How Decision Tree Analysis is Useful: read the following article and use to solve the questions.

Pricing Strategy: You can use decision tree analysis to determine the optimal pricing strategy for your products. By considering factors like product cost, market demand, competitor pricing, and seasonality, you can build a decision tree to identify the best price points for each product. This analysis can help you set competitive prices that attract customers while ensuring a healthy profit margin.

Stock Management: Decision trees can assist in stock management by predicting the demand for different products. By incorporating variables such as historical sales data, marketing efforts, and external events $($e$.$g$.,$ product launches or promotions$),$ you can make informed decisions on how much inventory to stock for each item. This minimizes the risk of overstocking or running out of popular products.

Customer Segmentation: To tailor your marketing and product offerings, you can create decision trees to segment your customer base. Factors like demographics, purchase history, and customer preferences can help you identify different customer groups. This segmentation allows you to offer personalized promotions and products to enhance customer satisfaction and loyalty.

Promotional Campaign Optimization: Decision tree analysis can help optimize your promotional campaigns. By analyzing the impact of various marketing channels, campaign duration, and discount levels on sales, you can create decision trees to determine the most effective promotion strategies. This way, you can allocate your marketing budget more efficiently and increase your ROI.

Product Recommendations: Implement decision tree algorithms for product recommendations on your e$-$commerce platform. By considering customer behavior, browsing history, and purchase patterns, you can create personalized product recommendation trees. This increases the likelihood of cross$-$selling and upselling, driving higher revenues.

Inventory Turnover: Decision trees can be used to predict how quickly different products will sell based on historical data and variables like product popularity, seasonality, and consumer trends. This information helps you optimize your inventory turnover, reduce holding costs, and improve your cash flow.