GRASPED Predictive Analytics in Sales and Marketing

Forecasting with Artificial Intelligence

This course structure aims to provide learners with a thorough understanding of how AI integrates with predictive analytics to revolutionize strategies in sales and marketing. It combines theoretical knowledge with practical applications, encourages ethical considerations, and promotes a global perspective, preparing students for future trends and innovations in the field.

Module 1: Introduction to Predictive Analytics and AI

• Primary Learning Prompt:

  What are the fundamental principles of predictive analytics within sales and marketing and the role AI plays in this domain. Why is predictive analytics crucial in these fields, and how does AI enhance its capabilities?
• Immediate Knowledge Check: Define predictive analytics and explain how AI contributes to its efficacy in sales and marketing.
• Self-rating Question: On a scale from 1 to 10, how would you rate your current understanding of the foundational principles of AI in predictive analytics within sales and marketing?

Module 2: Historical Evolution of Predictive Analytics

• Primary Learning Prompt:

  Trace the evolution of predictive analytics in sales and marketing, focusing on the milestones that marked significant advancements, leading up to the integration of AI.
• Immediate Knowledge Check: Identify a pivotal development in the history of predictive analytics that significantly altered sales or marketing strategies.
• Self-rating Question: On a scale from 1 to 10, how confident are you in your understanding of the historical progression of predictive analytics in sales and marketing?

Module 3: Key Concepts and Technologies in AI-Powered Predictive Analytics

• Primary Learning Prompt:

  Delve into the critical concepts, theories, and technologies that underpin AI-powered predictive analytics. What algorithms or models are central to this discipline?
• Immediate Knowledge Check: Describe a specific AI model or algorithm commonly used in predictive analytics for sales or marketing.
• Self-rating Question: On a scale from 1 to 10, how would you rate your grasp of the crucial technologies and concepts in AI-powered predictive analytics?

Module 4: Real-World Applications of Predictive Analytics in Sales and Marketing

• Primary Learning Prompt:

  Analyze how various industries implement AI-powered predictive analytics in their sales and marketing initiatives. What distinguishes successful applications?
• Immediate Knowledge Check: Present a real-world example of a company successfully utilizing AI-powered predictive analytics in its sales or marketing strategy.
• Self-rating Question: How confident are you in identifying and understanding real-world applications of AI-powered predictive analytics, on a scale from 1 to 10?

Module 5: Innovations and Trends in AI-Driven Predictive Analytics

• Primary Learning Prompt:

  What are the cutting-edge developments and current trends in AI-driven predictive analytics? How are these advancements reshaping the landscape of sales and marketing?
• Immediate Knowledge Check: Discuss a recent innovation in AI-driven predictive analytics and its potential impact on sales or marketing strategies.
• Self-rating Question: On a scale from 1 to 10, how well do you understand the current innovations and trends in AI-driven predictive analytics?

Module 6: Challenges and Debates Surrounding AI in Predictive Analytics

• Primary Learning Prompt:

  Explore the challenges and controversies associated with using AI in predictive analytics. Consider accuracy, privacy, ethical concerns, and the potential for bias.
• Immediate Knowledge Check: Argue a position regarding a current debate on the use of AI in predictive analytics, providing evidence to support your viewpoint.
• Self-rating Question: How comfortable are you in discussing the challenges and controversies related to AI in predictive analytics, on a scale from 1 to 10?

Module 7: Case Study - AI in Predictive Analytics

• Primary Learning Prompt:

  Examine a detailed case study where AI-powered predictive analytics was implemented in a sales or marketing campaign. Discuss the approach, challenges, results, and key takeaways.
• Immediate Knowledge Check: Summarize the case study, emphasizing the role of AI, the strategy employed, challenges faced, and the outcomes achieved.
• Self-rating Question: How proficient are you in analyzing and understanding case studies related to AI in predictive analytics, on a scale from 1 to 10?

Module 8: Ethical Implications of AI in Predictive Analytics

• Primary Learning Prompt:

  Consider the ethical dimensions of utilizing AI in predictive analytics. Reflect on issues like data privacy, consent, transparency, and the potential for misuse.
• Immediate Knowledge Check: Describe an ethical dilemma associated with the use of AI in predictive analytics and propose a possible solution.
• Self-rating Question: How would you rate your understanding of the ethical considerations in AI-powered predictive analytics, on a scale from 1 to 10?

Module 9: Global Impact of AI-Powered Predictive Analytics

• Primary Learning Prompt:

  Evaluate the global impact of AI-powered predictive analytics in sales and marketing. How do strategies and applications vary across different cultures and markets?
• Immediate Knowledge Check: Compare the use of AI-powered predictive analytics in two different global markets, highlighting any unique approaches or challenges.
• Self-rating Question: How confident are you in your understanding of the global implications and applications of AI in predictive analytics, on a scale from 1 to 10?

Module 10: Reflection and Future Exploration in AI and Predictive Analytics

• Primary Learning Prompt:

  Reflect on your learning journey: what insights have you gained about AI in predictive analytics for sales and marketing? Identify areas for further exploration and predict future trends in this technology.
• Immediate Knowledge Check: Write a brief reflective essay on your key takeaways from this course and specify the areas of AI in predictive analytics you are interested in exploring further.
• Self-rating Question: How confident are you in your overall understanding of AI in predictive analytics for sales and marketing, and what are your next steps in this learning journey, on a scale from 1 to 10?