All treats, no tricks with product recommendation reference patterns

In all things technology, change is the only constant. This year alone has brought more uncertainty than ever before, and the IT shadows have felt full of perils. With the onset of the pandemic, the way consumers shop has shifted faster than anyone could have predicted. The move to online shopping vs. brick and mortar stores was already happening, but it’s significantly accelerated just this year alone. Shoppers have quickly transitioned to online purchasing, resulting in increased traffic and varying fulfillment needs. Shopper expectations have evolved as well, with 66% of online purchasers choosing a retailer based on convenience, while only 47% choosing a retailer based on price/value, according to Catalyst and Kantar research.So the pressure is on for retailers to become digital and make sure shoppers are happy. But there’s no reason to be spooked out. Done right, you can serve customers better with an understanding of their customers’ purchasing behavior and patterns using predictive analytics. Deep, data-driven insights are important to ensuring customer demand and preferences are accurately met. To make it easier to treat (not trick) your customers to better recommendations, we recently introduced Smart Analytics reference patterns, which are technical reference guides with sample code for common analytics use cases with Google Cloud, including predicting customer lifetime value, propensity to purchase, product recommendation systems, and more. We heard from many customers that you needed an easy way to put your analytics tools into practice, and that these are some common use cases.Understanding product recommendation systemsProduct recommendation systems are an important tool for understanding customer behavior. They’re designed to generate and provide suggestions for items or content a specific user would like to purchase or engage with. A recommendation system creates an advanced set of complex connections between products and users, and compares and ranks these connections in order to recommend products or services as customers browse your website, for example. A well-developed recommendation system will help you improve your shoppers’ experience on a website and result in better customer acquisition and retention. These systems can significantly boost sales, revenues, click-through-rates, conversions, and other important metrics because personalizing a user’s preferences creates a positive effect, in turn translating to customer satisfaction, loyalty, and even brand affinity. Instead of building from scratch and reinventing the wheel every time, you can take advantage of these reference patterns to quickly start serving customers. It’s important to emphasize that recommender systems are not new, and you can build your own in-house or from any cloud provider. Google Cloud’s unique ability to handle massive amounts of structured and unstructured data, combined with our advanced capabilities in machine learning and artificial intelligence, provide a powerful set of products and solutions for retailers to leverage across their business.Using reference patterns for real-world casesIn this reference pattern, you will learn step-by-step how to build a recommendation system by using BigQuery ML (a.k.a. BigQu-eerie ML

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