Introduction to Advanced Statistical Techniques

Understand how and when advanced statistical techniques are used, and learn to match each one to the business question it answers.

The growth of integrated solutions, DIY platforms, and AI assistants means advanced techniques such as segmentation, conjoint, and MaxDiff are within reach of more researchers than ever. But having the tools is not the same as knowing which technique a problem calls for, what data it needs, or when the output should not be trusted.

And those tools are meant to help answer some of the highest-stakes business decisions like:

• Size: How big is the market, and which segment do we go after first?
• Prioritize: Which features, messages, or benefits actually drive choice?
• Optimize: What price, bundle, and configuration win?

This course provides a map of the terrain, and it starts from the business decision rather than the statistical method.   By the end of the course, you’ll know exactly which technique fits where, what data it will require and the strengths and weaknesses of the approach relative to the business question at hand. 

Core techniques covered in depth include: Factor Analysis, Cluster Analysis (Segmentation), Correspondence Analysis (Perceptual Mapping), Regression (Key Drivers), Conjoint Analysis, and MaxDiff. Also covered and placed in context are: market sizing (TAM/SAM/SOM), TURF, and different pricing approaches (Gabor Granger and Van Westendorp). This course is specifically focused on using survey data, rather than transactional / large database information.

The course balances teaching with discussion and applied practice and is software agnostic.  No advanced statistical knowledge is assumed and open-source code will be shared as much as possible as well as prompts and skill files where applicable.  Real-world case studies from tech, SaaS, healthcare, CPG and more will bring each of the business problems (and the research solution) to life, including one company — Mill, the modern food-recycling brand — followed from sizing its market, to prioritizing its claims, to pricing its product, so you see the full decision system play out end to end.

Learning outcomes

• Start from a business decision and select the technique that answers it
• Understand the difference between techniques that uncover structure (e.g. factor and cluster analysis) and those that explain or predict an outcome (e.g. regression and conjoint)
• Appreciate the strengths, weaknesses, and data requirements of each key technique
• Build practical confidence choosing and pressure-testing techniques through hands-on exercises

Who will benefit
People who want a practical, decision-led overview of advanced quantitative techniques: those who commission or interpret research, researchers broadening their role, and anyone considering specialising in advanced quant. No prior statistical training is required.

Course delivery mode
Online, across two half-day sessions.

Trainer Biography

Megan Peitz is the Founder and CEO of Numerious, a strategic quantitative consulting firm that helps companies make smarter decisions through advanced analytics.  

A passionate educator and speaker, Megan is known for making advanced quantitative methods accessible to insights, product, marketing, and research teams regardless of their statistical background. Through Numerious, she champions curiosity, innovation, open-source tools and education-forward consulting, helping teams not only get stronger research outcomes but also build the confidence to understand and apply the methods behind them.

Megan completed her Master's Degree in Mathematics & Statistics from Georgetown University (USA), has received multiple best paper awards at the Sawtooth Research Conference (2019, 2024, 2025), has been published in the Journal of Choice Modeling (2019) and has helped some of the world’s largest companies, including Google, Docusign, Lululemon, Notion, and more, fine tune their products and services with research.