{"id":22848,"date":"2024-10-14T13:33:52","date_gmt":"2024-10-14T13:33:52","guid":{"rendered":"https:\/\/www.bloomreach.com\/library\/using-predictive-marketing-analytics-to-boost-ecommerce-success"},"modified":"2024-10-14T13:33:53","modified_gmt":"2024-10-14T13:33:53","slug":"predictive-marketing-analytics","status":"publish","type":"library","link":"https:\/\/www.bloomreach.com\/en\/blog\/predictive-marketing-analytics","title":{"rendered":"Using Predictive Marketing Analytics to Boost Ecommerce Success"},"content":{"rendered":"

When retailers use artificial intelligence (AI)<\/u><\/a>, it allows them to better serve customers and ultimately generate more revenue. Of the organizations that have deployed AI, 80%<\/u><\/a> use predictive analytics, which identifies trends in historical data to forecast future outcomes.<\/p>\n

Within ecommerce, predictive analytics has several use cases, from optimizing the supply chain to lowering logistics costs and reducing organizational risk. One of the most effective use cases is predictive marketing<\/u><\/a>. Here, retailers use data insights to guide them toward the best marketing channels, the best time to send communications, and the best product recommendations<\/u><\/a> for individual customers.<\/p>\n

In this article, we explore predictive analytics in depth to discover how retailers are benefitting today, and what best practices marketers should follow to maximum value.<\/p>\n

What Is Predictive Analytics?<\/strong><\/h2>\n

There are four types of analytics:<\/p>\n

    \n
  • Descriptive analytics:<\/strong> using data to say what happened<\/li>\n
  • Diagnostic analytics:<\/strong> using data to explain why something happened<\/li>\n
  • Predictive analytics:<\/strong> using data to forecast what is likely to happen next<\/li>\n
  • Prescriptive analytics:<\/strong> using data to make recommendations on how to optimize outcomes<\/li>\n<\/ul>\n

    Predictive analytics is the point at which AI starts to give retailers a competitive edge because it allows them to target customers with the right products at the right time and in the right marketing channels<\/u><\/a>.<\/p>\n

    It uses historical customer data, market trends, and modeling techniques to make informed predictions about the future. It means that when a customer performs a site search<\/u><\/a>, they are shown relevant results with personalized product recommendations<\/u><\/a>\u00a0\u2014 for example, if an individual is known to prefer chunky knitwear, these products are prioritized over sweatshirts, turtlenecks, or hoodies.<\/p>\n