{"id":9394,"date":"2020-10-20T11:56:00","date_gmt":"2020-10-20T11:56:00","guid":{"rendered":"https:\/\/smarter-ecommerce.com\/blog\/en\/?p=9394"},"modified":"2022-06-24T12:04:13","modified_gmt":"2022-06-24T12:04:13","slug":"testing-the-solution-ai-based-bidding-goes-live-in-google-shopping","status":"publish","type":"post","link":"https:\/\/smarter-ecommerce.com\/blog\/en\/data-science\/testing-the-solution-ai-based-bidding-goes-live-in-google-shopping\/","title":{"rendered":"Testing the solution: AI-based bidding goes live in Google Shopping"},"content":{"rendered":"\n

In my last\u00a0post<\/a>, I discussed the challenging task of automated, item-level bidding in Google Shopping, and revealed how smec\u2019s data science team goes about designing an AI-based solution.<\/p>\n\n\n\n

We start by clearly defining the problem and choosing the appropriate data, and then we engineer this data to make it as useful as possible for our machine learning models. Next, we build and train those models, and tweak them to perfection. It\u2019s a great sneak-peek into the daily life of a data scientist in an industry setting, so if you haven\u2019t read it yet, take a look.<\/p>\n\n\n\n

Whenever we build or improve on a machine learning model, we need to thoroughly test it to ensure it benefits all customers. We\u2019ve developed a testing pipeline inspired by the pharmaceutical industry to do just that. Just like in a medical clinical trial, a proposed new drug must move through three stages and show real benefits at each one.<\/p>\n\n\n\n

To see how our testing procedure reflects this, let\u2019s walk through it with a hypothetical case study: introducing a new input feature to a model.<\/p>\n\n\n\n

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Our three stages of testing ensure \nimprovement at every step.<\/figcaption><\/figure>\n\n\n\n

For those who need a recap from the last post, our AI solution automatically generates item-level bids for any product in a retailer\u2019s inventory, on any browsing device, at any time. To do this, we train various machine learning algorithms to predict certain aspects of performance, which are used to calculate the best possible bid.<\/p>\n\n\n\n

The training data consists of \u2018features\u2019, carefully chosen aspects of the catalog and performance data. Each time we develop a new input feature for our models, we must test its effectiveness, before we can finally deploy the change.<\/p>\n\n\n\n

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Table of Contents<\/p>\n<\/div>