Systems and methods for selecting product placement locations and products

A method comprising:

• determining, by a computer processor, a first shot segment and a second shot segment of digital media;
• analyzing the first shot segment and the second shot segment using a first model to determine a first product location corresponding to a first position for placement within the first shot segment and a second product location corresponding to a second position for placement within the second shot segment, wherein the first model is a machine learning computer vision model trained to determine locations in shot segments of the digital media for placement of virtual products;
• determining the first product location is the same as the second product location;
• determining a first product size and a second product size, the first product size based on the first product location and the second product size based on the second product location;
• analyzing the first shot segment and the second shot segment using a second model to determine first contextual data corresponding to the first shot segment and second contextual data corresponding the second shot segment, wherein the second model is machine learning model trained to automatically determine contextual data in shot segments of the digital media;
• determining a first product and a second product based on the first product location, the first contextual data, and the second contextual data;
• determining a first revenue value corresponding to the first product and based on the first product, the first product size, and the first product location;
• determining a second revenue value corresponding to the second product and based on the second product, the second product size, and the first product location;
• and
• determining the first revenue value exceeds the second revenue value.

A system comprising:

• memory configured to store computer-executable instructions, and
• at least one computer processor configured to access memory and execute the computer-executable instructions to:
• determine a first shot segment and a second shot segment of digital media;
• analyze the first shot segment and the second shot segment using a first model to determine a first product location corresponding to a first position for placement within the first shot segment and a second product location corresponding to a second position for placement within the second shot segment, wherein the first model is a machine learning computer vision model trained to determine locations in shot segments of the digital media for placement of virtual products;
• determine the first product location is the same as the second product location;
• determining a first product size and a second product size, the first product size based on the first product location and the second product size based on the second product location;
• analyzing the first shot segment and the second shot segment using a second model to determine first contextual data corresponding to the first shot segment and second contextual data corresponding to the second shot segment, wherein the second model is a machine learning model trained to automatically determine contextual data in shot segments of the digital media;
• determine a first product and a second product based on the first product location, the first contextual data, and the second contextual data;
• determine a first revenue value corresponding to the first product and based on the first product and the first product location;
• determine a second revenue value corresponding to the second product and based on the second product and the first product location;
• and
• determine the first revenue exceeds the second revenue.