Marketing Analytics: tools for market and audience research

Course overview

Description generated based on course syllabus and open data.

The course structures approaches to working with data in communications and products: from market and audience research to channel analysis and testing. It reviews examples with Excel, Google Analytics, Meta, Semrush, Kantar, Nielsen, data.ai, LTV calculation, and the McKinsey loop for process improvement.

Marketing analytics: who it suits and who it does not

Who it suits

• Digital specialists and marketing managers working with metrics and reports.
• Brand managers evaluating audience impact.
• Product specialists for hypothesis checks and basic forecasts.
• Business owners and executives for structured data reviews.

Who it does not suit

• Those expecting theory only without practical examples.
• Those not planning to work with numbers and tools.
• Those seeking universal templates without context adaptation.

Marketing analytics: problem → approach

• Problem: Unclear performance measurement. Approach: key metrics overview, basic attribution models, interpreting data in Google Analytics and Meta.
• Problem: No structure in market and audience research. Approach: using Kantar, Nielsen, data.ai, Semrush; formulating hypotheses and data questions.
• Problem: LTV and unit-economics complexity. Approach: Excel modeling, scenario analysis, and basic forecasts.
• Problem: Inconsistent experiments. Approach: test design, reading results, and checklists.

Marketing analytics vs alternatives: brief view

• Self-study: flexible, but risk of fragmentation and gaps in fundamentals.
• Short seminars: narrow focus, limited hands-on data work.
• Academic programs: strong theory, slower tool updates.
• Structured course in marketing analytics: coherent tools-and-cases overview without promises.

Outcomes of completing marketing analytics materials

• Defining relevant metrics and data requirements.
• Market and audience analysis for hypothesis framing.
• Basic forecasts in Excel and reading reports in Google Analytics and Meta.
• LTV calculation and cohort-based assessment.
• Standardizing analytics rituals via the McKinsey loop.

Tools in focus

• Excel, Google Analytics, Meta Ads data.
• Kantar, Nielsen, data.ai, Semrush.
• LTV, cohort analysis, test design.