Webinar RecordingAI in Web Analytics: Where and How to Use It, and What's Better Not to Delegate

We'll break down the real capabilities and limitations of artificial intelligence, drawing on recent performance benchmarks and model research. You'll learn about the boundaries of AI's responsibility and which tasks are worth delegating to it, and which ones are better not to risk. We'll discuss how the role of the analyst is evolving — from routine report pulling to the position of a strategic business partner for marketing and product.

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AI in Web Analytics: Where and How to Use It, and What's Better Not to Delegate

Who is this Course For?
Webinar Syllabus
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Who is this Course For?

For Analysts
- Who already use AI but want to understand what tasks it's still suited for.
- Who want to learn how to build skills and systems, rather than just accumulating prompts.
- Who strive to automate routine work and become a business partner for marketing and product.
For Marketers
- Who have heard about AI in analytics but don't know where to start.
- Who want to use AI to find insights and build reports.
- For whom it's important to understand the limits of AI, in order to avoid making decisions based on "AI hallucinations."
For Managers and Team Leads
- Who want to understand what can realistically be delegated to AI, and what can't.
- For whom it's important to understand how the role of the analyst is transforming, in order to effectively distribute tasks within the marketing department.
- Who strive to build processes where AI strengthens the team rather than replacing common sense.

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Webinar Syllabus

Main part
What we will talk about
Mistakes and myths in integrating AI into business processes.
Principles for selecting tasks for initial automation.
What You Will Achieve
You'll get a clear understanding of where AI genuinely helps and where it's powerless, based on global research. You'll learn how to correctly choose the first tasks for automation and how to assess whether your business data is ready to work with neural networks.
What we will talk about
AI's capabilities in writing SQL queries.
An analysis of benchmarks and error statistics of modern LLMs.
The problem of error accumulation in complex processes.
What You Will Achieve
You'll learn the limits of accuracy of modern language models in generating SQL code, based on industry benchmarks. You'll understand the specifics of error accumulation in complex analytical processes and the principles of building data quality control systems.
What we will talk about
The difference between AI, ML, and LLM in the context of analytics.
The division of roles between rule-based systems and language models in the data quality monitoring process.
What You Will Achieve
You'll get a clear understanding of the differences between the concepts of AI, ML, and LLM. You'll understand the concept of dividing roles between mathematical anomaly detection using ML and data analysis using LLMs.
What we will talk about
Stages of working with data that can be safely delegated to AI.
The boundaries of algorithms' responsibility and the danger of "confidently wrong" answers.
The risks of the absence of business context in the work of neural networks.
What You Will Achieve
You'll learn which stages of information processing and structuring make sense to hand off to AI. You'll understand the boundaries of algorithms' responsibility and the specifics of how language models work without accounting for unique business context.
What we will talk about
Using AI to help with code and finding logical errors.
A breakdown of a practical case: grouping Landing Pages in GA4 by URL patterns using regular expressions.
Key principles of interaction: the concept of Human-in-the-loop and a focus on data context.
What You Will Achieve
You'll break down the algorithm for applying AI to code refactoring and automating the creation of regular expressions for GA4. You'll understand the concept of Human-in-the-loop and the principles of building sustainable data-working skills instead of copying ready-made prompts.
What we will talk about
The evolution of the analyst's role.
Blind spots of AI.
What You Will Achieve
You'll understand the direction in which the analyst profession is evolving and the new market requirements in the age of automation. You'll learn about the risks of AI working with incorrectly configured tracking and the limitations of algorithms in evaluating real business processes.