Recommended Product is one of the models available to find patterns and predict user preference based on historical data with ease thanks to Datagran’s pipeline tool.

As we all know by now, users interact with recommendation systems every day, for example streaming services like Netflix, or SlingTV tailor suggestions based on shows we have previously watched. Having Recommendation systems in place also broadens a company’s ability to exploit new sources of income, as well as the chance to build a stronger connection with their customers not only online, but offline as well. Starbucks is a great example of how recommending products automates a user journey seamlessly. For example, 16 million active Starbucks ® Rewards members now receive thoughtful recommendations from the app for food and drinks based on local store inventory, popular selections, community preferences, and previous orders. “Just like their relationship with a barista, customers receive the same care and personalized recommendations when it comes from our digital platforms,” says Jon Francis, senior vice president, Starbucks Analytics and Market Research. [1] This type of personalization brings companies closer to their users, waking up in them a sense of being cared for. Amazon reported total sales of $280.5 billion in 2019, from $177.86 billion in 2017 based on Statista. A big chunk of that growth has to do with the way Amazon has integrated recommendations into nearly every part of the purchasing process. But don’t think they only make use of recommendation systems inside the platform, “Amazon also doles out recommendations to users via email… In fact, the conversion rate and efficiency of such emails are ‘very high,’ significantly more effective than on-site recommendations. According to Sucharita Mulpuru, a Forrester analyst, Amazon’s conversion to sales of on-site recommendations could be as high as 60% in some cases based on the performance of other e-commerce sites.” 

And that is exactly what can be done with the Datagran platform. Our all in-one-data workspace gives the power to companies to centralize, clean, and process their data from multiple data sources, apply a Recommended Product Operator and use the results which could be a list of users in the form of a table, to send them to leading business applications such as Mailchimp, Google Ads, Intercom, Facebook Ads and more. Learn more about Recommended products here or watch our CTO Necati Demir explain in detail how he uses this tool in our platform.