How Machine Learning Will Improve Retail and Customer Service

by   |   April 15, 2016 5:30 am   |   0 Comments

John O’Rourke, Vice President of Marketing, Indix

John O’Rourke, Vice President of Marketing, Indix

Technology has transformed how customers and brands interact with each other. Shoppers once relied on face-to-face, in-store interactions to make purchases and receive support. Now, shoppers do their research before entering a store (81 percent of shoppers conduct online research before buying) and seldom rely on salespeople to help them make decisions. Retailers, for their part, have realized that by embracing technology, they can extend their storefronts to their customers’ fingertips.

The Internet, buy buttons, mobile payment apps such as Square and Venmo, and couponing and price-matching apps like SnipSnap have changed how we shop. Shoppers can make purchases from within social media apps and compare prices without leaving a store. While these technologies have propelled the retail industry further into the digital age, technology that is still evolving will have the largest impact on the future of the customer service and retail industries.

Embracing Big Data

More retailers are tracking customer shopping habits through data sources such as social media, purchase history, consumer demand, and market trends. By relying on big data technology to gain a deep understanding of shoppers and their buying trends, retailers can maximize customers’ spending and encourage customer loyalty.

According to research by Accenture Analytics, 58 percent of retailers described big data as “extremely important” to their organizations, while 36 percent called it “important.” Additionally, 70 percent said that big data is necessary to maintain competitiveness, and 82 percent agreed that big data is changing how they interact with and relate to customers. While most retailers recognize that there is power in big data and analytics as it pertains to shoppers and their purchasing habits, few have unlocked the true potential of that data through machine learning.

Matching Products with People

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Machine learning technology amplifies and extends the reach of big data analytics and can help create an exceptional shopping experience. Innovative retailers can tap into the power of machine learning algorithms to do things like determine available products from outside vendors or recommend the quantity, price, shelf placement, and marketing channel that would reach the right customer in a particular area.

Applications of this are already being seen. The North Face leveraged IBM’s Watson natural-language processing machine learning system to create the Expert Personal Shopper. So when you are in the Jackets and Vests section of the North Face website, you don’t have to get overwhelmed by the choices. You can just type, “I’m going on a cabin trip to Iceland in December.” Conflating its trove of product information with weather and other data, the app will surface the right product for you. The technology is relatively new, but you can imagine the implications it will have in the future.

Further, the ability to automate everything through advanced analytics and machine learning soon will mean that basic customer service will be performed by bots that can predict our needs and provide service in the fastest, most immediate way possible: by offering us items we didn’t know we needed. As retailers gain more insight into their customers and products, machine learning will be able to match buyers and sellers based on buyers’ needs and product availability.

Shopping is becoming increasingly programmatic. In the future, services like digital assistants (Siri, Cortana, Facebook’s M) will learn more about us and offer us relevant and personalized product offers. Say, for example, you use a particular brand of razor. Your digital assistant will learn your shopping and usage habits and offer you the best deal on the product at the right time. It might even place the order for you.

Improving the Backend

Machine learning and advanced analytics will not only change how we shop and provide customer service, but also simplify how retailers perform basic operations. Data science and machine learning give us the ability to automate so much of the heavy lifting required to find insight within heaps of data. With these tools, retailers can find useable and useful data to change the shopping experience for consumers.

Technology enables us to create an index of every product in the world, enabling retailers to offer customers the best prices, keep products adequately stocked, and track competitors’ minimum-advertised-price  violations. A central database of the world’s product information enables retailers to offer the best shopping experience for buyers.

An innovative-technology approach to customer service and commerce will combine data about our behaviors and choices with data about products and product attributes to create the optimal shopping experience. This approach takes the guesswork out of purchasing and makes the shopping experience more enjoyable for everyone.

John O’Rourke is Vice President of Marketing at Indix. John built his career launching and creating excitement for technology products and services. From his very first job as a product manager on Microsoft Publisher, to his leadership roles on Microsoft Office, Xbox, Windows Mobile, Motricity and Intermec, John’s focus centers around understanding the needs of the customer and building the teams and marketing programs needed to create brand value and deep customer connection. John is a native of the Pacific Northwest and earned his undergraduate degree and MBA from the University of Washington. When he’s not working, John enjoys spending time with his family, golfing, playing guitar, and cycling.

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