Article updated on: 3 September 2024
The retail industry has always been one of the early adopters of new technologies, and artificial intelligence (AI) is no exception. As AI continues to advance, it has become a game-changer for retail leaders with its ability to automate tasks, analyze large datasets, and make data-driven decisions in real-time.
Since the announcement of ChatGPT, retailers have been buzzing with excitement about the potential of generative AI. The most forward-thinking ones are already experimenting with its capabilities.
These forward-thinking market leaders are not only data-driven but also focused on improving customer experience and optimizing operational efficiency. AI is helping them do both, with unprecedented precision and accuracy.
In this article, we’ll cover:
AI in the Retail Industry
Before we jump into the core topic of using AI in retail, let’s quickly go over the basics of artificial intelligence, generative AI and machine learning.
- Artificial intelligence (AI) is a fast-growing area of computer science that uses machines and advanced datasets to simulate human thinking and actions.
- Generative AI is a subset of AI that uses deep learning techniques to generate new content based on existing data. It is used for tasks such as creating images, text, and audio.
- Machine learning is a technique used in AI that enables machines to learn from data without being explicitly programmed. It allows for the development of algorithms that can continuously improve and make predictions or decisions based on new information.
From cashless stores and robot assistants to fully automated delivery drones – these technologies are driving cutting-edge innovation and transforming the retail industry.
For example, retailers can now leverage ML and AI technologies to better understand customers and market trends in order to forecast retail trends or provide a tailored shopping experience.
In addition to traditional AL and ML applications, generative AI is another exciting branch that is making waves in the retail sector. This technology can create new content, such as product descriptions, marketing materials, and even visual designs, based on existing data.
By harnessing generative AI, retailers can automate content creation, making their operations more efficient and their customer interactions more personalized.
For instance, companies are already using generative AI to produce unique, engaging product descriptions that cater to individual customer preferences – and stay ahead of the curve.
So, it’s no wonder that according to a recent report, 81% of retail decision-makers feel an urgency to adopt generative AI in their business.
Benefits of AI in Retail
AI technology has been around for a while, but we are just now starting to see its full effects – and the retail industry has been leading the charge. For instance, we can agree that retail is becoming a true battlefield for generative AI.
The main benefits of AI in retail are improved customer experience, reduced costs, and data-driven decision-making.
In this section, we’ll briefly cover each and explain how they’ll shape the retail industry.
Improved Customer Experience
Today, you need more than a great product to stay ahead of the competition. To secure brand recognition and customer loyalty, you must provide a great customer experience. And to make things more complicated, customers are using more channels than ever to interact with a business.
To provide this experience, retailers can now create highly tailored content and AI-powered recommendations based on individual customer preferences. On the other hand, customers are now starting to expect this level of personalization as a standard part of their shopping experience.
Another way to leverage AI technologies in retail is through genAI-powered chatbots. These chatbots enable customers to obtain product information and connect with a brand without enduring long wait times – all while using conversational language. By automating responses to repetitive inquiries, chatbots free up agents’ time, allowing them to focus on complex tasks that bring high value or pose high risk.
But chatbots are not the only way generative AI (genAI) has been revolutionizing the customer service landscape. The technology also empowers agents to respond more quickly and efficiently and offers in-depth analyses and learning opportunities. This advanced technology significantly improves the quality of customer interactions, ultimately elevating the customer experience.
Reduced Costs
Advances in artificial intelligence affect retailer profits not only by driving sales but also by lowering costs. So, it’s not surprising that – according to Accenture – AI has the potential to boost profitability rates by an average of 38% by 2035 and drive an economic boost of $14 trillion across 16 industries.
For example, using AI-powered algorithms to optimize delivery routes can greatly decrease transportation costs for retailers. Similarly, automating the inventory management process with AI can reduce the risk of overstocking or stock shortages, leading to more efficient use of resources and cost savings.
Another example of cost reduction with generative AI is product data enrichment. Instead of manually creating product descriptions, retailers can use generative AI to create unique, personalized content for each product. This saves time and resources while allowing product teams to focus on high-value tasks instead.
Data-Driven Decision-Making
Retailers collect vast amounts of data every day, but due to its size, it isn’t easy to analyze it and extract value. However, with AI technologies, retailers can make data-driven decisions by generating real-time actionable insights – moving them toward more effective outcomes.
You can now use machine learning algorithms to process, filter, and analyze data from multiple sources as it comes in, transforming your big data into smart data.
In doing so, you can visualize the most likely outcomes of specific strategies, promotions, and initiatives, helping you predict and take advantage of market changes. With its ability to continuously learn, genAI can analyze data from past activities, such as campaigns, to predict the success of future ones, allowing retailers to make data-driven decisions and continuously improve their strategies. Ultimately, it is no wonder that 75% of retail decision-makers say they believe generative AI is a key way retailers can reinvent themselves, according to a recent report.
AI Use Cases in Retail
Even though artificial intelligence is becoming widespread, many retailers still don’t understand the underlying technology or how they can use it to differentiate. That could discourage them from implementing AI solutions, running the risk of being left behind.
Because of that, we think it’s crucial to dive deeper into AI’s real-life application. This is why we’re sharing examples of the most common AI use cases in retail: personalization, product search, AI-powered contact center, inventory and delivery management, and demand forecasting.
AI-powered Personalization
Thanks to advanced machine learning algorithms, it’s never been easier to provide a personalized shopping experience. By leveraging natural language processing (NLP) and machine learning algorithms, genAI can analyze customers’ demographic and past behavior and continuously learn from interactions. This provides an unmatched ability to anticipate customer needs and preferences over time and recommend products tailored to specific customer needs.
For example, Google Cloud Retail Discovery solution includes a ‘Recommendations AI’ module which offers highly personalized product recommendations based on customers’ past purchases and behaviors, context, and a product’s overall popularity. This means retailers can now show different product descriptions or images depending on the customer, resulting in higher relevance and engagement.
Artificial intelligence allows brands to implement strategic initiatives, such as conversational commerce. This new level of personalization not only enhances customer satisfaction but also drives higher conversion rates and fosters stronger brand loyalty.
Price Optimization
Another strategy retailers can implement with AI tools is dynamic pricing. By analyzing vast amounts of data, AI empowers retailers to adjust prices based on various factors, such as demand, competition, customer behavior, and market trends.
This allows them to enhance their profitability by maximizing sales during peak demand and minimizing losses during slow periods. Moreover, this approach ensures competitive pricing, attracting more customers and increasing market share.
Generative AI further enhances this by offering predictive insights and simulating various pricing scenarios. For instance, AI can analyze past sales data and market conditions to predict the best pricing strategy for an upcoming holiday season, helping retailers maximize revenue while maintaining customer satisfaction.
AI-powered Contact Center
Modern contact centers are becoming increasingly complex as they need to engage with customers across channels but also offer tailored recommendations and tech support.
This is where AI comes into play. AI-powered chatbots can communicate with customers in natural language, understand their inquiries, and recommend products and solutions accordingly.
With generative AI, contact centers can also utilize predictive analytics to anticipate potential issues and proactively provide assistance before the customer reaches out. This not only saves time for both the customer and the retailer but also enhances customer satisfaction by showing that the company cares about their needs.
Inventory and Delivery Management
AI can be a powerful tool for a retailer thanks to its ability to optimize promotional planning, pricing, supply chain operations, or delivery routes.
For example, once a customer orders a product online, an AI tool can select the optimal warehouse based on its location and stock, calculate the best delivery route, and then provide the customer with an approximate delivery time. This not only serves to cut costs but has great potential to greatly improve the customer experience.
In addition, with the help of AI tools, retailers can also manage delivery routes to reduce travel duration and decrease fuel usage, significantly influencing the bottom line.
Demand Forecasting
Demand predictions are vital in understanding consumers’ willingness to buy specific items and products at a particular price and timeframe. Thanks to that, you can identify optimal prices for each stock-keeping unit (SKU), set accurate and achievable targets, and stock sufficient supplies to maximize your sales for peak periods such as Black Friday.
With generative AI, retailers can now forecast future demand with remarkable accuracy. This allows them to maintain optimal stock levels, reducing the risk of overstocking or stockouts. Additionally, AI can analyze external factors such as weather, social media trends, and economic conditions to adjust demand predictions in real-time, providing retailers with the necessary insights to make data-driven decisions.
Reinvent Your Retail Experience with the Power of AI
To summarize, AI can be a powerful tool for retailers, with benefits ranging from automating great customer experiences to giving you smart data on which to base your decisions. Across the globe, retailers are getting started with generative AI to gain actionable insights and optimize operations.
However, before you can take advantage of advanced artificial intelligence solutions, you need to unify your data and remove any data silos present across your business. Google Cloud BigQuery is a cloud-based data warehouse that gives you a unified view of your data. It has built-in machine-learning capabilities, making it an ideal first step in your data-driven journey.
One thing is certain – once you have your data unified, AI tools will empower you to improve your data analytics and make informed decisions. The best way to then continue and understand what kind of AI-based solution will provide tangible results to your business is through a tailored workshop, designed to guide you step-by-step through the discovery process.
