Predictive and Marketing Analytics for Growth

Identify data sources for B2B and B2C models.

Evaluate internal and external data used in prospecting, customer segmentation, and lead qualification.

Examine whether to invest in specific data sources.

Align suitable rules to ensure high, reliable data quality.

Determine when audience segmentation is needed.

Examine various techniques such as off-the-shelf, rule-based and k-means segmentation.

Develop effective contact strategies based on segmentation for B2B and B2C companies.

Develop effective targeted marketing campaigns using predictive analytics and audience segmentation.

Identify appropriate target audiences for B2B and B2C marketing.

Measure marketing efforts’ success.

Evaluate prospecting approach examples from marketing campaigns and compare them with the course case studies.

Calculate metrics such as cost per conversion (CPC), cost per acquisition (CPA), and marginal cost.

Explore how and when to use linear or logistic regression for audience sizing.

Implement audience sizing and cutoff points based on KPIs such as ROI and ROAS.

Activities include selecting the right type of regression and targeting based on profitability.

Optimize product offerings through analytics metrics.

Evaluate segment- and probability-based predictive models.

Size and strategize target audiences with better accuracy.

Complete a project on optimizing a chosen product offering.

Discover the principles of customer lifetime value (CLV).

Examine customer loyalty strategies with CLV.

Explore how to plan campaigns with CLV as a central component.

Analyze CLV across different industries.

Ensure that your marketing strategy follows best practices in data ethics, privacy, and cybersecurity.

Assess the inherent challenges in predictive modeling.

Learn how to avoid predictive modeling pitfalls.

Develop a cohesive annual marketing and prospecting plan.