Techniques for optimizing the impact of video content on electronic commerce sales

A data processing system, comprising:

• an experiment management engine configure to track conversion results;
• and
• a recommendation/optimization engine coupled to the experiment management engine, wherein the recommendation/optimization engine is configured to measure a video profit of a product by:
• performing an A/B test for the product while monitoring for customer conversion, wherein at least one of ‘A’ and ‘B’ correspond to video;
• determining a unique number of visitors to a product webpage that viewed a call-to-action for a video of the product based on the test;
• determining a gain that accounts for customer bias based on the test;
• determining a non-viewer conversion rate based on the test;
• determining a video view rate based on the test;
• determining a video conversion lift based on the test;
• determining an abandonment factor based on the test;
• and
• determining an incremental video profit for the product based on the unique number of visitors, the gain, the non-viewer conversion rate, the video view rate, the video conversion lift, and the abandonment factor.

A method of measuring a video profit for a product, comprising:

• performing, using a data processing system, an A/B test for a product while monitoring for customer conversion, wherein at least one of ‘A’ and ‘B’ correspond to video;
• determining, using the data processing system, a unique number of visitors to a product webpage that viewed a call-to-action for a video of the product based on the test;
• determining, using the data processing system, a non-viewer conversion rate based on the test;
• determining, using the data processing system, a view rate based on the test;
• determining, using the data processing system, a video conversion lift based on the test;
• determining, using the data processing system, an abandonment factor based on the test;
• and
• determining, using the data processing system, a forecasted video profit for the product based on the unique number of visitors, the non-viewer conversion rate, the video view rate, the video conversion lift, and the abandonment factor.