What primary analytical technique identifies the optimal frequency and channel for retargeting potential customers who abandoned their online shopping cart?

A/B testing, combined with response modeling, is the primary analytical technique used to identify the optimal frequency and channel for retargeting potential customers who abandoned their online shopping cart. Retargeting involves displaying ads or sending messages to users who have previously interacted with a website or app, such as abandoning a shopping cart. A/B testing involves creating two or more versions of a retargeting campaign, varying the frequency of ads or messages, the channels used (e.g., email, social media ads, display ads), and the content of the ads or messages. Each version is then shown to a random sample of cart abandoners, and the results are tracked to determine which version performs best in terms of conversion rates, click-through rates, and return on ad spend. Response modeling involves using statistical techniques to predict the likelihood of a customer responding to a retargeting campaign based on their past behavior and characteristics. This allows for the creation of personalized retargeting strategies that target individual customers with the optimal frequency and channel based on their predicted response. By combining A/B testing with response modeling, marketers can identify the most effective retargeting strategies for different customer segments and optimize their campaigns to maximize conversions. For example, A/B testing might reveal that a higher frequency of email retargeting is effective for customers who abandoned high-value items, while a lower frequency of social media retargeting is more effective for customers who abandoned low-value items. Response modeling can then be used to further refine these strategies by predicting the optimal frequency and channel for each individual customer based on their browsing history and purchase behavior.