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Shopping basket analysis: definition & important aspects for buyers
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Shopping basket analysis in a nutshell:
Shopping basket analysis is a statistical method for investigating correlations between different purchased products or services. For purchasing, it enables the optimization of procurement strategies by identifying frequent product combinations and order patterns.
Example: An automotive supplier analyzes 10,000 order transactions over 6 months and discovers that in 85% of cases, fastening material is also ordered when purchasing seals, which leads to the renegotiation of framework agreements with bundled conditions.
Contents
What is the shopping basket analysis?
Shopping basket analysis is a method from data mining and statistics that identifies correlations and patterns in purchasing data. It examines which products are frequently ordered together in order to identify associations between items. Originally used in retail, shopping basket analysis is used in purchasing to understand procurement patterns and support strategic decisions.
Core elements of the shopping basket analysis
Importance of shopping basket analysis in purchasing
In purchasing, shopping basket analysis provides valuable insights into product combinations and procurement patterns. It supports buyers in identifying demand bundling, optimizing supplier strategies and improving negotiations. By understanding which items are frequently procured together, processes can be made more efficient and costs reduced.
Shopping basket analysis: from manual evaluation to data mining
Building on the theoretical foundation of market basket analysis, it is essential to understand its practical application in purchasing. The ability to identify hidden patterns in purchasing data enables companies to make strategic decisions and gain competitive advantage. With the shift from manual analysis to modern technology, there is a growing need for more effective methods of data processing and interpretation.
Old: Manual evaluation
Traditional approach: In the past, shopping basket analysis was often carried out manually. Purchasing teams analyzed sales data using spreadsheets and looked for obvious correlations between products. This method was time consuming and often limited to a limited amount of data. Manual processing led to a higher error rate and it was difficult to recognize complex patterns or rare associations. In addition, the results were often outdated as the data was not analyzed in real time.
New: Data Mining
Data mining: The modern approach uses advanced data mining techniques to efficiently analyze large amounts of data. By using machine learning algorithms, companies can now process extensive transaction data in real time. Key innovations include the application of inventory analysis, clustering and predictive models that provide deep insights into customer behavior and shopping habits. This technology not only improves the accuracy of the analyses, but also significantly reduces the time required. Practical benefits include the ability to create personalized offers and optimize inventory levels based on predicted demand.
Practical example: Implementation at a retailer
A leading retailer has implemented data mining techniques to improve its shopping cart analysis. By analyzing over 1 million transactions, the company identified that customers who purchased product A were 65% more likely to purchase product B as well. Based on these insights, joint promotions were developed, resulting in a 15% increase in sales for this product category. In addition, the retailer was able to optimize its stock levels and reduce inventory costs by 10%.
Conclusion on the shopping basket analysis
Shopping basket analysis is an indispensable tool in modern purchasing that opens up considerable optimization potential through the systematic analysis of procurement patterns. It not only enables the efficient bundling of requirements and improvement of supplier relationships, but also creates the basis for data-driven purchasing decisions. With the increasing integration of AI and machine learning, shopping basket analysis will provide even more precise and automated recommendations for action in the future, further increasing process efficiency and cost savings.
