I was reading an article about “Amazon using its MultiDimensional Datasets”. The news never gets old to me that Amazon Retail leveraging its Big Data Ecosystem to access 1,000,000,000 GB of data on more than 1,400,000 servers to increase sales through predictive analytics does personalized recommendations, price optimization, and anticipatory predictions on what the customer will buy. While Walmart is racing to improve digital capabilities by acquiring the men’s clothing store Bonobos, Amazon kept adding offline capabilities by buying Whole Foods Market. With its recommendation engine capabilities, Amazon can think about innovating its retail stores by fully automating using Amazon Go, and predetermining customers’ grocery list for people who shop in Whole Foods.
A recommendation engine is not just about Retail and its customers. Personalized care and Meaningful use is a kind of recommendation engine for Patient care. Managing inventory based on weather conditions and demand are a type of recommendation engine [Perficient has an IP called Nimbus-iV to manage that]. In addition, content recommendation, personalization of videos or application, segmented marketing based on customer demographics, and personalized content based on personas have direct impact on bottom line. Here is Gartner’s definition of AIOPs on Continuous Monitoring.
The recommendation engine is a common Big Data application that can be leveraged for so many industries who are en route their Digital Transformation. These recommendation engines are tipping point between operational and predictive analytics. While Amazon’s industry disruption is mind-blowing, Perficient has its own recommendation engine which we are implementing with a smaller retailer than Amazon. However, we believe that our recommendation engine has the capability to scale in any Big Data platform. Talk to us to learn more about this.
In my next blog, I will discuss Edge Computing and its relationship to Big Data Analytics and Recommendation Engines.