June 14, 2018
Originally posted on SiliconAngle.com
Like a hyper-efficient laundry center, Magento [Commerce]. takes dirty, disorganized data from diverse sources and sends it through a spin cycle, cleansing it and delivering information that’s prepped for predictive analysis.
“We’ve created an infrastructure that allows all kinds of data—whatever data that you have; it could be in a spreadsheet or it could be an automated feed—to come together into the platform to cleanse it, model it for your use, and then be able to leverage it for…key performance indicator reporting, but also to run advanced data science on it and then use that to power eCommerce,” said Anita Andrews (pictured), director of analytics services at Magento. “It’s really about bringing the data together into a single place and then using that all throughout the organization.”
Andrews spoke with Lisa Martin (@LuccaZara), co-host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, at the Imagine event in Las Vegas. They discussed digital transformation in the realm of commerce and how Magento Business Intelligence, or MBI, is maximizing the value of data through incorporating machine learning algorithms to predict customer demand and project inventory requirements.
Personalized business intelligence saves money, increases customer satisfaction
“The aim and the aspiration of MBI is to be able to take any data source,” Andrews said. The easiest method to import data is using Magento’s automated integrations that enable instant data flow. But there is also an application program interface for data import. And Magento’s active community of developers have created third-party methods to connecting to the MBI platform.
Leveraging MBI allows B2B customers to combine data sources and transform it within the stack, running it through machine learning algorithms to provide highly personalized predictions. MBI also has built-in dashboards to allow anyone in the organization access. “Many businesses have never seen this sort of total view of their customers,” Andrews stated.
Inventory management is one use case where Andrews sees a lot of benefit. “We’re starting to see a lot of traction, and results [are] around advanced analytics and machine learning,” she said.
Inventory is money, so excess inventory sitting on shelves or lack of inventory to sell impact a company’s bottom-line profit. “Traditionally, it’s been very hard to predict what…you’re gonna need and when you’re gonna need it,” Andrews said. “There’s now capability within MBI that you can feed into your vendor management or other sorts of merchandising management systems…and can be customized to predict which of your SKUs, which of your products are going to be running out,” she concluded.