Price optimization with machine learning
$250-750 USD
Betalades vid leverans
It would be preferred if the application is written in PHP since it is a part of CMS (Litecart), but other solutions that would include importing/exporting data would be accepted.
Required functionality:
1) Changes buying, selling and trade-in price of products to the optimal value for maximum profits and also tries to get rid of excess stock
2) Two modes: automatic price adjustment every day at certain hour and the the applocation recommends the changes, but the user has to accept them
3) Must work on used and new products. New products can be reordered (lets say infitive supply), while used products are brought by customers, so the supply is limited and increasing buying price would probably get more products.
4) Suggest removing certain products from sales since cluttering the web page with products that do not sell can hurt overall sales
5) Suggest realocating stock between stores by looking at how well a certain products sells and at what store it is more likely to sell. Sometimes customers pick from a product range and in some cases the specific product is less important, as long as there are some options of this kind of product type.
6) Automatically adding and removing certain products as on sale price with limited time offer.
7) Display sales statistics, number of products sold, total price, revenue,.. per store and online sales.
8) Work with preorders, also adjust their price
9) Recommend stock for items that can be resupplied by entering the number of days the stock should last
The following inputs are available, although some other could be added if possible:
1) quantity available (per store)
2) buying price for each product (note buying and tradein price are different)
3) date and selling price for each product sold
4) number of visitors for the product per day
5) how many times the product has been added to the basket per day (but not necessarly ordered)
6) how often the product has been ordered, but not actually bought (either the customer didn't pick up the product or rejected delivery)
7) in what store the product was purchased or if it was shipped to customer
8) type of product, for example sports, for children, racing
9) when the product will be available (preorders)
For the current stock there is no info for what price the products were purchased, for the new products it will have to be added manually at the moment.
The point of the application is that it is able to adjust to market quickly, there are big differences between time of the year, for example December has sales several times higher as during summer.
Number of sales can often be more important as maximum profits, since it generates more sales in the long run.
Projekt-id: #17958129
About the project
10 frilansare har lagt bud på i genomsnitt $896 för det här jobbet
Price optimization with machine learning Dear sir I have completed projects similar to your requirement in the past. If you could share your project detail requirements, I will share relevant past work and demo. Mer
Hello I have more than 10 years experience in machine learning, data mining, and other AI related fields. in addition i have the required economy (supply and demand rules..etc) and business knowledge to build such sys Mer
Hi, I read your requirement and ready to start working on it but please come over chat for further details discussion to start working on it. Thanks
Hello, I have genuine skills in *Angular, React JS, Node JS. *PHP MVC frameworks such as Wordpress, Laravel, Magento*. *Ruby and Ruby on Rails Don't worry about basic Mer
Hi, One of our team member is currently working on a consulting firm retail which focuses on stock optimization in retail. We are not experienced in PHP but we can look at what we can do if you are willing to share Mer
I have been Working in machine learning and data science field since 2+ year. I have done few projects related to optimization also. Would love to take up this project
Work include following steps 1) Gather input data a. Transactional: b. Description of the products: c. Customer Reviews: d. Inventory and supply data. 2) Mer