Advance techniques and tools for Marketing analytics and data insights #255005

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This 5-day intensive course dives deep into the advanced techniques and tools used to extract actionable insights from marketing data. Participants will learn to go beyond basic data analysis and master sophisticated methods to understand customer behavior, optimize marketing campaigns, and drive significant business growth. The program combines theoretical knowledge with hands-on experience, utilizing industry-standard tools and real-world case studies to equip participants with the skills to become data-driven marketing professionals.

Upon successful completion of this course, participants will be able to:
• Master advanced data analysis techniques: Apply statistical modeling, machine learning algorithms (e.g., regression, clustering, classification), and predictive analytics to marketing data.
• Develop and implement data-driven marketing strategies: Translate data insights into actionable marketing campaigns across various channels (e.g., digital, social media, email, search).
• Utilize advanced marketing analytics tools: Gain proficiency in using industry-leading tools like Google Analytics, Adobe Analytics, Tableau, and Power BI for data visualization and reporting.
• Conduct customer segmentation and targeting: Identify and profile customer segments, personalize marketing messages, and optimize customer journeys.
• Measure and evaluate marketing performance: Track key performance indicators (KPIs), analyze campaign effectiveness, and generate insightful reports for decision-making.
• Stay abreast of emerging trends: Understand the latest advancements in marketing analytics, including artificial intelligence (AI), automation, and predictive modeling.

This course is designed for marketing professionals, data analysts, and business professionals who seek to enhance their data analysis and decision-making skills. Ideal candidates include:
• Marketing Managers
• Digital Marketing Specialists
• Data Analysts
• Business Analysts
• Product Managers
• Market Research Analysts
• Anyone involved in data-driven marketing decision-making

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

• Morning:
o Data Collection and Preparation: Data Sources, Data Wrangling, Data Cleaning
o Statistical Foundations for Marketing: Probability, Distributions, Hypothesis Testing
o Introduction to Data Visualization: Creating Effective Charts and Dashboards
• Afternoon:
o Customer Lifetime Value (CLTV) Analysis: Calculating and Optimizing CLTV
o Attribution Modeling: Understanding Customer Journeys and Assigning Campaign Value

• Morning:
o Regression Analysis: Predicting Sales, Churn, and Customer Behavior
o Classification Models: Customer Segmentation, Lead Scoring, Churn Prediction
o Machine Learning Concepts: Supervised vs. Unsupervised Learning, Model Evaluation
• Afternoon:
o Hands-on Exercise: Building a Predictive Model for Customer Churn
o Introduction to Text Mining and Sentiment Analysis: Analyzing Customer Feedback

• Morning:
o Interactive Dashboards: Creating Dynamic and Engaging Visualizations
o Storytelling with Data: Communicating Insights Effectively to Stakeholders
o Advanced Data Visualization Techniques: Geographic Mapping, Network Analysis
• Afternoon:
o Hands-on Exercise: Building an Interactive Marketing Dashboard
o Data Visualization Tools: Tableau, Power BI, Google Data Studio

• Morning:
o Google Analytics 360: Advanced Features and Integrations
o Social Media Analytics: Tracking and Measuring Social Media Performance
o Search Engine Optimization (SEO) Analytics: Keyword Research, Ranking Tracking
• Afternoon:
o Programmatic Advertising: Data-Driven Campaign Management and Optimization
o Email Marketing Analytics: Campaign Performance, Segmentation, and Personalization

• Morning:
o Artificial Intelligence (AI) in Marketing: Chatbots, Predictive Personalization
o Marketing Automation: Automating Marketing Tasks and Improving Efficiency
o The Future of Marketing Analytics: Emerging Technologies and Best Practices
• Afternoon:
o Case Studies: Real-world Applications of Advanced Marketing Analytics
o Q&A and Wrap-up Session

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