Artificial Intelligence in Retail and eCommerce : Industry Use Cases

Kristjan Jansons
March 28th, 2019

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eCommerce and Retail

There are countless ways in which Artificial Intelligence tools can be used to help retailers and brands improve their business and better serve their customers. It is more difficult than ever for brands and retailers to make meaningful connections with busy, distracted consumers. Everyone wants to catch people’s eye, start conversations with consumers, make people smile and help solve shoppers’ problems.


“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



Customers’ behavior has changed a lot in recent years, customers are becoming much more conscious and base their buying decisions on many factors beyond price. These new customers are led by millennials and this leads retailers to take action.


In recent years there have been major shifts in technology and also in the amount of data retailers have. More data means companies are much more capable of powering AI solutions and deliver personalized, customized and localized experiences to customers. Companies are now able to apply AI across the entire retail product and service cycle, from manufacturing to post-sale customer service interactions. AI will give retailers who use AI to its fullest potential possibility to influence purchases at the moment and anticipate future purchases. This enables to guide shoppers towards the right products in a regular and highly personalized manner.


AI use cases holding most value to the retail and eCommerce industry include:

  • Personalised product recommendations

  • Assortment optimisation

  • Pricing optimization

  • Next best offer (NBO)

  • Customer service optimisation

  • Product search

  • Sales flow assistant

  • Customer segmentation

  • Product categorisation

  • Customer journey path identification

  • Propensity model

  • Demand forecasting

Personalised product recommendations

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, demographics, preferences etc. This allows retailers to personalize interactions with customers and provide more relevant experiences that drive increased conversion rates, average order value, and customer loyalty. This area of machine learning can be used for personalized marketing and sales offers, location-based recommendations, recommendations based on recent/past activity, recommendations based on real-time activity and matching people with interests.

Assortment optimization

AI models are ideal to optimize assortment as different stores can have different customers, weather, display, and inventory capacity. Besides looking at previous factors AI can also look at a variety of factors like past sales, local trends or online behavior. This allows to sell products at full price and avoid stockouts as inventory can be sent before products run out.


Pricing optimization

Competing with online or omnichannel competitors companies need to better position themselves through careful price management. 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. This allows retailers to increase demand and maximize profits.

Next best offer (NBO)

Beneficial to both the client and retailer are sales opportunities – when the client has a specific need or problem that is best satisfied through an upsell or cross-sell. The key, though, is identifying the opportunity and then matching the right product or service to that customer’s need at the moment. Next best offer (NBO) model analyses customer information and sales data from a database and gives suggestions regarding products and services to offer the client. This allows companies to improve customer experience, so they are offering meaningful upsells or cross-sells. The recommendation model also understands the features and benefits of the product and aligns them with a complex customer profile, which takes into account network activity, consumption and usage patterns. Ultimately the accuracy of the recommendations increases successful sales.

Customer service optimisation

Managing a contact center is a difficult feat. The topics of customer inquiries range from tracking packages, answering pre-purchase questions to handling returns and exchanges. In addition, customers expect prompt responses. Research indicates that about 64% of consumers expect real-time responses at any given moment. 65% of consumers indicated that they are likely to switch brands if they receive inconsistent customer service across all platforms. Handling the volume of customer contacts with headcount alone isn’t a feasible solution and AI can be used to automate part of customer service while identifying and nurturing highly valuable customer interactions with customer service representatives.

Integrating chatbots with the website can help companies to automate 100% of the chat process. Chatbots can answer 60% of questions automatically, forward 20% of conversations to salespeople if sales opportunity is signaled and send 20% of complex, non-repetitive questions to the right agents the first time. Using a deep neural network, the chatbot understands specific customer details, like past behavior, website usage, and customer statistics to swiftly solve problems, giving customers a feeling of instant gratification.

EmailBots help to automate email communication by analysing 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, prioritise emails, giving urgent problems and sales emails the highest priority, get accurate statistics about the reasons customers contact you.

CallBots can be used if the customer decides to call the company. Currently used option-based intelligent voice response (IVR) can be frustrating as they take a lot of time to connect with the right agent. AI-powered CallBots can transcribe and analyze the call in real-time and forward it to the right specialist for the first time. If the question is simple or common enough, the CallBot can even answer the question immediately. Using CallBot helps to reduce customer waiting times, reduce hangups from customers, answer frequently asked questions automatically and cut unnecessary call time by accurately forwarding all calls.

Product search

Sales flow assistant

Combining the power of propensity modeling and a chatbot, or any other customer-facing channel, intelligent sales flow assistant predicts the next steps and questions customers may have as they interact with the brand,  then feeds them information proactively. It can be a helpful chat window that pops up, a follow-up email, or an app notification that gets pushed, depending on the likely preference of the customer. This helps to remove the guessing game out of sales and marketing automation and convert website visitors from prospects to customers, prevent customers from going to competitors, increase the close rate and reduce the duration of customers in the sales funnel and prevent people from turning to your customer service department by serving them relevant information proactively.

Customer segmentation

The problem with customer segmentation is that currently it is run mainly by labor. Customer attributes are tagged manually and can be an intensive task. AI solutions have the possibility to automate the process and increase the granularity of attributes and give better accuracy of assigning those to customers. Besides this AI is able to create new segments by suggesting clusters of attributes that would not be readily apparent to humans when reviewing the same customer data.

Product categorisation

Product categorization can be a difficulty with basic web development, AI can automate that process and categorize products based on attributes or by natural-language description, besides AI can also include images and video automatically. Such a solution not only improves efficiency but also the accuracy of the task and makes a highly granular image/video categorization possible. This makes the content search much easier for business and product search easier for customers.

Customer journey path identification

Analyzing the customer journey through the purchase process is important as it can provide valuable information to the 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 such as device ID, browsing, call logs, app usage, locations, check-ins, social media, coupons collected/used, transactions, reviews, etc. The same visitor will be tracked through the journey and the path will be formulated.

Propensity model

By analyzing data from multiple sources AI can predict customer behavior such as a show likely they will register, open an email, purchase a product, or churn. Using this information AI can build a propensity model which can identify customers who will be more responsive to organization marketing and loyalty programs.

Demand forecasting

Forecasting demand has always been one of the most important things for businesses. Forecasting demand allows companies to improve efficiency for supply chain, manufacturing, and operations. Usually, this is done by using statistical models and judgment by experts who are their area specialists. Instead of the traditional way, AI allows analyzing a much larger amount of data with much more accuracy compared to humans.


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Konstantin Sadekov

Konstantin has graduated from the Estonian Business School major in economics and finance and is currently doing his MBA degree in the USA. Before joining MindTitan he had international business management experience for more than 5 years and overall more than 9 years of international B2B sales and marketing experience

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konstantin sadekov mindtitan
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