Data Science for Marketing and Customer Analytics Training Course

 Data Science for Marketing and Customer Analytics Training Course 

Introduction 

Data Science for Marketing and Customer Analytics Training Course provides comprehensive practical knowledge and advanced analytical skills for transforming marketing operations through data-driven decision-making, predictive analytics, artificial intelligence, and customer intelligence. As organizations increasingly adopt digital marketing, omnichannel engagement, and customer-centric business strategies, the ability to collect, process, interpret, and leverage customer data has become essential for achieving competitive advantage. This course explores marketing data science, customer segmentation, machine learning, marketing automation, sentiment analysis, customer lifetime value prediction, churn analytics, and campaign performance optimization. Participants will gain practical exposure to modern analytical tools and techniques for understanding consumer behaviour, identifying market opportunities, improving customer acquisition, and maximizing marketing return on investment. 

The training also examines how organizations can integrate big data analytics, predictive modelling, business intelligence, and real-time customer analytics into their marketing strategies. Through practical exercises and international case studies, participants will explore how leading organizations use customer data platforms, recommendation engines, attribution modelling, and personalization technologies to improve customer experiences and business performance. The programme emphasizes responsible data management, customer privacy, ethical artificial intelligence, and measurable marketing outcomes. By the end of the course, participants will be equipped to develop evidence-based marketing strategies, optimize customer journeys, strengthen customer retention, and implement scalable data science solutions that support sustainable organizational growth. 

Course Objectives 

By the end of this course, participants should be able to: 

1. Apply data science and advanced analytics to modern marketing strategies.
2. Develop customer segmentation using machine learning algorithms.
3. Analyse customer behaviour using predictive analytics.
4. Apply artificial intelligence to marketing automation and personalization.
5. Calculate and optimize customer lifetime value.
6. Develop predictive customer churn and retention models.
7. Implement marketing attribution and conversion analytics.
8. Analyse social media sentiment and consumer engagement.
9. Use big data technologies for customer intelligence.
10. Evaluate digital marketing performance using key performance indicators.
11. Apply recommendation systems to improve customer experiences.
12. Integrate business intelligence into marketing decision-making.
13. Implement ethical data governance and responsible customer analytics.

Organizational Benefits
 

1. Improved marketing return on investment through data-driven decisions.
2. Increased customer acquisition and conversion rates.
3. Enhanced customer retention and loyalty management.
4. More accurate customer segmentation and targeting.
5. Reduced marketing expenditure and campaign inefficiencies.
6. Improved personalization and customer engagement.
7. Stronger competitive intelligence and market positioning.
8. Better forecasting of customer demand and behaviour.
9. Enhanced marketing automation and operational efficiency.
10. Improved customer experience and sustainable revenue growth.

Target Audiences
 

1. Marketing managers and marketing directors.
2. Digital marketing specialists and strategists.
3. Customer experience and customer success managers.
4. Data scientists and business intelligence analysts.
5. Market research and consumer insights professionals.
6. CRM and marketing automation specialists.
7. Business development and sales managers.
8. Entrepreneurs and business intelligence consultants.

Course Duration: 5 days

Course Modules

Module 1: Foundations of Data Science in Marketing
 

• Introduction to marketing data science and analytical frameworks.
• Marketing data sources, collection, and integration.
• Descriptive, diagnostic, predictive, and prescriptive analytics.
• Marketing analytics tools and business intelligence platforms.
• Case study: Netflix's use of customer data for personalized marketing.
• Practical development of a marketing analytics framework.

Module 2: Customer Segmentation and Behavioural Analytics
 

• Demographic, geographic, psychographic, and behavioural segmentation.
• Clustering techniques using K-means and hierarchical algorithms.
• Customer profiling and purchase behaviour analysis.
• RFM (Recency, Frequency, Monetary) analysis.
• Case study: Amazon's customer segmentation and recommendation strategies.
• Practical customer segmentation using real-world datasets.

Module 3: Predictive Analytics and Machine Learning for Marketing
 

• Introduction to machine learning applications in marketing.
• Predictive modelling for customer purchasing behaviour.
• Classification, regression, and forecasting techniques.
• Feature engineering and model performance evaluation.
• Case study: Starbucks' predictive analytics for customer engagement.
• Developing predictive marketing models using Python.

Module 4: Customer Lifetime Value and Churn Analytics
 

• Customer lifetime value measurement and forecasting.
• Customer acquisition cost and profitability analytics.
• Churn prediction and customer retention modelling.
• Early-warning indicators and customer loyalty analytics.
• Case study: Telecommunications churn prediction and retention strategies.
• Developing customer lifetime value and churn prediction models.

Module 5: Digital Marketing and Campaign Performance Analytics
 

• Search engine marketing and digital advertising analytics.
• Multi-channel campaign tracking and performance measurement.
• Marketing attribution and conversion funnel analysis.
• Return on advertising spend and campaign optimization.
• Case study: Google's data-driven advertising and campaign measurement.
• Practical campaign performance dashboard development.

Module 6: Social Media Analytics and Customer Sentiment Analysis
 

• Social listening and digital customer engagement analytics.
• Natural language processing and sentiment analysis.
• Brand perception and reputation monitoring.
• Influencer marketing and audience engagement measurement.
• Case study: Coca-Cola's social media listening and brand engagement.
• Practical sentiment analysis using customer reviews and social data.

Module 7: Marketing Automation and AI-Powered Personalization
 

• Artificial intelligence applications in marketing automation.
• Recommendation engines and personalized customer experiences.
• Customer journey mapping and omnichannel analytics.
• Generative AI applications in marketing content optimization.
• Case study: Spotify's personalized recommendations and engagement campaigns.
• Developing AI-powered customer personalization strategies.

Module 8: Customer Data Governance, Visualization and Analytics Strategy
 

• Customer data platforms and marketing data integration.
• Data visualization using Power BI, Tableau, and Python.
• Data privacy, protection, and ethical artificial intelligence.
• Marketing analytics dashboards and strategic reporting.
• Case study: Sephora's customer data integration and personalized retail experiences.
• Developing an integrated customer analytics implementation roadmap.

Training Methodology
 

• Instructor-led presentations and interactive discussions.
• Practical exercises using marketing datasets and analytical tools.
• Real-world international case studies and group assignments.
• Hands-on machine learning and predictive analytics demonstrations.
• Group projects involving customer segmentation and campaign optimization.
• Individual assessments, presentations, and knowledge-sharing sessions.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.