';

Step 2: Load Data into R

Connect to the data source and load the transaction data:

library("RPostgreSQL")

con <- dbConnect(drv, host="<<REDSHIFT ENDPOINT>>", port="<<PORT>>", dbname="<<DBNAME>>", user="<<USER>>", password="<<PASSWORD>>")

t <- dbGetQuery(con, "SELECT ti_orderid AS transaction_id, ti_name AS sku FROM events WHERE event = 'transaction_item'")

Step 3: Preprocessing Data

Convert the data into a transaction object:

i <- split(t$sku, t$transaction_id)

txn <- as(i, "transactions")

Step 4: Running the Apriori Algorithm

Apply the algorithm to identify rules:

library("arules")

basket_rules <- apriori(txn, parameter = list(sup = 0.005, conf = 0.01, target = "rules"))

inspect(basket_rules)

Visualizing the Results

To manage large result sets, use the arulesViz package to visualize the top rules by lift, confidence, and support:

library("arulesViz")

plot(basket_rules)

Interpreting the Results

Focus on rules with high lift and confidence. High lift (>1) indicates strong associations, while high confidence signifies a reliable rule. Prioritize rules with higher support to maximize impact.

Applications of Market Basket Analysis

• Website Optimization: Group associated content items for improved user experience.
  
  
• Recommendation Engines: Power recommendation systems based on user behavior.
  
  
• Targeted Marketing: Develop marketing campaigns that promote frequently co-purchased items.
  
  

Expanding the Analysis Scope

While the above example focuses on transactions, the analysis can be extended to:

• Add-to-basket data, identifying potential upsell opportunities.
  
  
• Session-level interactions to reveal content affinities over time.
  
  
• Multi-session analysis for deeper insights into user behavior patterns.
  
  

Final Thoughts

Market basket analysis is a versatile tool that can significantly impact marketing, content strategy, and customer experience. By implementing it within Snowplow, organizations can leverage robust data sets to drive actionable insights and optimize both product and content placement effectively.