Dynamic pricing with revenue optimization

by Regressor   Last Updated May 16, 2019 03:19 AM

I have been working on a case study where I have been given some data about fruits. Features include fruit_id, fruit_type, color, demand_supply_ratio, date_posted, geo_country, is_purchased, fruit_price. Every seller in the market can post a fruit_price on a given day(date_posted captures this day). I want to build a predictive model so as to recommend a daily fruit price such that the revenue for that seller is optimized. How do I go about solving this problem?

Data dictionary

fruit_id - unique ID describing the fruit
fruit_type - categorical - A/B/C etc
color - color of the fruit
demand_supply_ratio - ratio of how many people saw that fruit to how many such fruits were available in the given country
date_posted - timestamp of the date when the seller posted that fruit for sale
is_purchased - 0 or 1. Was the fruit sold that day or not
fruit_price - numerical variable.Price of the fruit posted by the seller

How do I come up with an equation such that when I as a new-seller want to launch a fruit, the model should recommend me an optimal price for my fruit on that day.

Is it similar to dynamic pricing for companies like Airbnb where a host while in the process of posting their listing, is recommended a price which gives maximum returns to the host?

Tags : optimization


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