You have a list of text strings. Q: Here is my query for you: Market AI Tech, Inc. has developed an algorithm that predicts customer behavior in online shopping environments by analyzing customer data collected from the company' vice presidential sales department and creating personalized advertising campaigns. The algorithm of this problem-solving system takes as input a collection of products, their respective prices, discount rates for each product, and the budgetary constraints that apply to each customer group (e.g., 50% off on Black Friday). To develop your company’s recommendation model using the given data below, you need to find the most profitable approach to market different groups of products to a particular targeted niche in an online fashion store with multiple constraints: - The algorithm should be able to calculate and apply elastic net present value (ENP) based on current inventory, price information, customer demographics, purchase history, as well as the seasonal changes. Here is a description of what I want you to do in this task: Let's start with an overview of your business case: You are operating an e-commerce platform that has access to thousands of items and customers around the world can use our product! The objective here is to find the most profitable approach for marketing different groups of products. Here, we provide a hypothetical example on how such task could be achieved with 10 constraints: Product A ($50) sells at $20 and has a discount rate of 30% during Black Friday. Product B ($70) usually retails for $40 but is now available for $60. Your target market segment (Group X), composed by tech-savvy consumers, has an average willingness to pay of 85 and a standard deviation in their spending power is given as sigma = $5, with the coefficient of variation at 0.25. Product C ($100) usually costs for $90 but can be obtained with 40% discount on Wednesdays. Your marketing team forecasts that there will be a demand elasticity factor (elasticity) of 1.5 when you offer the product A at its current price, and a cross-price elasticity of 2 for products B and C with respect to Product A. Constraints: 1. The algorithm must take into consideration the discount rate, but not only based on a fixed percentage off but adjusted by using the following formula : P(s) = p * (d / e^(−β*(p − S))^2, where β is set to be equal to 3 for all computations. Here is some information about how this algorithm should operate: It must calculate and apply the ENP of each product considering that there are three different customer groups with distinct willingness-to-pay; Group X being a tech-savvy consumers who spend an average $85, group Y consisting of students at 60% less than group X but twice as voluminous in their spending power and have no elasticity to the price changes for any product they see. Lastly, there is Group Z (young professionals), with willingness-to-pay $125, a higher degree of sensibility of 0.7 towards prices changes but a smaller cross-price elasticity than group Y at 1.2. It has to consider the fact that: The algorithm must also factor in seasonal trends (for instance holiday sales) and be able to predict the potential revenue for each product, after implementing your recommendation model based on historical data from previous years as well as provide a forecast of how much stock you should hold for each product so that no item is left over or under-stocked by the end of quarter.