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How AI And Machine Learning Can Transform The Customer Experience For Retail

  • Written by  Tim Tuttle, MindMeld

0aaTim Tuttle MindMeldAccording to IDC, the market for chatbots and conversational artificial intelligence (AI) platforms has exploded over the past year, with over 50 companies offering various tools and technologies. There are many reasons why retailers are jumping on the bandwagon to adopt these advanced technologies, but the most common is to improve the customer experience.  

The customer experience is your retail customers’ perception of how the company treats them. These perceptions impact their buying behaviors, build memories and feelings as well as drive brand loyalty. Simply stated, if your customers like you and continue to like you, they are going to do business with you and recommend your company and products to others. According to a Walker study, by the year 2020 customer experience will overtake price and product as the key brand differentiator.

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With this in mind, retailers are now investing in AI and machine learning technology to help create a more interactive and personal experience for consumers. For example, Gilt now uses deep learning to search for similar items of clothing with different features, like a longer sleeve or a different cut.

In addition, in 2016 Etsy bought Blackbird Technologies to apply the firm's image-recognition and natural language processing (NLP) to its search function.

UNIQLO, the global casual apparel retailer, has also taken a leadership position by offering its customers an expert, AI-powered assistant with deep knowledge about UNIQLO's product catalog, retail locations and more.

“As one of the world's leading fashion apparel companies, UNIQLO is committed to delivering the industry's gold standard for customer service and support," said Makoto Hoketsu, Group Senior Vice President and CTO at Uniqlo's parent company Fast Retailing.

The focus on natural language processing is dramatically growing as consumers are now accustomed to intelligent conversational assistance, fueled by the use and acceptance of consumer tools like Google Assistant, Siri, Microsoft Cortana and Amazon Alexa.

When looking at voice/AI-enabled application tools, the market is flooded with options and growing more competitive with every passing day. The need and desire for richer interfaces, insights, accuracy and efficiency are key concerns in the market for voice-enabled retail applications. There are numerous established and emerging vendors addressing and providing services and solutions within this space; these vendors offer a very wide range of capabilities.

When evaluating an AI platform, you should focus on ones that are specifically designed to meet enterprise needs, that can build and deploy production-quality conversational applications. The platform should be well suited for building large-vocabulary language understanding capabilities for any custom application domain.

Here are some specific technology capabilities to shop for:

  • Data collection tools. In contrast to machine learning toolkits that offer algorithms but little data, the AI tools should include algorithms and functionalities that streamline the retail data collection and management of large sets of custom training data.

  • Question/answer processing. While conversational AI platforms available today typically provide natural language processing support, you’ll want to select one that also assists with question/answering and dialogue management. This is extremely important for customer service issues in retail. It should provide end-to-end functionalities including advanced NLP, Q&A and direct messaging (DM), all three of which are required for production applications today.

  • Knowledge base creation. Nearly all production conversational applications rely on a comprehensive knowledge base to enhance intelligence and utility. The AI platform you select should support custom knowledge base creation. The technology must demonstrate deep understanding of a large product catalog, content library, or FAQ database, for example.

  • Data control. Unlike cloud-based natural language processing services, the AI platform should ensure that proprietary training data and models always remain within the control and ownership of the organization.

In conclusion, there are numerous vendors providing individual AI component technologies, but only a few of these vendors have robust cognitive/AI software platforms today that include all or most of the technologies needed to build a cognitive/AI-enabled application. There are even fewer companies that offer complete, robust conversational AI platforms. It's important to note that none of these technologies can individually create a successful conversational AI-based interface application. Rather, it's a combination of these technologies/services that results in a successful application.

Great conversational applications for retail require both advanced technology and solid design judgment. The most widely used conversational applications today, such as Siri, Alexa, Google Assistant and Cortana are all built using a similar set of techniques and technologies to ensure both high accuracy and utility.

Ultimately, retail companies should invest in a cognitive/AI solution that’s able to achieve the desired business outcome, like an improved customer experience, utilizing a cognitive/AI system such as a conversational AI platform.

The recent successes by Google Assistant, Siri, Microsoft Cortana and Amazon Alexa indicate a future where many voice and chat assistants will soon be the ubiquitous way to conduct all retail shopping. If you make sure these shoppers’ interactions with your company are smooth and pleasant, you will drive brand loyalty and ultimately keep your competitors from stealing them.


Tim Tuttle is the CEO and Founder of MindMeld, a leading provider of Conversational AI technology. He started his career at the MIT Artificial Intelligence Lab, where he received his PhD. Tuttle has also served on the research faculty at MIT as well as Bell Laboratories. His first company built the Internet’s first large-scale CDN for real-time data. His second company, Truveo, built the web’s second-largest video search platform, reaching over 70M monthly visitors, and was acquired by AOL. Tuttle served as Senior Vice President at AOL responsible for the Truveo business unit.

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