Partner POV | How generative AI and data are redefining retail experiences

This article was written by Anu Kaggadasapura Nagaraja, Solutions Architect at AWS, Brendan Jenkins, Solutions Architect at AWS, Esther Kang, Associate Solutions Architect at AWS, and Parker Bradshaw, Enterprise SA at AWS. It was submitted for publication by our partner, AWS.

The retail and consumer goods landscape is shifting seismically, with digital transformation at its core. Retailers and consumer brands are at various stages of this digital journey, each seeking tailored solutions to propel their businesses forward. To kick-start digital transformation, organizations need data-based insights that help them deliver on business outcomes. Accessing and acting on these insights is simpler than ever because of technologies such as data lakes, machine learning (ML), and artificial intelligence (AI).

Elevating retail to new heights with generative AI

In the dynamic world of retail and consumer goods, generative AI is a catalyst for transformative change. It's transforming how companies interact with customers, manage operations, and drive growth. The potential of generative AI is incredibly exciting, with Goldman Sachs forecasting a 7-percent increase in global GDP and productivity growth of 1.5 percentage points over the span of 10 years.

Automating product marketing

Retailers and consumer goods brands can use Amazon Bedrock, large language models (LLMs), and multi-modal models for intelligent product analysis and descriptive writing. These models can ensure product accuracy, as well as optimize product descriptions for search engine optimization across online product portfolios.

Take The Very Group (TVG), for example. TVG, a leading multi-brand online retailer in UK, offers customers access to thousands of products—across categories such as clothing, homeware, and electronics—through its popular ecommerce websites. It transformed its product-development process by developing a generative AI system to bolster TVG development. The system uses Amazon Bedrock, LLMs, and multi-modal models to analyze products and create content for TVG copywriters. This improved productivity by condensing development-processing times and increasing the quality of product descriptions.

Empowering Customer Service

Retailers are also striving to improve the customer experience both online and in stores. In a 2021 Qualtrics survey, 80 percent of customer respondents said they've switched brands because of a poor customer experience. Behind the scenes, AI-powered chatbots and customer service solutions are redefining personalized consumer interactions. With Amazon Web Services (AWS) contact center solutions, organizations can use chatbots to assist web customers almost instantly, improving customer satisfaction while reducing operational costs. And with Amazon Q in Connect, a generative AI assistant suggests agent responses and actions to address customer questions, providing faster issue resolution and improved customer experience.

As Brian Dick, Senior Manager of Customer Care at Orbit Irrigation, said, "To resolve customers' questions, our agents spend two to three minutes per interaction searching through several different sources of knowledge…. Amazon Q in Connect will create 10–15-percent time savings on every contact, and the increased number of calls handled every hour is expected to translate directly into costs savings for Orbit." This personalized approach not only boosts customer satisfaction, but also cultivates loyalty.

Another company revitalizing customer service is DoorDash. In early 2024, DoorDash deployed generative AI–powered self-service in its Amazon Connect contact center. DoorDash used generative AI to strengthen its customer support experience, particularly for Dashers, its delivery drivers. Faced with a high volume of requests from consumers, merchants, and Dashers, the company sought to improve its self-service options. DoorDash collaborated with AWS in its Generative AI Innovation Center, developing a voice-operated, self-service contact center solution within two months.

Using Amazon Connect and Amazon Lex, DoorDash created an interactive voice response system that has reduced agent transfers by 49 percent. DoorDash also increased first-contact resolution by 12 percent, leading to better CX and $3 million in annual operational savings. However, many calls still required live agent assistance, prompting DoorDash to further enhance self-service capabilities.

Since Dashers prefer phone support for quick help while on the road, DoorDash focused on shortening response times. It implemented generative AI through Amazon Bedrock, which helped answer common questions quickly, improving efficiency and trust in self-service. This initiative not only streamlined support, but also reinforced DoorDash's commitment to empowering local economies and enhancing CX for its user base of more than 37 million consumers and 2 million Dashers.

Hyper-personalizing consumer experiences

Retailers are using Amazon Bedrock to create hyper-personalized shopping experiences. Hyper-personalized consumer experiences guide customers to desired products with unprecedented accuracy and engagement. With generative AI, these businesses are transforming their online storefronts—offering better tailored product recommendations, dynamic content generation, and intelligent search capabilities. The effects are significant. McKinsey reported in 2021 that hyper-personalization can lift revenue by 10–15 percent on average, while company-specific revenue increases range from 5 to 25 percent.

There are other great examples. Buy with Prime is pursuing hyper-personalization with Amazon Bedrock Retrieval Augmented Generation (RAG). It uses RAG to power a chatbot solution that handles product support queries, surpassing traditional email-based support.

These innovations reflect a trend that's yielding impressive results: 77 percent of companies that personalize B2B experiences report increased market share, and nearly three-quarters of consumers say they won't complete a purchase without personalization. Furthermore, personalization can lower customer acquisition costs by as much as 50 percent. These examples and statistics demonstrate the power and promise of generative AI. Consumers are already benefitting from its use with more engaging, efficient, and personalized experiences throughout their shopping journey.

Transforming retail with advanced data insights

Generative AI technologies, such as Amazon Q and Amazon QuickSight, offer several capabilities, such as gathering advanced data insights. Retailers and consumer brands can use these insights to unlock the full potential of their data. Why is this essential? Effective data management and analysis help retail and consumer goods companies remain competitive. AWS provides a robust framework that combines scalable storage solutions such as Amazon S3 with powerful analytics tools. Businesses use the AWS framework to securely manage vast amounts of structured and unstructured data. This integration facilitates streamlined data processing, enabling retailers to derive actionable insights quickly.

Amazon QuickSight is a pivotal tool in this ecosystem. Its ultra-fast, highly parallel, in-memory calculation engine enables rapid analysis of large datasets, allowing users to visualize billions of rows of data interactively. This capability is crucial for retailers that need to make swift decisions based on real-time data trends. The platform also supports natural language querying through Amazon Q in QuickSight, so users can ask questions and receive immediate visualizations. This democratizes data access—employees across various departments, regardless of technical expertise, can engage with complex datasets effectively.

The automated data-preparation feature in Amazon Q in QuickSight significantly improves efficiency by inferring and adding semantic information to datasets. This reduces time spent on manual data-preparation tasks, freeing teams to focus on extracting insights rather than the intricacies of data management.

Retailers can use these insights for various applications, including the following.

Personalized marketing strategies

With Amazon Q, retailers can quickly analyze customer behavior through natural language queries. For instance, a marketer can ask, "What products did customers aged 18 to 25 purchase last month?" With that data, they can improve campaigns.

Integrating generative AI allows for real-time customization of marketing content based on user behavior. Marketing teams can also use Amazon Q to generate narratives about campaign performance, facilitating swift strategy adjustments. Combining tools such as Amazon Personalize with QuickSight and Amazon Q enhances content creation and distribution. Additionally, the Stories feature in Amazon Q simplifies insight sharing, offering visually engaging reports that improve collaboration across teams. These capabilities empower marketing teams to respond quickly to trends and drive more effective campaigns.

Inventory management

Effective inventory management is crucial to maintaining optimal stock levels while minimizing costs. With Amazon Q, retail and consumer goods teams can query historical sales data using simple language, facilitating precise demand forecasting. A retailer can ask, "What are projected sales for the next quarter based on past trends?" This allows businesses to adjust stock levels proactively, reducing excess inventory while ensuring popular items remain available. Continual monitoring of key inventory metrics through QuickSight visualizations—enriched by insights from Amazon Q—empowers retailers and consumer brands to make timely adjustments to their supply chains, improving customer satisfaction and operational efficiency.

Operational efficiency

Maximizing operational efficiency is essential for retailers. Amazon Q complements QuickSight's robust visualization tools by tracking key performance indicators through conversational queries. Managers can ask questions like, "How are we performing against our sales targets this month?" Real-time dashboards continually monitor performance, identify bottlenecks, and offer opportunities for swift responses. By integrating these advanced capabilities into their operations, organizations can make informed, data-driven decisions quickly—achieving business objectives faster.

Moreover, the integration of generative AI capabilities helps teams to perform complex analyses without needing extensive training in data science. A team member can ask Amazon Q in QuickSight, "What factors contributed to last month's sales increase?" and receive a summary of key drivers within seconds. This functionality empowers retail and consumer goods teams to understand underlying trends and make proactive adjustments to their strategies.

Amazon Q and Amazon QuickSight support retail and consumer goods companies in extracting actionable insights from accrued data. By simplifying data-management processes and enhancing visualization capabilities with natural language queries, these tools give organizations the power to adapt swiftly to market changes and optimize their operations for sustained growth.

Conclusion

The retail and consumer goods industry continues to evolve rapidly, driven by technological advancements and changing consumer expectations. Generative AI and data-driven strategies are key trends that are guiding the industry to create more personalized experiences, optimize operations, and make data-informed decisions. By embracing these innovations, retailers and consumer brands can enhance customer satisfaction, streamline processes, and gain a competitive edge in an increasingly digital marketplace.

On AWS, an example architecture for building your own solution can be found in the Generative AI Application Builder on AWS solution within the AWS Solutions Library. The retail and consumer goods solution include an expansive variety of vetted solutions and guidance for business and technical use cases. For retail and consumer goods organizations looking to buy vs. build, AWS offers a vast partner community with solutions built on top of AWS, curated for the industry in the AWS Marketplace hub for retail and consumer goods.