Artificial intelligence is transforming every industry that uses it, and the potential of AI solutions for retail is enormous. At the tactical level, it can enhance the customer and employee experience, streamline operations and reduce costs.
Smart retail business leaders leverage AI at the strategic level as well. They combine insights into customer attitudes with data into supply-chain fluctuations to understand how demand could grow (or be induced to grow) over the long term. Then they leverage those insights to design new products, take them to market more quickly, and drive revenue more consistently.
The result is a more efficient, sustainable and innovative retail landscape for everyone.
Practical applications for generative AI in retail are almost limitless. They include product design, content generation, personalized marketing, product recommendations, inventory management and many others that are yet to be uncovered.
Any of these use cases can help retailers engage customers more deeply, drive revenue, cut costs and streamline operations. AI technology in retail can also generate income streams on its own. Retail companies can resell the data on which AI applications depend, they can sell the insights they provide, and the applications themselves can be sold to companies in other adjacent vertical markets or value-chain partners. In time, this cross-pollination will become unavoidable, and companies that fail to leverage generative AI will struggle to keep up with those that do.
The best way to display products in a retail setting is the one that drives the most revenue. But how can you determine which displays perform in that way? For years, AI has leveraged rapid testing of hypotheses by combining data science, hyperautomation and structured data. The latest algorithms and technologies have accelerated that, but now generative AI can also design the hypotheses that it tests. It automates the tests themselves by incorporating PDFs, documents and other unstructured data into its tests.
These AI models enable retailers to incorporate a wide range of factors into each hypothesis, then adjust online interfaces and other smart displays in near real-time based on the results. If a trend happens to drive sales of a specific item, retailers can adjust quickly to capitalize on it and drive sales of related merchandise and services.
Until now, much of the focus about generative AI in retail has been using it to create images and text for product descriptions, blogs, social media and other promotional channels. This has led to speculation about how Gen AI will affect the relationships between brands, agencies and other vendors, potentially impacting job opportunities. Some agencies have already begun to restructure themselves as a result.
It also has raised some questions and lessons from launching and scaling retail media. Ultimately, the important thing to remember when using generative AI use cases in retail is to keep the customer in mind. Everything that’s being done, from personalized ads to social posts are for the customers. While using AI for content creation is a great way for retailers to save time and resources, it’s important to never forget about the customer and what they would like to see. Human oversight of AI platforms and automation is necessary to keep your content focused on the customer.
Improving the customer experience is one of the most critical use cases for AI when it comes to retail transformation. AI-enabled platforms enable marketers to parse enormous amounts of data on customers’ past purchasing behavior, preferences and other tendencies much more quickly than a human ever could.
These platforms use insights from the data to shrink segments and audiences into ever-smaller groups, then map the customer journey across touchpoints in real time. Such maps help marketers offer relevant information at the exact moment the user needs it — or just before. AI models can then enhance the shopping experience with tailored marketing content, product recommendations, pricing and offers for specific customers.
After the sale, generative AI models can give employees access to larger knowledge bases to provide support and drive repeat sales. Personalizing marketing campaigns in this way can improve customer engagement, increase conversion rates, drive brand loyalty and increase customer lifetime value. Even in-store advertising strategies can be improved by capturing retail media insights based on various data points and trends.
This holistic AI transformation requires a solid data foundation, starting with an advanced customer data platform (CDP) to enable attribution modeling. Once the brand understands which touchpoints are the most effective, and which specific conditions are required, the brand can make smarter decisions about how to manage their retail business.
Smart retail business leaders leverage AI at the strategic level as well.
Generative AI models analyze customer buying history and preferences to help recommend relevant products to shoppers. This can help increase sales by guiding customers to items they are more likely to purchase, as well as improve customer satisfaction by providing a tailored shopping experience.
An example of this is when you’re shopping online and see suggested items that are similar, either in clothing style to pieces you’ve purchased or reviewed recently or in similar patterns or colors. With AI for retail, you can analyze and make these recommendations easily to help increase your bottom line.
Personalization ensures that the information contained in any communication is relevant to users and meets their needs. No salutation can personalize irrelevant information enough to make it relevant, but information that arrives at the right time and in the right place will be valuable to the user and the brand.
If the brand constantly increases the relevance of the information and support it provides, people will engage with it more deeply and authentically. Over time, this drives customer satisfaction and ultimately customer lifetime value.
Generative AI presents a significant opportunity for retailers to enhance customer experiences and streamline operations.
Delivering a great customer experience is more important than ever when things go wrong. There are a lot of reasons a customer may have an issue with a brand, such as a missing product from their order or a delayed order. When this arises, it’s important to try to help the customer quickly and efficiently.
Before the customer escalates to the call center, AI-enabled bots can help customers find self-service solutions. If the issue reaches the human-intervention stage, AI tools in the call center can help employees quickly and smoothly find the next best action and dig into knowledge bases to resolve issues.
Generative AI presents a significant opportunity for retailers to enhance customer experiences and streamline operations. AI confers strategic advantages that drive growth and innovation, and when used wisely, can enable brands to extend lines of business and find new revenue streams.
Finding the value that lies hidden in data requires solid platforms, the right cloud upgrades, good governance and people who know how to use those things. This talent is rare in-house, so brands usually need to upskill their workers using programs like Sand Academy and invest in partnerships with AI solution providers.
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