Accurately modeling consumer behaviour and the events that drive and inhibit sales is a tricky problem for retailers.
Specialist in IT operation and security analytics Prelert, has turned its expertise on the problem and is launching a new Retail Order Analytics solution. It's aimed at helping online and multichannel retailers to identify technical and operational issues as they're happening in order to stem losses and protect revenue streams.
Built using unsupervised machine learning technology, Prelert's solution automates data analysis and detects the periodic nature of daily and weekly order cycles. It adapts to changing data patterns that may result over time due to factors like a new product becoming available or current events that cause a spike in product interest.
"A significant drop in the number of orders taken by an e-commerce site during a particular day might be obvious in retrospect, but can be very difficult to catch in near real time without automated machine learning. Static thresholds and even moving averages can’t reliably identify issues," says Mark Jaffe, CEO of Prelert. "Our anomaly detection algorithms have been proven to work and provide significant ROI within hundreds of progressive IT organisations around the globe. We can provide the same value now for retail and e-commerce organisations, with a solution tailored specifically for them".
Unlike other solutions which require data to be moved or batch uploaded, Prelert is designed to be easy to deploy, bringing analytics to where an organisation's data already resides and analysing it in near real time. In addition, an open API allows developers to use Prelert in their own products or environments.
For more information on Prelert's Retail Order Analytics you can visit the company's website.