AI and machine learning are undoubtedly changing how retail works, but this does not mean retailers are yet making the most of the capabilities of this technology. In this Q&A with RetailTechNews, Kerry Liu, CEO & co-founder, Rubikloud, explains how his business is helping retailers already, and the steps both e-commerce pureplays and brick-and-mortar businesses can be taking to tap into the opportunities offered by this technology.
RetailTechNews: Can you briefly explain what Rubikloud does?
Kerry Liu: Rubikloud is an artificial intelligence (AI) company that has created the world’s leading machine-learning platform for retail. Before launching, we noticed a sizeable gap in the retail market that demanded the implementation of AI and machine learning software. The retail market was growing too sophisticated for humans alone to manage. Many processes and decisions relied on guesswork and too much was at stake to have human errors erode thin margins.
We realised the need for the data to be warehoused on a sophisticated and modern cloud for real-time insights across hundreds of thousands of products. Our data platform, RubiCore, gathers and ingests retailer data from both legacy and new systems into a single data platform in the cloud. This single source offers a unified view and universal access to the data.
How is your machine-learning technology helping retailers?
The use cases for AI are mission critical to retailers. By enabling enterprise business solutions that address the unique business issues that retailers face, (including promotion planning, forecasting, inventory, overstock and understock accuracy, and loyalty revenue/growth), Rubikloud’s platform can generate 10+% revenue and profit gains. Rubikloud has purposefully designed their solutions to readily integrate with legacy systems and to begin delivering tangible results to our clients, within weeks instead of years.
Machine learning gets better at predicting what to sell, to whom, and when to sell it, every single time it is used and as more data is inputted into the system. Many retailers offer thousands of SKUs, scores of promotions, and numerous loyalty programmes simultaneously. Rubikloud analyses these terabytes of data to help merchandisers and marketers automatically decide the price of an item based on the customer. It also helps retailers decide how much of any item to stock, when it should go on sale, and how to avoid over or under stocking. These are just a few of the benefits to retailers.
Is machine learning being better used by online pure plays, rather than brick-and-mortar stores?
Amazon pioneered many of the machine-learning advances for online pure plays, but brick-and-mortar stores like Walmart and A.S. Watson’s brands have reached near parity with Amazon to predict what products to serve to customers at the price they will pay. It’s equally beneficial for both online and traditional retailers.
Can machine learning help traditional brick-and-mortar retailers keep their stores open?
Yes! Machine learning is designed to not only improve retail’s e-commerce game, as many are led to believe, but also the brick-and-mortar game. For example, A.S. Watson Group recently announced that it plans to open 1300 stores this year, which is equal to a new store every seven hours! We’ve seen that early AI-adopters, like them, are thriving because the data is doing the work for them.
What are the most exciting applications of AI and machine learning going to be in the retail ecosystem?
The sector is in its early days and will continue evolving at a rapid pace. It’s both exciting and a relief to retailers to know that their data can accurately determine exactly what to stock, when to stock it, when to move it, and how to price it. In the future, AI will be able to layer in more variables beyond season, weather, and shopping patterns – creating even more advanced predictions with less data. This is what we are most excited about.