COMMENTARY

The time is now for AI in ecommerce

July 28, 2017

Photo: iStock.com

By Eldar Sadikov, CEO and co-founder, Jetlore

Slowly but surely, artificial intelligence is making an impact on the average consumer's daily life.

Siri, Amazon Echo, Google Home, etc. have made AI mainstream, but what if we could make retail shopping with AI mainstream as well?  For example, imagine walking into your favorite retailer and having the store not only recognize that you're there, but then direct you to particular styles, brands, and products based on your unique user profile. Sound too good to be true? It's coming — the time is now for AI in ecommerce.

IDC says that by 2020, worldwide spending on AI will reach $46 billion, up 768 percent from 2016. There is tremendous competition in the retail industry, so chances are a brand's rivals are already developing an AI strategy for their business. This means CMOs, CTOs and chief digital officers need to become familiar with different approaches to using AI to reap the benefits and build a structured, holistic and more predictive approach to their consumer understanding.

AI's potential within ecommerce is massive. As more browsing, research and purchases take place online, retail brands now know much more about shoppers than they ever could from brick-and-mortar stores alone. So, with all this consumer data, how should brands take advantage of it? One way is to use AI to make smarter merchandising decisions. In 2013,Gartner reported that fewer than five percent of ecommerce companies were using big data or predictive analytics software, but that number is on the upswing. Brands need to capitalize on the customer and purchase data available to them.

Retailers today need to implement new technologies in order to future-proof their business, and AI should be at the top of that list. Ecommerce doesn't lack ad hoc machine learning applications like up-sell and cross-sell recommendations or dynamic creative optimization. However, what ecommerce does lack is true AI that can aggregate and analyze thousands of attributes associated with millions of consumers with probabilities updated in real time.

Ecommerce needs AI to interpret consumer behavior and map it to structured data of predictive attributes, such as size, fit, color, brand preferences, price range, etc., to create a real and actionable customer relationship management (CRM) system. This holistic approach to understanding consumer preferences opens a whole slew of new and interesting applications for retailers from predictive merchandising to dynamic re-ranking of product listing pages to in-store personal digital concierges, etc.

Here are the top three benefits of implementing AI in ecommerce:

1.           Enable smarter merchandising and inventory planning

Decisions have to be done quantitatively across millions of data points, across millions of consumers. Merchandising teams need to be empowered with data and tools that can aggregate and compute such data. It cannot be just a log or historical summary of consumer behavior, but rather provide predictions of what will happen in the future based on millions of unique attributes for each individual consumer. 

2.           Create a personal concierge experience

Consumers can change their preferences on an hourly basis. Today a woman might be looking for pink pants; tomorrow she might want a blue dress. Yet there are certain preferences that are sticky like a woman's size or her brand preferences or the fact that she likes three-quarter sleeves. Retailers need AI to take all this consumer behavior, map it to predictive attributes, and compute this data in real time. When a brand provides an optimized and unique consumer experience, the likelihood of customer engagement goes up. When consumers feel a connection with a brand on a more personal level, the probability they will turn into loyal customers increases.

3.           Use AI-computed data to generate more revenue

If you are computing aggregate demand data across all channels and building unique attribute data for each consumer, then you can create new revenue opportunities across the board by empowering each channel with this data. For example, your search can become smarter because it understands aggregate demand for different products and understands individual consumer preferences at the attribute level, like size, fit, color, brand preferences, etc. Investment in AI leads to exponential growth in revenue, where one investment can have a ripple effect on multiple channels.

Taking advantage of AI technology is the only way a brand will be able to differentiate itself and win in the hyper-competitive modern retail world.

As today's consumers become more sophisticated and have higher expectations of how brands target them, using structured data based on predictive attributes will be key for retailers. Some players like Nordstrom Rack have already begun using structured data based on consumers' predictive attributes to properly target customers.

We'll see this adoption increase among other retailers moving forward. Artificial intelligence-powered predictions can provide a better shopping experience for your consumers, provide new insights about overall demand and trends, and ultimately result in new revenue opportunities for the retailer. It would be foolish to pass on it.


Topics: Assisted Selling, Connectivity, Consumer Behavior, Customer Experience, Customer Service, Marketing, Merchandising, Robotics / AI, Shopper Marketing, Technology


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