Artificial Intelligence in Retail and eCommerce : Industry Use Cases

Kristjan Jansons
March 28th, 2019

eCommerce and Retail

AI in retail and e-commerce can be used to help meet customer demands and improve customer satisfaction. If you’re serious about creating a seamless shopping experience, you should leverage artificial intelligence to elevate customer service and distinguish yourself from traditional retailers.

 

It is more difficult than ever for brick and mortar stores and online retail to make meaningful connections with busy, distracted consumers. We believe artificial intelligence and machine learning can play a critical role in promoting customer engagement and increasing customer satisfaction.

“82% of consumers will not give a brand another chance after just one bad experience and over 90% will look to other brands for a more satisfactory experience”
Mary Meeker , Kleiner Perkins Internet Trends Report 2017

 

Customer behavior in the retail industry has changed considerably in recent years. Prospective buyers are becoming more conscious and base their buying decisions on factors far beyond the price of a product. Unlocking customer insights through artificial intelligence and machine learning is vital to breaking new ground in the retail sector. The retail industry is starting to embrace the potential of AI.

 

In recent years, there have been major shifts in technology and also in the amount of data retailers have access to. Business leaders can use raw data to power AI solutions and deliver personalized, customized, and localized experiences with shopping technology for customers. From price optimization to inventory management, AI can be used in all aspects of a retail business.

working online

 

E-commerce strategies can also be informed by artificial intelligence and machine learning. Many retailers are using AI in retail stores to generate actionable insights, resulting in increased sales figures and significant savings in operational expenses. We are also encouraged to see many retailers choosing to apply artificial intelligence to price forecasting – enabling them to adjust prices accordingly.

 

From manufacturing to post-sale customer service interactions, companies are now able to apply artificial intelligence across the entire retail product and service cycle. To influence buying decisions and anticipate future purchases, you should leverage AI in both retail stores and online. We believe there are clear use cases for predictive analytics in both online and physical stores.

 

As consumers look for more personalized shopping experiences, you can use predictive analytics and machine learning to enhance shopping experiences both online and in your retail stores. AI models will enable you to develop multiple pricing strategies for different buyer personas and provide specific product recommendations based on the needs of consumers.

 

Here are just some of the ways in which AI technology can be used across retail and e-commerce:

Without wasting any time, let’s break down each of these use cases for AI in retail and e-commerce.

Personalized product recommendations

91% of consumers are more likely to shop with brands that provide relevant product offers and recommendations. Personalized product recommendations can enhance consumer experiences and lead to more sales. Many retailers are using artificial intelligence to generate highly-relevant product recommendations for prospective customers visiting their e-commerce websites.

 

AI-powered recommender engines can make recommendations based on tens of thousands of data points. AI can find patterns in customer behavior from prior purchases and preferences. This allows retailers to personalize interactions with customers and provide more relevant experiences that drive increased conversion rates, average order value, and customer loyalty.

 

 

Intelligent recommender

 

This form of AI technology can also be used for personalized marketing and sales offers, location-based recommendations, and recommendations informed by recent activity. If you’re committed to delivering exceptional customer experiences, you should be leveraging AI-recommender engines as part of your e-commerce sales strategy. As more e-commerce stores embrace machine learning and artificial intelligence, customer expectations around personalized experiences are only likely to grow.

Assortment optimization

AI models are ideal for assortment optimization as they take a range of different variables into account, such as inventory capacity, past sales, local trends, and online behavior. Assortment optimization through machine learning will help to maximize revenue by identifying the right products to offer to specific groups of customers. This can also be used to support inventory control.

 

Pricing Optimization

With the support of AI in retail, you can pursue multiple pricing strategies at the same time. Various AI models can be used to determine the best price for different products based on seasonality and price elasticity along with real-time inputs on inventory levels and competitors. Pricing optimization can be used by retailers to increase demand for specific products and maximize profits where possible.

Rather than instructing “human employees” to run the numbers and account for many different variables, you can use artificial intelligence – as an extension of your workforce – to do the heavy lifting for you. To identify potential opportunities to increase sales and respond to seasonal trends, you should use emerging technologies for pricing optimization.

Customer Service Optimization

Responding to customers in a timely manner can put significant pressure on support teams. From answering pre-purchase questions to handling returns and exchanges, support teams can find themselves under considerable pressure at any given moment of the working day. Fortunately, there is a range of AI-powered interactive solutions on the market that can help to scale customer support.

benefits of chatbot

Handling the volume of customer contacts with headcount alone isn’t a feasible solution. AI can be used to automate aspects of customer service while identifying and nurturing highly valuable interactions with human staff members.

AI chatbots provide a scalable solution to service support. In some cases, you can automate nearly all customer service interactions with the support of an e-commerce chatbot.

It’s rare to find an e-commerce website that isn’t leveraging some form of chatbot to automate its sales process. As chatbots continue to become more sophisticated, you can train the AI-powered solution to answer common sales-related questions to boost conversions. When a chatbot is unable to provide an answer to a specific question, it should be able to forward interactions to your team.

Using machine learning, a chatbot can understand specific details, such as past behavior and website usage to provide a personalized customer experience. You can train an AI-powered solution with data sets from previous customer interactions to make the application more useful at providing exceptional customer service and support. Machine learning should make the application more useful over time.

Email bots help to automate email communication by analyzing the contents of the email and relevant data about the customer and enable to; automatically answer frequently asked questions, forward emails to the right agent the first time, prioritize emails, give urgent problems and sales emails the highest priority, get accurate statistics about the reasons customers contact you.

Call bots can be used if the customer decides to call your company directly. Option-based interactive voice response (IVR) can offer poor experiences as it takes time to connect with the right agent. AI-powered call bots can transcribe and analyze calls in real-time to ensure they’re forwarded to the right specialists. In some cases, call bots can answer common sales questions immediately.

Sales Flow Assistant

Combining the power of propensity modeling and a chatbot, or any other customer-facing channel, an intelligent sales flow assistant can predict consumer behavior. Based on these insights, the sales flow assistant can proactively feed potential buyers with the necessary information to keep things moving in the right direction and increase conversions.

From a helpful chat window to an app notification, AI-powered sales flow assistance can come in many different forms. In some cases, it will be as simple as a follow-up email. The method of communication will depend on the preferences of consumers and how aggressively you choose to pursue a sale. Sales and marketing automation is critical to converting website visitors from prospects to customers.

Customer Segmentation

The problem with customer segmentation is its reliance on human input, making it difficult to scale. Customer attributes are tagged manually and this can be an intensive and repetitive task. You can leverage AI solutions to remove customer segmentation tasks from your workflow. Many tools can create new segments by suggesting clusters of attributes that would not be readily apparent to humans when reviewing the same consumer data.

Product Categorization

AI can automate the process of product categorization and batch products based on attributes or by natural-language description. An AI-powered product categorization solution would not only improve efficiency but also the accuracy of the task with the support of natural language processing. Improved product categorization can make product searches easier for new customers.

Customer Journey Path Identification

Analyzing the customer journey through the purchase process is important as it can provide valuable information to a retail business. AI makes it easier than ever to identify the visitor by correlating their activities and attributes across the channels and devices. AI will gather different data points such as device ID, call logs, app usage, locations, check-ins, transactions, and reviews. The same visitor will be tracked through the journey and the path will be automatically formulated.

Propensity Model

By analyzing data from multiple sources, AI can predict customer behavior. For instance, an AI model may be used to predict how likely they are to register, open an email, purchase a product, or churn. Using this information, artificial intelligence can build a propensity model which can identify customers who will be more responsive to your marketing efforts, such as loyalty programs.

In the retail industry, predicting consumer behavior is essential to deploying the right marketing and sales strategies at the right time. The adoption of AI in retail shows no signs of slowing down. As more businesses begin to utilize data processing in the retail industry, you must integrate AI-powered solutions that are equally capable of predicting behavior.

Demand Forecasting

A retail business can use demand forecasting for both inventory management and supply chain management. Forecasting demand allows companies to improve efficiency for supply chain, manufacturing, and operations. Typically, demand forecasting is completed through statistical models. As technology evolves, larger pools of data can be analyzed using AI-powered tools.

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