Module 7: Customer Analytics-

7.1 Introduction to Customer Analytics

  • Defining customer analytics and its role in CRM.
  • The significance of data-driven decision-making.
  • How customer analytics drives business growth.

7.2 Customer Data Sources

  • Identifying sources of customer data (e.g., CRM databases, transaction logs, web analytics).
  • Data collection methods and data quality considerations.
  • Data aggregation and data storage.

7.3 Data Preprocessing and Cleaning

  • Data preprocessing techniques (e.g., data cleaning, data transformation).
  • Handling missing data and outliers.
  • Preparing data for analysis.

7.4 Descriptive Customer Analytics

  • Descriptive analytics vs. predictive and prescriptive analytics.
  • Exploratory data analysis (EDA) techniques.
  • Visualizing customer data for insights.

7.5 Customer Segmentation

  • Principles of customer segmentation.
  • Methods for segmenting customers (e.g., demographic, behavioral, psychographic).
  • Creating customer segments for targeted marketing.

7.6 Predictive Customer Analytics

  • Predictive modeling in CRM.
  • Machine learning algorithms for customer predictions (e.g., regression, classification).
  • Churn prediction and customer lifetime value (CLV) modeling.

7.7 Prescriptive Customer Analytics

  • The role of prescriptive analytics in CRM.
  • Recommender systems for personalized recommendations.
  • Optimization techniques for decision-making.

7.8 A/B Testing and Experimentation

  • Designing and conducting A/B tests for marketing campaigns.
  • Interpreting A/B test results.
  • Using experimentation to refine customer strategies.

7.9 Customer Analytics Tools

  • Overview of customer analytics software and platforms.
  • Hands-on experience with analytics tools.
  • Building and deploying customer analytics models.

7.10 Customer Analytics in Action

  • Case studies of successful customer analytics implementations.
  • Real-world examples of data-driven decision-making.
  • Learning from industry leaders.

7.11 Data Privacy and Ethics

  • Ethical considerations in customer analytics.
  • Compliance with data protection regulations.
  • Ensuring data privacy and security.

7.12 Customer Analytics Trends

  • Emerging trends in customer analytics (e.g., AI, big data, real-time analytics).
  • The future of customer analytics and its impact on CRM.

7.13 Reporting and Visualization

  • Designing dashboards and reports for stakeholders.
  • Communicating insights effectively.
  • Data storytelling and visualization tools.

7.14 Assessment and Evaluation

  • Methods for assessing the effectiveness of customer analytics initiatives.
  • Key performance indicators (KPIs) for analytics success.
  • Continuous improvement in customer analytics strategies.