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Big Data: Big Retail Transformation

In this special guest feature, Kerry Liu, Rubikloud CEO, discusses how the retail sector has reached an “AI or Die” moment. Retailers that don’t adapt AI will soon die out. But those that embrace the technology to make automated decisions will enjoy an immediate uptick in their business, and should be able to transform themselves to survive. Kerry leads three important functions at Rubikloud: people, sales, and technology disruption. Rubikloud helps retailers monetize their data to power personalized campaigns through the use of advanced machine learning techniques, effectively bridging the gap between marketing automation and retail management consultants. In his role, Kerry works to manage and maintain a thriving company culture that recruits the best and brightest in the industry, while also maintaining relationships with global retailers and investors. He is passionate about machine learning and big data, and enjoys providing enterprise retailers with the tools and knowledge needed to enhance their overall business goals.

What do you get when you combine 38,000 product stock keeping units (SKUs) across 35 promotion mechanisms, 8 store formats, 16 in-store display locations, and multiply it by 52 weeks each year? An ulcer for sure, once you realize that a retailer this size has about 9  trillion possible promotion decisions to navigate yearly. Yes, trillion!

Hundreds of variables affect retailers’ sales and promotions each day, rendering it incredibly challenging for humans alone to be timely and effective simultaneously. Retail is becoming a David vs. Goliath industry and it’s clear that there’s no match between man and machine, especially when faced with Goliath’s like Amazon and Walmart, which use artificial intelligence (AI) solutions to drive results based on sophisticated machine learning algorithms.

Machine learning models use individual SKU and store data to determine accurate sales forecasts. They provide data-driven insights for merchandisers that reduces the complexity of promotions and generates stronger ROI. Done well, in three months, machine learning can reduce excess inventory for a retailer by 25 percent and reduce their “stockouts” (when demand exceeds available product) by 31 percent. These automated decisions can also lead to a 30 percent increase in promotion forecast accuracy and 50 percent less time spent on managing product promotions. Yes, 50 percent more time. Just imagine what you could do with half your time magically available again.

Delivering a meaningful customer program that successfully targets individual customers with relevant offers can magnify the possible decisions by more than 4x. No retailer can afford to serve up generic campaigns in the era of one-to-one marketing, particularly in an industry that is so fiercely competitive. To maximize individual long-term customer value, automated, intelligent decision making is required to understand the combination of communications strategies, product availability, offer types, and marketing channels that are most ideal for any given customer.

Machine learning creates a single view of the customer to personalize offers and predict customer purchase intentions. This includes recommendations whether to target a customer via direct mail, SMS, email or social media today, while also flagging changing customer behaviors to optimize future campaigns. In three months, retailers can expect to see a 14 percent increase in revenue resulting from a 13 to 15 percent increase in cross-selling and upselling their products. AI is that good.

The retail sector has reached an “AI or Die” moment. Retailers that don’t adapt AI will soon die out. But those that embrace the technology to make automated decisions will enjoy an immediate uptick in their business, and should be able to transform themselves to survive. This is a big retail transformation and the only way to leverage it is understanding the benefits of big data.

 

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