AI+ Sales Practitioner™

Struktura kurzu

Tato část není lokalizována

Course Overview

• Course Introduction Preview

Module 1: Introduction to Artificial Intelligence (AI) in Sales

• 1.1 Fundamentals of AI 
• 1.2 Generative AI and Modern Evolution of AI in Sales 
• 1.3 AI Tools and Technologies that Transform Sales 
• 1.4 Benefits and Challenges in the Adoption of AI in Sales
• 1.5 Real-world Examples and Applications of AI in Sales 
•  1.6 The Future of AI in Sales 

Module 2: Understanding Data in Sales

• 2.1 Categories of Sales Data 
• 2.2 Techniques for Effective Data Collection 
• 2.3 Basic Concepts of Data Analysis and Interpretation 
• 2.4 Data Management Methods 
• 2.5 Data Protection Principles 
• 2.6 Data Integration in CRM Systems 
• 2.7 Overview of Analytical Tools 
• 2.8 Ethical Use of Sales Data 
• 2.9 Case Studies: Real-World Data Applications

Module 3: AI Technologies for Sales

• 3.1 Introduction to Machine Learning in Sales 
• 3.2 Predictive Analytics: Sales Trend Forecasting 
• 3.3 NLP for Enhancing Customer Interactions 
• 3.4 Chatbots: Customer Service Automation 
• 3.5 Segmentation: Tailoring Customer Experiences 
• 3.6 Personalization: Customizing Sales Approaches 
• 3.7 Recommendation Engines: Driving Product Suggestions 
• 3.8 Sales Automation: Streamlining Sales Processes 
• 3.9 Performance Analysis: Measuring Sales Effectiveness 
• 3.10 Modern AI Sales Stack: Generative AI, Copilots, and Intelligent Workflows

Module 4: Implementation of AI in CRM Systems

• 4.1 Foundation of CRM Systems 
• 4.2 AI Integration into CRM Systems 
• 4.3 Lead Scoring 
• 4.4 Customer Insights 
• 4.5 Sales Automation 
• 4.6 Personalized Communication 
• 4.7 Chatbots and CRM 
• 4.8 Gaining Actionable Insights from Data 
• 4.9 Case Studies 
• 4.10 Modern AI CRM Stack: Tools, Integrations, and Execution Layer

Module 5: Sales Forecasting with AI

• 5.1 Introduction to Sales Forecasting 
• 5.2 Overview of Predictive Models in Forecasting 
• 5.3 Data Preparation for Analysis 
• 5.4 Identifying Sales Patterns and Trends  
  
• 5.5 Enhancing Forecast Reliability 
• 5.6 Key Forecasting AI Tools in AI 
• 5.7 Utilizing Real-time Data for Forecasts 
• 5.8 Developing Forecasts for Different Outcomes 
• 5.9 Measuring the Success of Sales Forecasts 
• 5.10 Case Study – How SaaS Companies Forecast Revenue Using AI

Module 6: Enhancing Sales Processes with AI

• 6.1 Task Automation 
• 6.2 AI-driven Email Marketing 
• 6.3 Social Media with AI Analytics 
• 6.4 AI-Powered Lead Generation 
• 6.5 Customer Segmentation 
• 6.6 Optimization of Sales Visits and Calls 
• 6.7 Tailoring Content with AI Insights 
• 6.8 Real-Time Sales Activity Monitoring 
• 6.9 Upselling and Cross-selling with AI

Module 7: Ethical Considerations and Bias in AI

• 7.1 Ethical Use of AI in Sales 
• 7.2 Bias Identification in AI Systems 
• 7.3 Bias Mitigation 
• 7.4 Transparency in AI Decision Making 
• 7.5 Accountability for AI Actions 
• 7.6 Safeguarding Customer Data 
• 7.7 Regulatory Compliance 
• 7.8 Building Customer Trust through Ethical AI 
• 7.9 Anticipating Ethical Problems in AI Advancements

Module 8: Practical Workshop

• 8.1 Scenario-Based Exercises 
• 8.2 Addressing Sales Challenges with AI 
• 8.3 Collaborative AI Implementation Plans 
• 8.4 Hands-On Workshop: AI-Powered Sales Execution using HubSpot CRM

Optional Module: Introduction to AI Agents for Sales

• 1. What are AI Agents
• 2. Types of AI Agents
• 3. Applications and Trend of AI Agents in Sales
• 4. Case studies
• 5. Hands-on