Preparing to catch the retail Generative AI wave

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While murmurings of Generative AI had been humming in conversations for a number of years, on November 30, 2022, ChatGPT entered the public domain, changing hypothetical discussion into tangible reality. Seemingly overnight Generative AI moved from “what if?” to “what now?” as more AI exploded onto the scene — from chatbots and writing assistants to code generation and image generation.

By definition, Generative AI is essentially any artificial intelligence that can rapidly “generate” new content or products based on its cumulative synthesis of existing data pools. With the the number of Generative AI options now materializing, every industry is scrambling to brainstorm, develop, or access potential AI that can improve processes and customer satisfaction. What this will look like for retail is still a hot topic of conversation.

A report published by IHL Group in May 2023 estimates the worldwide retail economic impact of AI through 2029 will land around 9.2 trillion USD. According to the report, larger retailers are set to benefit most from AI due, in part, to having scalable data-centric governance models that ensure clean datasets.

Another form of AI, Artificial General Intelligence (AGI), also is on the horizon, speculated to debut in the next decade. The goal of AGI is to perform human intellectual tasks, which for retail could be groundbreaking by automating operational processes at unprecedented levels.

How clean transactional data supports AI integration

What has become abundantly clear is the necessity for all retailers, large and small, to ensure they have a strong data and testing backbone so that as AI integrations become available, retailers can hit the ground running. Clean transactional data will be essential for AI systems to succeed in the retail industry. It enables accurate personalized product recommendations and customer segmentation, improved demand forecasting, fraud detection, and optimized pricing strategies. By leveraging AI capabilities with clean transactional data, retailers will be poised to gain valuable insights, improve decision-making, enhance customer experiences, and drive business growth.

How strong is your test and learn backbone? Take a retail testing maturity assessment here.

Provide personalized recommendations

Generative AI can analyze customer data, including purchase history, preferences, and demographics, to generate personalized recommendations. By understanding individual customers’ interests and needs, retailers can offer tailored suggestions and improve the overall shopping experience.

Clean transactional data enables AI-powered recommendation systems to generate more accurate and relevant product recommendations, which not only improves the customer experience by offering relevant suggestions but also increases the likelihood of cross-selling and upselling, thereby driving revenue and customer loyalty.

Additionally, clean transactional data allows retailers to segment their customers based on their purchasing patterns, preferences, and behaviors. AI algorithms can analyze this data to identify distinct customer segments, such as high-value customers, frequent buyers, or occasional shoppers. Accurate segmentation enables retailers to tailor their marketing strategies, personalize product recommendations, and provide targeted promotions that enhance customer satisfaction and driving sales.

Nike remains one of the trailblazers here, utilizing AI to create 3-D shoe images, to help customers find the right fit, and to build a firm base in data mining and analytics.

Optimize visual merchandising

AI can generate virtual visual merchandising displays and online product presentations that showcase products in appealing ways. Retailers can use generative AI to simulate different store layouts and window displays, helping them optimize store designs and product displays to attract customers.

Transactional data can be used to evaluate the effectiveness of different visual merchandising strategies through A/B testing. By comparing the performance of different store layouts, product placements, or display designs, retailers can identify which approaches lead to higher sales and customer engagement. AI algorithms can analyze transactional data in real-time and provide insights on which visual merchandising elements are most effective for specific products, locations, or customer segments. This iterative optimization process allows retailers to continuously refine their visual merchandising strategies for maximum impact.

Levi Strauss & Co recently made the news with its innovative approach to online visual merchandising, partnering with to create diversity in online models.

Evolve your inventory management

AI algorithms can analyze historical sales data and current market trends to predict demand and optimize inventory management. By accurately forecasting demand, retailers can reduce overstocking and avoid out-of-stock situations, leading to improved customer satisfaction and increased sales.

Transactional data provides insights into these historical sales patterns. Clean transactional data ensures that the forecasting models are based on reliable information, resulting in more finite predictions of product demand. This helps retailers optimize their inventory management, reduce stockouts, minimize excess inventory, and enhance overall operational efficiency.

Shelf Engine is an industry leader utilizing AI to generate intelligent forecasting, reduce waste, and optimize ordering.

Create Augmented Reality (AR) experiences

Generative AI can contribute to creating AR experiences in physical stores. By integrating AI with AR technologies, retailers can provide interactive product demonstrations, virtual fitting rooms, or virtual interior design experiences, allowing customers to visualize products in their intended environment.

Clean transactional data contains details about customers’ past purchases, such as product specifications, features, and usage patterns. AI algorithms can leverage this data to provide contextual information within AR experiences. For instance, when a customer points their smartphone at a product using AR, AI can overlay relevant information, such as pricing, reviews, or complementary items, based on the customer’s transaction history. This contextual information enhances the customer’s understanding of the product and facilitates informed purchasing decisions.

IKEA is potentially the best known trailblazer when it comes to AR, being one of the first to enable customers to see how products would look in their homes.

An image of ChatGPT on a computer screen. Chatbots are one of many retail Generative AI resources.

Rely on chatbots and virtual assistants

Retailers can employ generative AI-powered chatbots and virtual assistants to handle customer inquiries and provide real-time assistance. These AI systems can answer frequently asked questions, help customers find products, and offer personalized support, even in physical stores through kiosks or mobile apps.

Virtual assistants can utilize clean transactional data to assist customers with support requests and issue resolution. By analyzing past transactional data, virtual assistants can quickly access relevant information to address customer inquiries, such as order status, return processes, or product warranties. This ensures efficient and accurate support, enhancing customer satisfaction.

Coca-Cola is one of the first to jump on the boat here, partnering with both Dall-E and ChatGPT to enhance its marketing and CX strategies.

Develop dynamic pricing

AI algorithms can evaluate various factors such as inventory levels, competitor pricing, and customer demand to optimize pricing strategies. Generative AI can help retailers dynamically adjust prices in real-time, enhancing revenue and profitability.

Clean transactional data allows AI algorithms to analyze price elasticity and customer buying behavior. By understanding how price changes affect customer purchasing decisions, retailers can optimize their pricing strategies to maximize revenue and profitability. AI-powered pricing models can dynamically adjust prices based on factors like demand, competition, and customer preferences, ensuring retailers stay competitive and increase their profit margins.

Albertson’s has implemented AI to stay apprised of pricing fluctuations and adjust or scale pricing in response to those fluctuations. Academy Sports utilizes Revionics for AI price modeling, allowing the sports retailer to assess if a price reduction in one category might motivate buyers to purchase a complementary basket item at a higher price.

Ensure loss prevention

AI-powered systems can analyze in-store video surveillance footage to detect suspicious behavior and prevent theft. Generative AI can enhance these systems by providing more accurate and efficient object recognition, reducing false positives, and enabling proactive loss prevention measures.

Clean transactional data is also essential for detecting and preventing fraudulent activities. In continuously monitoring and analyzing transactional patterns, AI algorithms can identify anomalies that may indicate fraudulent behavior, such as unusual purchasing patterns or transactions outside the customer’s typical behavior.

Chooch and Lenovo recently partnered at NRF to discuss the potential of AI in loss prevention. NVIDIA is also an emerging player in the field, working to curb the influx in shoplifting that has been plaguing retailers in the wake of inflation.

The need for clean transactional data and a culture of data-centric test and learn has never been more essential. At MarketDial, we not only clean and maintain your data, we offer a comprehensive test and learn platform and BasketAnalyzer tool to maximize transactional data learnings. Additionally, our client success team is well equipped to guide you to developing into a mature retail organization. Take the retail maturity assessment here.

To dive deeper check out these resources:

Key drivers for developing into a mature testing organization
Leveraging test and learn to combat theft in stores
Enhance decision making with test and learn software

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