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 systems
Product 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 cases
In 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 👻) to generate product or service recommendations from customer data in BigQuery. Then, learn how to make that data available to other production systems by exporting it to Google Analytics 360 or Cloud Storage, or programmatically reading it from the BigQuery table. The key advantage of using BigQuery ML is really how quickly and simply you can build a machine learning model with data already stored in BigQuery. In addition, the ease of productionizing the recommendation system ultimately saves you time and money. The same person can now analyze data and also train and deploy models in BigQuery using BigQuery ML. You no longer need a data engineer in between to export data out of BigQuery for ML purposes.
You can also see this step-by-step guide that explores the e-commerce recommendation system, as well as in this Notebook environment that helps walk you through the entire process of building such a system in your organization. You will learn how to:
Process sample data into a format suitable for training a matrix factorization model.
Create, train, and deploy a matrix factorization model.
Get predictions from the deployed model about what products your customers are most likely to be interested in.
Export prediction data from BigQuery to Google Analytics 360, Cloud Storage, or by programmatically reading it from the BigQuery table.
Smart Analytics reference patterns are designed to reduce the time to value to implement analytics use cases and get you quickly to implementation. To get started, check out the existing reference patterns and select the one that best fits your needs.
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