The retail analytics market is growing almost as fast as the retail market itself. In fact, a recent study by Reportlinker estimates that the retail analytics market will grow from $2.2 billion in 2015 to $5.1 billion in 2020. The report finds that the factors driving this growth include the rise in retail data volumes, types and accumulation, as well as demand for omnichannel insights.
However, most retailers have not yet deployed analytics solutions at scale, and most who have are still in the early stages of deployment. That means it’s not too late to get started, and you still have a huge opportunity to gain an edge on your competitors using data. In fact, it’s never too late to get a competitive edge from your data.
To accomplish this, you will want to start at the top, using analytics to first detect revenue-impacting events, including operational issues such as a broken checkout button or internal process interruptions, business issues such as rapid consumer behavior changes, or even Internet infrastructure issues, as quickly as possible. We’ll refer to this subset of retail analytics as Retail Order Analytics. Below, we’ll explain how automated machine learning has proven itself to be the preferred technology for companies who want to stay competitive in today’s eCommerce landscape, and we’ll highlight some of the key mathematical challenges involved in accurately analyzing retail operations data to detect revenue-impacting events. Below, we share seven reasons to deploy retail order analytics and some key factors you should consider before deciding on any retail analytics solution: