Steven' Extended Transactions Machine Learning Model

Steven's Extended Transaction and Bidirectional Model is a sophisticated association-based model that aims to capture more complex consumer behaviors and provide greater insights into product pairings than traditional basket analysis association models. Steven's model is designed to consider the buying behavior of consumers who purchase across platforms, such as online and retail stores, as well as the constraints that consumers may face, such as product availability or the need to make an afterthought purchase. Additionally, the model captures the behavior of consumers who purchase a single item that acts as a gateway to subsequent purchases.

The core of Steven's Extended Transaction and Bidirectional Model is a bidirectional approach that analyzes transactions in both directions, from past to future and from future to past, allowing the model to consider both forward and backward relationships between items. This approach allows the model to identify both direct and indirect associations between items, providing a more comprehensive understanding of consumer behavior.

To run the model, stakeholders must set adjustable parameters based on their analysis, including Extended Transaction Days (ETD) and Transaction Continuity (TC) for the Extended Transaction Model, and Direction for the Bidirectional Model. By adjusting these parameters, stakeholders can fine-tune the model to better capture the complexities of their specific market and consumer behavior.

The model has several use cases in the business world. By analyzing complex buying behaviors across various platforms and customer segments, the model can help companies optimize their product offerings and marketing strategies to maximize sales and profitability. Specifically, the model can be used to:

• Identify cross-selling and upselling opportunities: By analyzing the bidirectional relationships between items, the model can identify complementary products that are frequently purchased together, even if they are not purchased within the same transaction. This can help companies identify opportunities to increase sales by promoting complementary items together and optimizing product bundles.

• Improve inventory management: By understanding the relationships between items, companies can optimize their inventory management by ensuring that complementary products are in stock and that popular products are always available.

• Enhance marketing strategies: The model can help companies identify which products are frequently purchased together by different customer segments, enabling them to develop targeted marketing strategies that are tailored to specific customer needs.

• Optimize store layout: By understanding how customers navigate through stores and which products they are likely to purchase together, the model can help companies optimize their store layout to increase sales.

In summary, Steven's Extended Transaction and Bidirectional Model provides a powerful tool for companies to understand their customers better and make data-driven decisions about product development, marketing strategies, and business operations. By analyzing complex buying behaviors across various platforms and customer segments, the model can help companies optimize their product offerings and marketing strategies to maximize sales and profitability.

A detailed overview of Steven's Extended Transaction model is made available at the bottom of this page.

Steven created an advanced 'Product Relationship Insights' dashboard which allows users to interact with his Extended Transaction Model and view real-time results. By modifying model parameters such as date range, ETD and TC values, model direction, consumer age group and gender, and region, users can refine the model to suit their specific use case. After the model runs, users can view product relationships to identify which products are most commonly purchased together based on their entered criteria. 

The ability to interact with the model in real-time provides valuable insights for businesses looking to optimize their product offerings and marketing strategies. By refining the model based on specific criteria, users can gain a deeper understanding of their customers' behavior and make data-driven decisions that will drive growth and increase profitability.