Professional Master in Data Science and Artificial Intelligence for Marketing Strategy

The Data Science and Artificial Intelligence for Marketing Strategy course, offered by Geneve Institute of Business Management, presents a structured approach to understanding how data-driven methods and intelligent systems are reshaping modern marketing practices. The program connects analytical techniques with strategic decision-making, enabling participants to interpret data with precision and apply insights to real marketing challenges.

It focuses on how data science tools and artificial intelligence models contribute to customer understanding, market segmentation, campaign optimization, and performance measurement. Rather than treating technology as an isolated function, the course positions it as an integral part of strategic marketing planning and execution.

Participants will develop a clear perspective on how to align analytical capabilities with business objectives, ensuring that marketing strategies are informed, measurable, and adaptable to evolving market conditions.

Target Group

• Marketing professionals seeking to strengthen their analytical and data-driven decision-making capabilities.

• Business managers responsible for planning and evaluating marketing strategies.

• Digital marketing specialists aiming to integrate AI tools into their workflows.

• Data analysts working within marketing or commercial departments.

• Entrepreneurs looking to leverage data for customer acquisition and retention.

• Brand managers interested in improving targeting and positioning strategies.

• Professionals involved in customer experience and engagement optimization.

• Individuals with basic knowledge of marketing or data analysis aiming to advance their expertise.

Objectives

• Explain the role of data science in shaping modern marketing strategies.

• Clarify how artificial intelligence enhances customer insights and campaign performance.

• Strengthen the ability to interpret marketing data for strategic decision-making.

• Introduce techniques for segmenting markets using data-driven approaches.

• Explore methods for predicting customer behavior using analytical models.

• Examine tools used to optimize marketing performance and resource allocation.

• Build understanding of integrating AI into marketing systems and processes.

• Prepare participants to align data insights with organizational marketing goals.

Course Outline 

• Foundations of Data Science in Marketing

  • Explanation of data science as a discipline and its growing influence on marketing strategy development and execution.

  • Identification of key data sources used in marketing environments, including digital platforms and customer databases.

  • Description of how structured and unstructured data contribute to marketing insights.

  • Overview of the relationship between data analysis and informed marketing decisions.

• Introduction to Artificial Intelligence in Marketing

  • Explanation of artificial intelligence concepts and how they are applied within marketing contexts.

  • Description of the evolution of AI tools in customer engagement and campaign management.

  • Identification of areas where AI enhances marketing efficiency and accuracy.

  • Discussion of how AI complements traditional marketing approaches.

• Marketing Data Collection and Management

  • Explanation of methods used to collect customer and market data from various channels.

  • Description of data storage techniques that ensure accessibility and reliability.

  • Overview of data cleaning processes to improve quality and usability.

  • Identification of challenges in managing large marketing datasets.

• Data Preparation for Analysis

  • Explanation of organizing data into usable formats for analytical purposes.

  • Description of transformation techniques that prepare raw data for modeling.

  • Overview of handling missing or inconsistent data values.

  • Discussion of maintaining consistency across multiple data sources.

• Exploratory Data Analysis for Marketing

  • Explanation of techniques used to summarize and understand marketing datasets.

  • Description of identifying patterns and trends within customer data.

  • Overview of key metrics used to evaluate marketing performance.

  • Discussion of interpreting data distributions for strategic insights.

• Data Visualization for Decision Making

  • Explanation of presenting marketing data in visual formats for clarity.

  • Description of charts and dashboards used to communicate insights effectively.

  • Overview of selecting appropriate visualization methods for different data types.

  • Discussion of how visualization supports faster and better decisions.

• Customer Segmentation Techniques

  • Explanation of dividing markets into meaningful segments based on data characteristics.

  • Description of demographic, behavioral, and psychographic segmentation approaches.

  • Overview of clustering techniques used in segmentation processes.

  • Discussion of how segmentation improves targeting accuracy.

• Targeting and Positioning Strategies

  • Explanation of selecting target segments using analytical insights.

  • Description of aligning marketing messages with segment characteristics.

  • Overview of positioning strategies informed by data analysis.

  • Discussion of maintaining consistency across marketing channels.

• Predictive Analytics in Marketing

  • Explanation of predictive models used to forecast customer behavior.

  • Description of key variables influencing purchasing decisions.

  • Overview of techniques for building predictive frameworks.

  • Discussion of evaluating model performance in marketing contexts.

• Customer Lifetime Value Analysis

  • Explanation of estimating long-term customer value using data.

  • Description of factors affecting customer retention and profitability.

  • Overview of methods used to calculate and interpret lifetime value.

  • Discussion of applying these insights to strategic planning.

• Machine Learning Fundamentals for Marketing

  • Explanation of machine learning concepts and their relevance to marketing.

  • Description of supervised and unsupervised learning approaches.

  • Overview of algorithms commonly used in marketing analytics.

  • Discussion of model training and evaluation processes.

• Recommendation Systems

  • Explanation of how recommendation engines personalize customer experiences.

  • Description of collaborative and content-based filtering methods.

  • Overview of data requirements for building recommendation systems.

  • Discussion of their impact on engagement and sales performance.

• Digital Marketing Analytics

  • Explanation of tracking user behavior across digital platforms.

  • Description of metrics used in evaluating online campaigns.

  • Overview of tools that collect and analyze digital marketing data.

  • Discussion of interpreting performance indicators effectively.

• Search and Social Media Analytics

  • Explanation of analyzing search engine and social media data.

  • Description of engagement metrics and audience behavior patterns.

  • Overview of sentiment analysis in social media contexts.

  • Discussion of aligning insights with marketing objectives.

• Natural Language Processing in Marketing

  • Explanation of how text data is analyzed using NLP techniques.

  • Description of extracting insights from customer feedback and reviews.

  • Overview of sentiment and topic analysis methods.

  • Discussion of applying NLP results to marketing strategy.

• Customer Experience Analytics

  • Explanation of measuring customer interactions across touchpoints.

  • Description of analyzing feedback to improve service quality.

  • Overview of identifying pain points in customer journeys.

  • Discussion of enhancing overall customer satisfaction.

• Marketing Automation and AI Tools

  • Explanation of automation systems used in marketing operations.

  • Description of AI-powered tools for campaign management.

  • Overview of integrating automation with marketing workflows.

  • Discussion of improving efficiency through intelligent systems.

• Campaign Optimization Techniques

  • Explanation of adjusting campaigns based on performance data.

  • Description of testing and refining marketing strategies.

  • Overview of metrics used to measure optimization success.

  • Discussion of balancing cost and performance outcomes.

• Pricing and Revenue Optimization

  • Explanation of data-driven pricing strategies in competitive markets.

  • Description of factors influencing pricing decisions.

  • Overview of revenue optimization techniques.

  • Discussion of aligning pricing with customer value perception.

• Market Basket Analysis

  • Explanation of identifying relationships between purchased items.

  • Description of association rules used in analysis.

  • Overview of applications in cross-selling and upselling strategies.

  • Discussion of leveraging results to improve sales.

• Marketing Strategy Development Using Data

  • Explanation of building strategies based on analytical insights.

  • Description of aligning data findings with business objectives.

  • Overview of planning campaigns using evidence-based approaches.

  • Discussion of ensuring adaptability in strategy design.

• Performance Measurement and KPIs

  • Explanation of defining key performance indicators for marketing success.

  • Description of tracking progress against strategic goals.

  • Overview of tools used for performance measurement.

  • Discussion of continuous improvement based on results.

• Ethics and Data Privacy in Marketing

  • Explanation of ethical considerations in using customer data.

  • Description of privacy regulations affecting marketing activities.

  • Overview of responsible data usage practices.

  • Discussion of maintaining trust with customers.

• Risk Management in Data-Driven Marketing

  • Explanation of identifying risks in data usage and AI applications.

  • Description of mitigating potential issues in marketing systems.

  • Overview of compliance requirements.

  • Discussion of ensuring secure data handling.

• Integration of AI into Marketing Systems

  • Explanation of embedding AI tools into existing marketing infrastructure.

  • Description of connecting data sources and analytical platforms.

  • Overview of system interoperability challenges.

  • Discussion of maintaining system efficiency and scalability.

• Strategic Decision-Making with AI Insights

  • Explanation of using analytical outputs to support executive decisions.

  • Description of translating data insights into actionable strategies.

  • Overview of aligning insights with organizational priorities.

  • Discussion of enhancing competitive advantage through data.

• Future Trends in Data-Driven Marketing

  • Explanation of emerging technologies shaping marketing strategies.

  • Description of evolving consumer behavior in digital environments.

  • Overview of advancements in AI and analytics tools.

  • Discussion of preparing for future market developments.

• Sustaining Data-Driven Marketing Capabilities

  • Explanation of maintaining long-term analytical capabilities within organizations.

  • Description of continuous improvement in data processes.

  • Overview of adapting strategies based on changing data trends.

  • Discussion of building a culture focused on data-informed decisions.