Instructor led courses & training

Practitioner’s approach to using machine learning for business decisions

In analytics driven enterprise, business professionals are expected to leverage data driven insights and machine learning techniques to help drive business decisions. Key cross-functional teams such as strategy, sales and account management, sales operations, customer service, product management, marketing and customer acquisition can improve return on investment and increase top-line through machine learning techniques.

This course highlights application of machine learning within cross-functional teams and provides a practitioner’s approach. The course will illustrate key analytics/machine learning approaches for business functions with sample datasets using R and Tableau. K-means clustering, predictive modeling (logistic regression, decision tree) and market basket models will be used on real-world public datasets to illustrate business applications.

Session 1: Overview of business functions in an organization

• Cross-functional teams and their organizational objectives
• Key metrics for individual functional teams
• Analytical approaches to address functional business problems

Session 2: Overview of tools used in analytics and machine learning

• Overview of various tools and use-case scenarios
• Introduction to R, Tableau
• Case study – Introduction and walk-thru of dataset

Session 3: Market assessment and market segmentation

• Market assessment – Need for sizing the market and creating segments
• Approaches to sizing and segmentation of market
• Utilizing segmentation for customer strategy

Session 4: Principles of Visualization

• Key concepts in visualization
• Pros and cons of using different tools for visualization
• Visualizing charts in Excel, R and Tableau

Session 5: CRM lifecycle

• Lifecycle of a customer (Acquisition to Retention)
• Relationship of various organizations during customer lifecycle
• Organizational goals while managing customer lifecycle

Session 6: CRM analytics

• Analytics in customer lifecycle management (CRM)
• Modeling techniques used during customer lifecycle
• Technology and marketing stack to support CRM objectives

Session 7: Customer segmentation

• Customer segmentation vs. Market segmentation
• Approaches to creating customer segments
• Leveraging segmentation for sales and marketing strategy

Session 8: Analytical approaches to customer segmentation

• Heuristics segmentation vs. statistical clustering techniques
• R modeling to create customer segments
• Identifying profile of segments from the statistical model

Session 9: Product bundling and basket recommendation

• Overview of product bundling
• Applications of market basket analysis
• R modeling to create product bundles

Session 10: Customer satisfaction and churn

• Importance of customer satisfaction and its impact on topline and bottom-line
• Red-flag-metrics for churn; model to measure customer churn
• Managing customer churn

Session 11: Defining KPIs and designing dashboard for health of business

• Key principles in designing dashboards
• Identifying key operational metrics
• Case study

Session 12: Creating dashboard in Tableau

• Creating charts for KPIs
• Dashboards from multiple charts
• Telling story from dashboards