Rubikloud stresses long-term approach to securing clients following partnership with international beauty retailer

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In almost any industry imaginable, incumbents are eager to take advantage of the lean solutions coming out of startups. But for anyone who’s overheard the many panel discussions featuring enterprises and startups (FinTech is a common example), working with industry giants can be an arduous process compared to the nimble operations a startup is used to.

Toronto-based Rubikloud, a machine learning intelligence platform for retailers, is one startup that’s looking for success by matching the pace of the large retailers it works with. The company recently found a significant client in A.S. Watson Group (ASW), Asia and Europe’s largest international health and beauty retailer.

Rubikloud will work with ASW to deploy its solution, Rubicore — which allows retailers to migrate massive volumes of legacy data into the cloud — across ASW’s network of 13,300 retail stores across 25 Asian and European markets. The company will integrate legacy databases from across the Group’s business units and sources into a cloud-enabled database, which the company says will shorten the roll-out lead time of current and future machine learning tools by 50 to 80 percent.

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“Retail is one of those verticals where you have to make a pretty significant investment in that relationship, and if you want to sell really deep products to the retailer, you can’t treat them like a low-touch customer,” said Kerry Liu, Rubikloud’s CEO. “There’s a mistake startups make where they think that because software is so light-touch, they can get away with not being invested in that relationship, and we disagree with that.”

Liu said in the last few years, startups have created a new dynamic for retailers accustomed to working with tech partners like IBM or Oracle on massive, years-long million-dollar contracts, with the idea that people from those tech companies will live with that retailer.

“I think we have an opportunity to look at what machine learning means practically at the enterprise level, and Toronto can lead that field.” – Rubikloud CEO Kerry Liu

“The pendulum has shifted where startups are deploying lightweight products and they think that these retailers will adopt them with no real relationship or understanding of the retailer. So they’re like, it’s $5,000 a month, call us when you need help,” said Liu. “The impact for [machine learning and cloud] technologies to make the biggest impact for retailer sis on the big P&L systems like merchandising and loyalty, and even though deployment times are in weeks, you have to make an investment to get to know that retailer and their business based on the size of the contract.”

Because of the nature of retail — which measures itself based on week-over-week and month-over-month performances — it’s difficult to invest in innovation strategies even if it will pay off long term.

“We are investing in big data amid global economic uncertainties because we believe that technology is a critical enabler for successful retailing in today’s world,” said Malina Ngai, chief operating officer of ASW. “With the right technology, we will be able to focus our resources, from the backend support to shop floor, on building better customer experience. Rubikloud has cutting-edge machine learning applications and their new thinking in data science is a true value-add to our well established retail experience.”

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Liu describes ASW as being at an “interesting transition point.” While the Group wanted to get ahead of the curve in leveraging cloud technology to store and access data, they still had the problem of actually migrating the data into the cloud.

“On one hand, they’re like, yes, we’re innovative, we’re going to deploy apps rapidly, we’re going to train up our team, we’re going to have data available anywhere in the organization,” said Liu. “But it will still take two years to move data or have the data ready on Microsoft or Google.”

As places like Toronto and Montreal grow as machine learning hubs, Liu feels it’s telling that an international publicly-traded company like ASW would put their future in the hands of a startup of less than 100 people (though CEO Kerry Liu plans to change that soon). And his advice for Toronto founders thinking about launching machine learning startups is to look at the enterprise level, not just consumer-facing applications.

“I am in the camp where this could not just be a North American hub for machine learning; we should be a global leader for that. The implication of machine learning on business processes will be huge, and will impact everything,” said Liu. “I think we have an opportunity to look at what machine learning means practically at the enterprise level, and Toronto can lead that field.”