Module 6: Customer Value and Role of AI in
Value Delivery Process
Introduction
- Welcome to the NPTEL online certification course on Artificial Intelligence in
Marketing.
- Module focus: Chapter 2 - Developing Marketing Strategies and Plans using AI.
- Specific focus: Module 6 - Customer Value and Role of AI in Value Delivery Process.
Learning Objectives
In this module, we will cover the following topics:
1. **Background for Understanding AI Implications in Customer Value Chain**
- Importance of AI in customer value chain.
- Exploration of AI's emergence in value delivery.
2. **Relevance of AI in Value Chain**
- Understanding AI's significance for the value chain.
- Recognition of AI's importance and exploration of AI-driven value creation forms.
3. **Current State of AI Adoption in Value Chains**
- Assessment of the present status of AI adoption.
- Analysis of industry adoption levels.
4. **Marketing and Customer Value**
- Overview of the business as a value delivery system.
- Objective: Provide customer value at a profit.
5. **Phases of Value Delivery Process**
- Three phases: Choosing value, Providing value, Communicating value.
- Optimization of the value delivery process for sustained success.
6. **Strategic Marketing vs. Tactical Marketing**
- Differentiating strategic marketing (STP) from tactical marketing (4Ps - Product,
Price, Place, Promotion).
- Understanding how these two aspects form marketing strategy.
Value Delivery Process
- Explanation of customer segmentation, market selection, and value positioning.
- Discussion of product development, pricing, sourcing, distribution, and sales in the
value delivery process.
AI in Value Delivery Process
- Overview of how AI optimizes value delivery.
- Introduction of AI applications: AI-related services, computer vision, deep learning,
edge AI, intelligent applications, machine learning, robotics, process automation, and
virtual assistants.
Operational Efficiency through AI
- Case study of gd.com, a Chinese retailer, using AI to achieve 92% same or next-day
order delivery.
- Implementation of augmented intelligence by Nike for customized shoe designs within
a 2-week process.
AI in Financial Services - Ant Financial
- Overview of Ant Financial's use of AI for financial transformation.
- Achievement of serving over 1 billion customers with AI-driven services in lending,
wealth management, insurance, and more.
Value Proposition and Value Chain
- Definition and significance of value proposition.
- Understanding the integrative nature of strategy linking value proposition and value
chain.
Value Chain Definition and Components
- Explanation of a value chain as a tool for identifying key activities.
- Primary and secondary activities contributing to value creation.
Relevance of AI in Value Chain
- Discussion of the complex interconnected web of business activities.
- Utilization of AI for decision-making in material purchase, storage, production plans,
etc.
AI-Driven Value Creation
- Forms of AI-driven value creation: process efficiency, process enhancement, product,
and service innovation.
- Overview of AI's impact on communication, customer insights, design, manufacturing,
delivery, and retail.
AI Process Efficiency
- Explanation of AI automation improving repetitive and challenging processes.
- Example: Abundant Robotics using AI-powered machines for apple harvesting.
AI Process Enhancement
- Discussion of how AI enhances existing processes.
- Example: Salesforce Einstein providing leads for salespeople and prioritizing
high-value leads.
AI in Product and Service Innovation
- Utilization of AI for creating new products and services.
- Example: Stitch Fix using AI to design new clothing styles based on customer
preferences.
Current State of AI Adoption in Value Chain
- Overview of functional areas adopting AI: IT, customer service, marketing, sales,
finance, and accounting.
- Shift towards investing in core functions, as seen with Stitch Fix deploying deep
learning.
Industry Adoption Levels
- Analysis of varying AI adoption levels across industries.
- McKinsey report data indicating differing rates of adoption between
technology-focused and traditional industries.
Elements of Emerging Technology Landscape
- Introduction to the IDEAS framework: Intelligence, Data, Expertise, Architecture, and
Strategy.
- Emphasis on focusing on these elements for effective AI adoption.
Conclusion
- Summary of key points discussed in the module.
- Importance of customer value in marketing and the amalgamation of AI.
- Recognition of AI's relevance and significance in the value chain and value delivery.
- Exploration of forms of AI-driven value creation and the adoption level of AI in different
industries.