Data & Marketing Analytics

Overview

Marketing has evolved into a highly analytical, technology-driven field, with concepts like ‘big data’, ‘data analytics’ and ‘marketing analytics’ now integral to success. This workshop offers a comprehensive, hands-on approach to leveraging analytics across both traditional and digital channels, incorporating the latest advancements in AI, machine learning, and data ecosystems.

This practical workshop focuses on using KPIs, metrics, and analytical tools to enhance marketing effectiveness and maximise Return on Marketing Investment (ROMI). You’ll gain insights into applying descriptive, predictive, and prescriptive analytics to evaluate past and current performance while forecasting future outcomes.

The course will enable you to:

• Develop a structured approach to marketing analytics, using best practices to drive data-informed decision-making across all channels.
• Apply advanced analytics techniques to assess market performance, customer value, and optimise marketing strategies.
• Utilise AI, machine learning, and Big Data to identify actionable insights, build predictive models, and support strategic growth.
• Integrate data analysis tools and digital analytics to refine customer journey touchpoints, optimise the marketing mix, and maximise ROMI.

Delegates will receive an Excel® workbook with templates, formulas, and spreadsheets, equipping them with practical tools to immediately apply their new skills.

Who should attend?

This workshop is ideal for those who understand the importance of data-driven decision-making and seek practical tools to optimise and prove marketing performance. It offers a structured approach to analytics, enhancing strategic insights and driving growth. While a basic understanding of statistics is helpful, it is not essential.

Learning outcomes

• Design and implement a structured approach to data and marketing analytics, integrating key concepts (data, information, analytics, insights) and best practices
• Differentiate between Descriptive, Predictive, and Prescriptive analytics, applying each to drive strategic business outcomes
• Identify different data types (categorical, numerical) and select the most appropriate analysis tools to extract actionable insights
• Use data to design new products, assess customer value (CLTV), and conduct market appraisals for demand, competition, and market share
• Develop data-driven segmentation, targeting, and competitive positioning strategies to enhance market performance and identify growth opportunities
• Apply statistical methods to distinguish correlation from causation and build models such as Market Response Models (MRM) and Marketing Mix Models (MMM) to inform strategic planning
• Incorporate attribution modelling to optimise the marketing mix, ensuring effective allocation of resources across channels
• Integrate digital analytics, including SEM, email metrics, and social media sentiment, to optimise customer journey touchpoints, using both quantitative and qualitative data for channel evaluation
• Leverage AI, Machine Learning, and Big Data to identify patterns and develop predictive optimisation models that support data-driven growth
• Create a 90-day analytics action plan, utilising data ecosystems to guide investment decisions and maximise ROI