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AI Trends Shaping the Retail Industry

The key to retail success in today’s digital world is for retailers to understand where their most significant opportunity exists. Retailers need to anticipate their customers’ needs and competitors’ capabilities by adopting a digital platform. By using Artificial Intelligence (AI) in customer-facing functions, the online retail world can enhance customer satisfaction, increase time efficiency and reduce customer churn.

There are many examples of AI technologies that can aid in this effort. Visual Search can help by detecting and displaying a more tailored array of products related directly to the consumers’ aesthetic guide. Online Personalized Storefronts can improve customer satisfaction by creating a custom shopping experience for each shopper. Conversational Support can streamline the process promptly with 24/7 available support. The backbone of digital transformation is AI. No matter which journey they chose to follow, AI-powered retail experiences will become essential for retailers to meet their customers’ personalized needs.

Microsoft is stepping up to the plate by apply AI technologies to the retail/point-of-sale space. This multinational technology company has many Internet of Things (IoT) and AI services which potentially could apply to the retail space. By increasing its focus on IoT endpoints (sensors, embedded devices, etc.) and the cognitive services — such as image-processing/vision, face recognition, speech, and search — that can connect to these endpoints, they have provided a vast platform for AI to enrich the retail vertical.

Let’s take a closer look at AI Trends shaping the Retail Industry:

1. Visual Curation

Microsoft’s Visual Search API turns real-life browsing behaviors into digital retail by allowing customers to detect new or related products using visual-based search and analysis — making recommendations based on aesthetic and similarity.

One real-life example is Fashion retailer H&M who is rolling out a new e-commerce site and mobile application, explicitly aimed at its U.S. customers and equipped with visual search and other new capabilities.1 Visual search allows buyers to upload an inspirational photo leaving the platform’s search to for individualized, relevant items using this photo. In addition it aids the ease of e-commerce and traditional retail discovery by enabling consumers to search for products in the same way they think about it – using graphics, not text. It’s important to offer an initiative and interactive tools to help customers find what they look for without getting irritated.

2. Personalized Storefront

AI has become a crucial component in the digitalization of the retail vertical by personalizing the consumer experience and allowing an engaged business-to-consumer interaction. AI creates an opportunity to aid virtual and physical channels in retail companies. Retail locations under digital transformation are facilitating individualized customer experiences. It’s essential that these retailers successfully combine technology and function, which is why AI is at the forefront of personalized storefronts in e-commerce.

One example of bringing the in-store experience online is The North Face. Though e-commerce has seen large amounts of growth over the past few years, consumers still prefer to buy apparel items through brick-and-mortar. Traveling to physical locations allows consumers to try on different sizes, touch and feel the product and asses it’s other qualities. Brick-and-mortar stores also give their customers a level of customer service that currently cannot be achieved online, including recommendations from knowledgeable sales associates that are trained to know their products. The North Face is using AI and Machine Learning to work around this and make the in-store and online shopping experience more cohesive. The A.I. tool used acts as both a personal shopper and knowledgeable sales associate for the online consumer to help consumers find their perfect jacket through conversational Q&A. Instead of having to scroll through pages of jackets, the customer can start a conversation with North Face’s AI shopping assistant. It’ll ask questions such as “Where and when will you be using this jacket?” It with follow up by then asking who the jacket is for with varied questions. From this information it will be able to narrow down the options until the customer find the perfect jacket for their needs.2

Introducing personal shopping assistants online that can guide consumers via chat is a great way to help them find what they need while staying engaged in the process. However, this solution is costly at scale. This scalability problem can be solved with an AI-powered solution. The assistant will allow you to speak to it openly on the device that is being used during the shopping experience while helping the consumer engage in a question-and-answer conversation to help figure out exactly what they need. This is important because it allows the user to speak to the computer in the same way one would talk to a human being. It also eliminates friction often associated with online shopping. Additionally, Machine Learning (ML) models like the one created for The North Face continually learn as they are being used, meaning the program will become even more effective over time. After the first two months of the AI shopping experience being launched, and some 50,000 users, the platform had a 60% click-through rate and 75% total sales conversions. Also, three out of four users said they would use it again. Success? Definitely.3

AI-based e-commerce experiences can help alleviate a universal human condition that is exacerbated by online shopping: The Paradox of Choice. When confronted with too many options, consumers will opt most often not to purchase at all. AI can execute initial assistance, limiting the choices for the end user while still allowing them to be in control of the final decision. In this way the decision is split into two parts: AI assistant provides the best options from a technical standpoint, and the customer chooses among these the ones that best fit their style.

3. Conversational Support

Conversational Support is based off an AI technology component called Natural Language Processing. Natural language processing (NLP) is the ability of a computer program or machine to analyze, understand, and generate human speech as it is spoken. It allows Conversational AI to sound more humanized than robotic, therefore allowing the process to relate more the consumer on the front end. AI conversational assistants use natural language processing to help shoppers effortlessly navigate questions, FAQs or troubleshooting and redirect to a human expert when necessary—improving the customer experience by offering on-demand, 24/7 available support while streamlining customer service.

One company taking advantage of conversation support is Macy’s. Macy’s virtual agent delivers a conversational text-based interface support experience for customers. The system is connected on the back end to the internal system APIs so that it can access information about merchandise and orders to give customers real-time responses to common inquiries. Macy’s has an existing API and highly scalable services that power e-commerce functions around search, catalog, bag, order, and customer service, which simplified the fast development process.4

The API integration lets the virtual agent solve customer problems via customized responses which span from the following:

  • Order tracking
  • Local retail inventory searches for in-store pickup
  • Matching coupons and relevant discounts to current customer orders
  • Providing merchandise recommendations.

The goal is to promote customer service and engagement across all channels and empower the brand to deliver exceptional customer experiences.

The capacities of intellectual advances are consistently improving. Computer systems are not only showing signs of improvement at facial recognition and understanding speech, yet they are additionally getting to be able to read human sentiment. We will likely continue the application of Machine Learning improve the shopping experience, make products work better, and even understand and anticipate a customer’s wants and needs.

Cognitive technologies are destined to play a more significant role in how customers find, choose, purchase, receive, use, and get assistance with goods in the retail industry. These technologies are opening possibilities for many retail companies to make consumers’ lives more accessible and more straightforward, to pacify uncertainty and help increase confidence in customer purchases, and to help customers experience and even relate with products. As these technologies become more widely adopted, customer expectations and behaviors are likely to mature. Just as most consumers have come to expect high-quality online and mobile shopping experiences, they are also to anticipate the features and experiences that cognitive technologies provide increasingly.

[1] H&M powers up a new website, mobile app

[2] The North Face & Watson: Bringing the In-Store Experience

[3] Here’s How North Face Boosted Conversions Using AI

[4] What Retailers Can Learn from Macy’s Use of Microsoft

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