Highlights:
- Predictive market basket analysis examines items purchased in sequence to evaluate cross-selling opportunities.
- Market basket analysis provides valuable insights into customer behavior, revealing which products are often purchased together and which items are most frequently bought.
Machine learning is transforming B2B industry by enhancing various aspects, from sales predictions to customer identification. One prominent approach is Market Basket Analysis (MBA), which helps marketing personnel understand which products are generally bought together. This insight enables optimized store or website layouts and supports cross-selling strategies.
Market analysis is also used to detect fraudulent insurance claims and credit card transactions. Known as frequent itemset mining or association analysis, this data mining technique examines co-occurrence patterns to identify relationships between items, ensuring that frequently bought products are always in stock.
Market Basket Analysis Algorithm
Market basket analysis deploys association rules in the pattern of {IF} -> {THEN} to predict the certainty of certain products being ordered together. This process involves counting the frequency of items appearing together and identifying associations that occur more often than expected.
Algorithms that utilize these association rules include AIS, Apriori, and SETM.
Among these, the Apriori algorithm is widely leveraged by data scientists to identify frequent item sets in the database. It’s particularly valuable for unsupervised learning, as it doesn’t require training or making predictions. This algorithm is especially effective for analyzing large datasets to uncover meaningful relationships between items.
Interestingly, the Apriori algorithm in MBA uses a shortcut known as the Apriori property, which asserts that all items in a frequent itemset must themselves be frequent. This property significantly reduces computational time.
The Apriori algorithm systematically identifies item sets that frequently occur in the dataset and have support above a pre-specified threshold. It then calculates the confidence for all possible rules, retaining only those with confidence greater than a predetermined threshold.
The algorithm serves as the foundation for uncovering patterns in customer purchasing behavior, which can then be explored through various types of market share analysis to gain deeper insights.
Types of Market Basket Analysis
Understanding the analysis types is crucial for leveraging the MBA’s full potential. Each type offers unique insights into customer purchasing behavior and helps businesses tailor their strategies accordingly.
- Descriptive market basket analysis
This analysis provides actionable insights derived from historical data. It is a widely used method that doesn’t make predictions but evaluates the strength of associations between products using statistical techniques. Because of its approach, it is also known as unsupervised learning.
- Predictive market basket analysis
While the term “predictive analysis” includes the words “predict” and “analysis,” the process actually works the other way around. It starts by analyzing data and then predicts future outcomes. This approach uses supervised learning models like regression and classification. It remains a precious tool for marketers despite being less commonly used than descriptive MBA.
Predictive market basket analysis examines items purchased in sequence to evaluate cross-selling opportunities. For instance, when customers purchase a product, they are more likely to prefer an extended warranty as well. This type of analysis helps identify such sequential items so they can be effectively marketed and sold together.
- Differential marketing basket analysis
This approach examines data from various stores, along with purchases made by different customer groups at different times of the day, month, or year.
If a rule applies to one dimension, like a specific store, period, or customer group, but not to others, analysts can identify the factors behind the exception. These insights can inform new product offerings that boost sales performance.
Learning the approaches of market analysis sets the stage for exploring real-world instances where businesses have successfully integrated them and have borne optimal results.
Market Basket Analysis Real-world Examples
Amazon’s website provides a classic example of market basket analysis in action. On product pages, Amazon suggests related items under sections like “Frequently bought together” and “Customers who bought this item also bought.”
Market basket analysis is also applicable in physical stores. For instance, if the analysis reveals that customers frequently purchase bookmarks along with magazines—an unexpected pairing since no books are involved—a bookstore might strategically place bookmarks near the magazine rack to capitalize on this trend.
Analyzing credit or debit card history offers valuable opportunities for IBFS companies. For example, Citibank often sends sales reps to malls to attract customers with instant discounts and partners with services like Swiggy and Zomato to offer exclusive deals redeemable via credit cards.
Given the intense competition in the telecom and technology sector, companies focus on the benefits customers frequently use. For example, telecom providers are now bundling TV and internet services with other low-cost internet platforms to minimize customer churn.
These real-time business instances of MBA adoption intrigue sales and marketing decision-makers worldwide to give it a try, sit back, and see stellar sales growth in autonomy.
Why Should Business Decision-makers Adopt Market Basket Analysis?
Incorporating market basket analysis into business operations is no longer just an option but a critical necessity. It empowers organizations with sophisticated insights into consumer behavior, enabling the development of data-driven strategies that optimize inventory management and enhance cross-selling opportunities. The following reasons stand firm in advocating MBA adoption in marketing operations.
- Better customer understanding
Market basket analysis provides valuable insights into customer behavior, revealing which products are often purchased together and which items are most frequently bought. Companies can leverage this information to better understand their customers, leading to informed decision making.
- Streamlined inventory management
By analyzing market basket data, sales personnel can identify slow-moving products and those frequently bought together. This information enables them to make informed decisions about stocking and managing inventory more effectively.
- Simple pricing strategies
Understanding the relationship between product prices and consumer behavior can help businesses craft more effective pricing strategies. With this insight, they can develop pricing plans that drive both sales and profitability.
- Stellar sales growth
Market basket analysis report helps businesses identify which products are often purchased together and their optimal store placement to increase sales. Business executives can boost revenue and plot customer experience strategies by optimizing store layouts and product positioning.
Summarizing
Market basket analysis sequential patterns are increasingly being adopted by businesses to uncover valuable insights into product associations and implicit linkages. In particular, a predictive variant of market basket analysis is gaining popularity across modern-day industries as business leaders seek to identify patterns in sequential purchases.
Industry leaders actively explore this technique to enhance their understanding of consumer behavior and optimize their strategies.
Explore a curated selection of sales & marketing related whitepapers designed to enhance your understanding with detailed analysis and comprehensive insights.
