The materials cover data collection and interpretation for decision-making. Includes 28 tools and testing examples without promising outcomes.
Language of course:
Ukrainian
Format of the event:
Online
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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.
Course Description
Personal feedback from teachers Throughout your studies, you will be able to share experiences, complete assignments in groups, analyze cases, and receive constructive feedback on homework from industry experts.
COURSE LECTURERS: Dmytro Tsapiy Product Growth Manager at Universe (Genesis) Former Growth Marketing Lead at Glovo Has over 8 years of experience in marketing Headed the marketing analytics and insights function at Colgate-Palmolive Company Valeria Solomkina Former Performance Marketing Lead at Samsung Electronics Nordic Has 10 years of experience in Digital Marketing in international projects Collaborated with brands such as: Samsung, InBev, Abbott, lifecell, Subaru, Borjomi, etc. Focused on developing data-driven strategies and implementing innovative solutions to improve the effectiveness of marketing campaigns
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