The problem for machine learning developers lies in the availability of that data. Retail datasets typically contain proprietary information and are consequently hard to find, as are sales datasets. To help you out, Ishow you a list of open data sources that may prove useful:

Product Datasets for Machine Learning

• Electronic Products and Pricing Data: This dataset contains a list of over 7,000 electronic products with 10 fields of pricing information.

Retail Transaction Datasets for Machine Learning

• Retailrocket Recommender System Dataset: This data was collected from a real-world ecommerce website over a period of 4.5 months. Furthermore, it contains information on visitor behavior including events like clicks, add to carts, and transactions.

Ecommerce Data and Search Relevance Datasets for Machine Learning

• ECommerce Search Relevance: This set contains image URLs, rank on page, a description for each product, the search query that led to each result, and more from five major English-language ecommerce sites.

What can be done with this information?

• Example: Lineage is an artificially intelligent engine that enables the exploration of digitized visual archives in a human-like manner. With Lineage, the user can input any image, and get in return visually similar images from thousands of years of art and design. The returned images are not identical to the input but rather give the user the visual context in which it exists, allowing for a deeper understanding of the input image.

MediaMarkt can make an own database: This set can contains image URLs, rank on page, a description for each product, the search query that led to each result, most popular products and more. Then, the machine learning can make a predictions of sales, and products success

Reference to the health and wellbeing area:

Machine Learning can imitate the human brain based on experiences.

I suggest to the Media Markt company that you hire a firm of consultants with experience in Machine Learning. They know what mathematical models to implement in your store. For example:

• The sales area will provide price lists and the firm will develop a mathematical model to predict the correct price of your product in this times of coronavirus.
• The logistics area will provide lists of products distributed in all markets. This firm will develop a mathematical model to predict how to distribute the products in chrismas days.
• The customer service area will provide stock lists of spare parts for its department. The firm will develop and use a mathematical model to predict how many parts to order next year.
• The marketing area will provide lists of the events that it organizes each year. The firm will develop and use a mathematical model to predict how many events to organize for example in December.

NOTE: This idea is part of a holistic project with overview, where I suggest a process reengineering in MEDIA MARKT stores, and this detailed content you can find in the next idea's link: THE POP UP