Predictix
announced the latest generation of its cloud-based forecasting applications at this year’s NRF BIG Show. The new version adds advanced machine learning to the company’s suite of predictive and prescriptive analytics solutions for retail.
Machine learning is a process that allows supercomputers to learn from mining masses of Big Data without human intervention. While traditional approaches rely on time series models, machine learning accesses thousands of data attributes, some of which are publicly available and external to the retailer. This could lead to a deeper understanding of customer activity.
“Retailers have made do with a variety of time series forecasting tools that came of age when technology was expensive and difficult to use, data were scarce and latent, and growth and profitability were not as dependent as now on fast and accurate ‘granular’ forecasts,” said Greg Girard, Program Director of Worldwide Omnichannel Retail Analytics Strategies at IDC Retail Insights. “Analytically astute retailers running complex omnichannel businesses in highly competitive markets must give serious consideration to machine learning-based forecasting.”
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To illustrate the potential of machine learning in retail, Predictix points to an example from grocery. An international grocery chain reduced wastage by 50% by applying machine learning-based forecasting and supply chain applications to its fresh food categories.
“The benefits of machine learning can be enormous for retailers,” said Ron Menich, EVP and Chief Scientist at Predictix. “Every 5% improvement in forecasts can yield a 3% reduction in inventories, with similar increases in sales and margins. With our machine learning-based solutions, improvements of 25% to 50% are possible, particularly at the local level and for the most challenging forecasts, such as promotions and new products where there is no history.”
