AWS for Industries

Top 5 ways artificial intelligence and machine learning are changing retail

The retail industry is facing numerous challenges in today’s dynamic landscape that are forcing them to rethink their strategies. Retailers must keep up with technological advancements, such as generative AI, artificial intelligence and machine learning (AI/ML) and ecommerce platforms, to remain competitive to retain customers and market share.

Retailers face intense competition from both traditional brick-and-mortar stores and ecommerce companies—impacting their profit margins and forcing retailers to innovate to stay competitive.

To mitigate these challenges and stay ahead, retailers are leveraging artificial intelligence and machine learning technologies. Amazon Web Services (AWS) offers various services tailored to address these challenges that enable retailers to harness the power of AI/ML effectively. We’ll explore the top five qualified AI/ML use cases for retail, their significance, implementation strategies, and how they address the current challenges faced by the industry.

Various AI/ML use cases were categorized based on their impact for the different challenges in retail industry, using factors like customer satisfaction, revenue, and operational efficiency. The matrix grid shown in Figure 1 explains the rationale on the qualification of the top five AI/ML use cases for the current challenges of the retail industry.

Figure 1 AIML use case impact grid

Figure 1 – AI/ML use case impact grid


AI-powered personalization has revolutionized how retailers operate, especially when it comes to product recommendation and marketing campaigns. According to a McKinsey study 71% of consumers expect companies to deliver personalized interactions. Companies that grow faster generate more than 40% of their revenue from personalization.

Personalized product recommendation provides retailers a powerful tool to increase sales, improve customer engagement, drive customer loyalty and gain valuable insights into customer behavior. Personalized marketing enables retailers to:

  • Deliver highly targeted and relevant messages to customers
  • Increase engagement
  • Increase conversion rates
  • Increase customer loyalty
  • Increase overall business growth

By leveraging customer data and insights, retailers can create more effective, marketing strategies that drive measurable results.

AWS customers can leverage Amazon Personalize to quickly build personalization engines for use cases like product recommendations, personalized emails and targeted marketing campaigns. Amazon Personalize is a fully managed machine learning service that uses your data to generate item recommendations for your users. It can also generate user segments based on the users’ affinity for certain items or item metadata. AWS built Amazon Personalize based on our own experience of running personalized recommendations engines for more than 20 years.

We recommend our customers start with Amazon Personalize for your personalization use cases. However, if you need a higher level of customization (with the flexibility of choosing your own algorithms and have data science teams to build your own models) then you can use Amazon SageMaker. Amazon SageMaker provides the ability to build, train, and deploy machine learning models with fully managed infrastructure, tools, and workflows.

If you would like to explore large language models (LLM) for personalization use cases, Amazon Bedrock and Amazon SageMaker JumpStart provides many foundational models (FMs) for you to get started.

AWS has published documentation for ready to deploy solutions for production recommendations to get started on this. You can see how AWS customers like Cencosud, Lotte Market and others are able to leverage Amazon Personalize to solve their personalization challenges.

Demand Forecasting

Demand forecasting employs historical sales data, market trends, and external factors to predict future product demand. Accurate demand forecasting helps retailers optimize inventory management, reduce stockouts, minimize overstocking, and improve supply chain efficiency. Faster and accurate demand forecasts and forecast traceability are the most important factors to improve margins through stock optimization. According to a McKinsey study, 20% of the executives surveyed have already implemented AI/ML for demand planning and 60% plan to implement in future.

Retailers can leverage Amazon Forecast to forecast business outcomes accurately using machine learning. They can also take advantage of other AWS services like Amazon Simple Storage Service (Amazon S3), Amazon SageMaker and AWS Lambda to upload historical sales data, train ML models, and integrate demand forecasts into inventory management systems or supply chain workflows.

Generative AI techniques can be leveraged to automate the generation of product categories and descriptions. It can help generate new product ideas. It can also be used to generate synthetic sales data, augmenting the historical data for more robust and accurate predictions.

Social Media Monitoring/Sentiment Analysis

Sentiment analysis utilizes natural language processing (NLP) techniques to analyze customer feedback, reviews, and social media data to gauge sentiment and extract insights. Sentiment analysis addresses the challenge of understanding customer opinions at scale. This enables retailers to identify emerging trends, promptly respond to concerns, and improve overall customer satisfaction.

Retailers can use Amazon Comprehend as a core to perform both comprehensive and targeted sentiment analysis. AWS services such as Amazon Simple Queue Service (Amazon SQS), AWS Lambda, Amazon API Gateway can be leveraged to build solutions. Also, it can be used to:

  • Analyze social media data in near real-time
  • Perform sentiment extraction
  • Perform feedback categorization
  • Automate the processing of customer-generated content

It can also be used to understand the different sentiments associated with specific entities in the text (such as company, product, person, or brand) directly from the API.

Generative AI techniques can complement sentiment analysis by using natural language generation (NLG) models to automatically generate personalized responses.

Call Center/Conversational services

Call centers and online chat support play a significant role in any retailer’s omni-channel customer experience. A well-designed customer experience can help resolve customer issues and turn one-time buyers into long-term, loyal customers. However, in the post pandemic era (where the volume of online orders has increased and there is a talent shortage where high turnover looms), a poor contact center execution can have negative consequences. This can include turning away both new and loyal customers, eroding revenue and profits, and negatively impacting brand image.

Retailers can leverage AWS Contact Center Intelligence (AWS CCI) solutions to improve the customer experience, boost agent productivity, and gain conversation insights by adding AI capabilities to the contact center of your choice—without any ML expertise. AWS CCI solutions use a combination of AWS ML-powered services to provide self-service virtual agents, near real-time call analytics and agent assist, and post-call analytics.

Retailers can consider developing generative AI solutions, using Amazon Bedrock or Amazon SageMaker JumpStart foundation models to automate tasks, like summarizing customer interactions and call scripts, to save agents time in every interaction. Retailers can train LLMs securely with their own brand voice to create a unique branding experience when responding to customers at their service centers.

Generative AI engagement chatbot solutions, can better articulate a customer’s needs and emotions to offer personalized advice/recommendations, leading to a more satisfying customer experience. Generative AI can analyze large volumes of social media signals, live sporting events, and customer persona, in near real-time, to pitch offers that have a higher propensity to close.

Fraud and Threat detection

Fraud detection utilizes machine learning algorithms to detect and prevent fraudulent activities, such as payment fraud, account takeover, and identity theft. Faster movement of money usually increases the risk of fraud, and real-time disbursements are set to double in 2022. A McKinsey study suggests that customer satisfaction improved by 42 points when companies respond well to fraud events. Fraud detection helps retailers safeguard against financial losses, protect customer data, and maintain brand reputation in an increasingly digital and complex retail landscape.

Retailers can use AWS Fraud Detection machine learning solutions to proactively and more accurately detect and prevent online fraud in near real-time. This allows customers to instantly apply containment or remediation measures designed to block or deny fraudsters and fast-track low-risk activity to provide better customer experiences for legitimate customers.

AWS services such as AWS Lambda, and AWS Step Functions can be used to train fraud detection models, automate near real-time alerts, and integrate fraud detection into payment and authentication workflows. Amazon Fraud Detection Machine Learning solutions leverage over 20 years of experience preventing fraud and abuse across AWS,, and subsidiary businesses. This wealth of knowledge is harnessed to enhance the models created by AWS with insights into fraud patterns.


Artificial intelligence and machine learning use cases powered by AWS services and generative AI techniques offer practical solutions to address the complex challenges faced by retailers. From personalizing shopping experiences to optimizing demand forecasting, enhancing customer support, detecting fraud and understanding customer sentiments, AWS services empower retailers to innovate and thrive.

By embracing these imperatives and leveraging AWS services, retail enterprises will be able to drive the adoption of AI/ML technology more rapidly and comprehensively. Doing so can unlock its full potential and help retailers gain a competitive edge in the market.

Contact an AWS Representative to know how we can help accelerate your business.

Further Reading

Sarath Krishnan

Sarath Krishnan

Sarath Krishnan is a Senior Solutions Architect with Amazon Web Services. He is passionate about enabling enterprise customers on their digital transformation journey. Sarath has extensive experience in architecting highly available, scalable, cost effective and resilient applications on the Cloud. His area of focus includes DevOps, machine learning, MLOps and generative AI.

Arun Chellappa Ganesan

Arun Chellappa Ganesan

Arun Chellappa Ganesan is a Senior Customer Solutions Manager with Amazon Web Services. With a solid foundation in technology and a knack in strategic thinking, Arun thrives on spearheading cloud transformation and modernization. He finds immense joy in unraveling the complexities of technology to unlock innovation. Arun is a marathon runner, he cherishes quality time spent with family and finds joy in coaching young minds and helping the community.

Indy Sawhney

Indy Sawhney

Indy Sawhney is a Senior Customer Solutions Leader with Amazon Web Services. Always working backwards from customer problems, Indy advises AWS enterprise customer executives through their unique cloud transformation journey. He has 25+ years of experience helping enterprise organizations adopt emerging technologies, and business solutions. Indy is an area of depth specialist with AWS's Technical Field Community for artificial intelligence/machine learning (AI/ML), with specialization in Generative AI and LCNC (Low code-No code) SageMaker solutions.

Shibu Nair

Shibu Nair

Shibu Nair is a Principal Solutions Architect helping customers in their cloud transformation, artificial intelligence/machine learning (AI/ML) and data analytics. He is a technical leader, advising executives and engineers on cloud strategies designed to innovate using data-driven methodology and AI/ML.