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Generative AI for Retail Analysts
Rating: 4.3 out of 5(10 ratings)
55 students

Generative AI for Retail Analysts

1000+ GenAI Prompts for Retail Decision-Maker and Choose Your Preferred Tool: ChatGPT, Gemini, or Claude
Created byEdcorner Learning
Last updated 5/2026
English
English [Auto],

What you'll learn

  • Understand the core technologies powering Generative AI, including LLMs, diffusion models, and transformers, and how they apply to retail analytics.
  • Gain access to a curated 1000+ expert-level prompts to drive innovation across every function of retail analytics.
  • Differentiate between Generative AI and traditional Predictive AI, and identify their unique use cases in retail operations.
  • Master the fundamentals of prompt engineering, including crafting effective prompts, using prompt chaining, and applying zero-shot, one-shot, and few-shot techn
  • Develop and deploy reusable prompt templates for daily retail tasks such as customer segmentation, inventory planning, and pricing optimization.
  • Generate customer personas, basket narratives, and churn-risk summaries using behavioral and transactional data.
  • Create AI-generated product descriptions, shelf layouts, and category affinity reports to optimize merchandising strategy.
  • Automate demand forecasting through stockout/overstock analysis, EOQ narratives, and external signal integration (e.g., weather, events).
  • Simulate dynamic pricing strategies, auto-generate markdown plans, and evaluate campaign ROI using scenario-based prompts.
  • Summarize customer sentiment from reviews and social media, and monitor competitor pricing using AI-generated reports.
  • Auto-generate executive summaries, PowerPoint charts, and natural language dashboards from retail databases and Excel/CSV files.
  • Deploy GenAI-powered chatbots and store assistants for operational tasks such as inventory lookup, shift scheduling, and FAQ handling.
  • Integrate conversational bots with CRM and POS systems to support seamless, intelligent customer and employee interactions.
  • Study real-world case examples from Amazon, Target, and Sephora demonstrating scalable Generative AI adoption.

Course content

11 sections • 80 lectures • 2h 53m total length

  • What is Generative AI2:54

    Explore how generative AI uses large language models and diffusion models built on transformers to create text, images, and code, with applications from content creation to retail analytics.

  • Role of Generative AI in Retail Data Analytics3:10

    Generative AI transforms retail data analytics by turning structured and unstructured data into actionable narratives, forecasts, and inventory planning, plus personalized recommendations across marketing, merchandising, and operations.

  • Generative AI vs. Predictive AI: Key Differences in Retail Use Cases3:22

    Explore how generative AI and predictive AI drive retail insights, from forecasting and demand planning to creating promotional content, dashboards, and personalized customer experiences.

Requirements

  • Basic Understanding of Retail Operations
  • Interest in AI and Automation
  • No Programming Background Required
  • Access to a Computer and Internet

Description

This course provides a comprehensive exploration of how Generative AI is transforming the retail industry through intelligent automation, enhanced personalization, and real-time decision support. Participants will begin by understanding the foundational technologies behind Generative AI, including Large Language Models (LLMs), Diffusion Models, and Transformer architectures. Emphasis is placed on the role of Generative AI in modern retail data analytics, especially in contrast to traditional predictive AI methods.

Learners will master the art of prompt engineering, including crafting effective prompts, using zero-shot, one-shot, and few-shot learning, and deploying reusable prompt templates for daily analytics tasks. Through applied exercises, participants will use Generative AI to create customer personas, analyze basket and journey data, and implement churn prediction with tailored messaging strategies.

The course then shifts to merchandising and inventory, where Generative AI is applied to generate product descriptions, identify substitution patterns, and optimize shelf layouts. It also covers demand planning through stockout/overstock simulations, EOQ and reorder point narratives, and forecasting with external signals such as weather and events.

Advanced modules focus on pricing and promotions, including markdown strategy generation, dynamic pricing simulations, and campaign ROI analysis. Sentiment analysis using LLMs, competitor pricing intelligence, and social media trend mining are also integrated to enhance competitive positioning.

Operationally, learners will auto-generate executive summaries, charts for dashboards, and query business data using natural language. Finally, the course explores the deployment of AI-powered store assistants, FAQ bots, and CRM-integrated POS chatbots to enhance in-store efficiency.

Case studies from Amazon, Target, and Sephora highlight real-world applications, while a curated collection of 1000+ Generative AI prompts equips learners to apply these methods across the retail analytics spectrum.

This course is designed for learners who want to build practical skills in GenAI, Generative AI, prompt engineering, and modern Generative AI tools. The course also helps you understand how to write effective prompts, improve AI-generated responses, select the right AI tool for different tasks, and apply Generative AI concepts in real-world situations. Whether you are a beginner, developer, student, professional, entrepreneur, or business leader, this course will help you strengthen your understanding of Generative AI applications, prompt design, AI workflows, large language models.

This course gives you access to 1,000+ practical AI prompts that you can use with your preferred Generative AI tool, including ChatGPT, Google Gemini, and Claude. Instead of being limited to one platform, you can choose the AI assistant that best fits your needs and apply the prompts to workplace, business, productivity, career development, and everyday problem-solving. Each prompt can be copied, customized, and adapted across different AI platforms, helping you improve your prompt engineering skills and achieve more accurate, relevant, and useful results.


Who this course is for:

  • Retail Analysts and Data Professionals Individuals working with sales, customer, or inventory data who want to enhance their analysis using Generative AI-powered insights and automation.
  • Category Managers and Merchandisers Professionals responsible for product assortments, shelf layouts, and SKU-level decisions who seek to use AI for trend forecasting, substitution patterns, and pricing strategy.
  • Marketing and CRM Specialists Teams focused on personalization, campaign planning, churn prevention, and customer engagement looking to deploy prompt-driven AI solutions.
  • Store Managers and Operations Leaders Frontline retail professionals interested in automating store workflows using AI-powered assistants and improving decision-making through dashboard summaries.
  • Business Intelligence (BI) and Reporting Teams Professionals responsible for creating reports, presentations, and dashboards who want to convert natural language into executive-ready analytics.
  • Retail Strategy and Innovation Leads Individuals leading digital transformation or AI adoption projects in retail seeking practical, case-driven applications of Generative AI.
  • Students and Job-Seekers in Retail Analytics Aspiring data professionals or students aiming to build job-ready skills in AI-powered analysis, forecasting, and reporting in the retail domain.
  • Product Owners and Tech Teams in Retail Startups Innovators looking to rapidly prototype AI-based retail tools like chatbots, smart dashboards, or customer-facing virtual assistants.

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