AI & Machine Learning for Market Research: From Zero to Hero

Learn with Google Colab; no software installs or technical headaches; access notebooks, datasets, and slides via Google Drive, and practice with real market research examples.

Market research gathers information on consumers' needs and behavior to tailor products for the target market, and begins with a clear objective rather than a tool.

We conduct market research to deepen understanding of people and phenomena, turning insights into better products, services, and marketing while validating ideas with A/B testing and data-driven action.

Explore primary and secondary research methodologies, including qualitative methods like interviews, ethnography, shop along, and quantitative methods such as surveys, social listening, eye tracking, and EEG, plus hybrid approaches.

Distinguish quant and qual research by comparing design, participant experience, data structure, and analysis methods, from surveys and questionnaires to interviews and thematic coding.

Define your research objective by clarifying why you're researching, what you need to learn, and how the findings will be used, while aligning stakeholders and purpose before choosing methods.

Unpack research objectives with AI by learning to document a project brief and use prompt chaining to build a multimodal market research plan, combining exploratory, descriptive, and causal methods.

Learn to ask questions that reveal insights, define research objectives, and choose method from problem first, using artificial intelligence to streamline planning, data analysis, and project management for quality data.

Discover common market research tools for qualitative and quantitative work, spanning project management, data collection, data analysis, and reporting, focusing on free tools and staying adaptable to changing software.

Explore common market research tools for data collection, data analysis, and qualitative research, comparing paid and free options and highlighting key software like survey tools, SPSS, R, Python, and Lyra.

Plan and execute a research project by moving through stages from planning and preparation to acquire, analyze, interpret, and act, using AI tools to streamline data collection and insights.

Cut through AI hype to build a practical foundation in AI, machine learning, deep learning, and generative AI, and learn how these concepts relate for market research beyond generative tools.

Take a tour of ai and generative ai tools across categories, from foundational models by OpenAI and Google Gemini to coding, video creation, writing, audio, image, and language solutions.

AI augments and streamlines market research, with humans guiding the research brief, asking valuable questions, and shaping design from opportunity identification to reporting.

Learn a proactive AI problem-solving framework for market research that defines the problem, identifies data types and tasks, selects AI methods, executes, and evaluates results.

Master a repeatable prompt engineering framework for market research by defining roles, context, data structure, step-by-step tasks, and clear output formats to optimize AI results.

Access a free, ai-powered learning platform for this course, featuring a library of market research templates for surveys and open-ended text analysis, plus prompts, experiments, quizzes, and flashcards.

Explore prompts in the market research and AI lab app, run experiments across models, and generate campaigns for Cocoa. Test knowledge with quizzes and review concepts through flashcards.

Create, store, and run prompts in the AI learning platform using official templates, dynamic variables, and multi-model experiments to compare outputs and costs for market research tasks.

Define and diagnose market research problems, map steps, and preemptively plan AI solutions. Identify suitable tools, execute iteratively, and evaluate outputs for quality, bias, and source reliability.

Explore the main data types in market research across qualitative and quantitative methods. Identify numerical, text-based, and media data, including video, audio, pictures, and output formats like transcripts and charts.

Begin with data foundation and quality to enable AI in market research. Define the problem and classify data into structured, unstructured, and semi-structured to select suitable AI tasks and methods.

Match data types to AI methods in market research, from tabular data with decision trees and boosting to text, images, and graph data with transformers, CNNs, and graph neural networks.

Explore how data structures, structured, unstructured, and semi-structured, shape AI choices, from tables with XGBoost to images with CNNs and text with language models, emphasizing task-first data understanding.

Explore how to structure raw respondent level survey data, including single and multiple choice formats, and learn to transform it into crosstabs and charts for quantitative analysis.

Study tabular data and time series data, the core structured data types in quantitative market research for AI and machine learning, with emphasis on rows, columns, sequencing, trends, and seasonality.

Clean and organize interview data by identifying speakers and redacting PII. Apply qualitative data analysis to identify themes and codes using deductive or inductive coding.

Explore unstructured data in market research by examining image data’s pixels, edges, textures, and high dimensionality, then analyze text data’s sequential meaning and context.

Explore semi-structured graph data by examining nodes, edges, topology, and directional relationships, and analyze centralities and communities to identify key nodes and clusters in social networks for market research.

Explore common challenges in cleaning and analyzing qualitative and quantitative market research data, from transcription and coding to weighting, conflicts, and creating readable cross tabs.

Identify opportunities to apply ai in market research by problem solving with generative ai, crafting and benchmarking prompts across models, and preemptively automating tasks like data analysis and questionnaire design.

Apply a task-driven approach to market research by breaking projects into granular tasks across six stages—planning, preparation, acquire, analyze, interpret, act—and using AI to enhance interviews, surveys, and secondary research.

Explore real world AI applications across market research stages—from recording and transcribing interviews to thematic analysis, survey preparation and data cleaning, to deep research on secondary sources.

Explore secondary research with deep research modes across LLMs, including Gemini, to source, extract data, and craft citation-rich reports.

Define a task, align it with a real-world problem, and apply AI methods—from supervised, unsupervised, and self-supervised learning to regression, classification, forecasting, and imputation—for market research.

Explore how generative ai handles tasks through archetypes like retrieval augmented generation and ai agents, grounding responses in private data and enabling autonomous reasoning and action.

Compare supervised and unsupervised learning, from labeled data and predictions to discovering patterns without labels. Explore common models like linear regression, clustering, PCA, neural networks, transformers, and CNNs.

Explore a framework for understanding ai models, starting with a line and linear regression, applying it to logistic regression with gradient descent for training, and form, loss, train, and evaluate.

Explore supervised and unsupervised models, from logistic regression and SVM to decision trees and boosting, and learn a four-question framework for model form, loss, training, and evaluation.

Explore how AI relates to traditional statistical and econometric techniques in market research, comparing inputs, formulation, training, evaluation, and the generalized linear model.

Apply a simple four-part framework to demystify deep learning, from linear and logistic regression to multi-layer neural networks, including CNNs, RNNs, and transformer models behind ChatGPT.

Explore deep learning fundamentals, including convolutional neural networks for image data, recurrent neural networks for text, and transformers with attention, training concepts like forward and backward propagation and gradient descent.

Explore the foundations of language modeling, the ecosystem of transformers and text generation, and the rise of multimodal and open-source generative AI models for market research.

learn notebook fundamentals, load and explore data, train and evaluate models, and view feature importances using Google Colab, pandas, numpy, and sklearn on a market research dataset.

Perform exploratory data analysis on a survey dataset by loading libraries and data with pandas, inspecting data info, handling missing values, and visualizing distributions, correlations, and box plots.

Apply linear regression from scratch in a Google Colab notebook using synthetic data with noise to predict gadget spending from science and technology interest, and interpret the fitted line.

Explore logistic regression for classification, using science and tech interest to predict high or low gadget spending, with train-test split and evaluation via confusion matrix, accuracy, precision, recall, and F1.

Build end-to-end data pipelines for market research using scikit-learn, including imputation, scaling, one-hot encoding, and transformers, ready for modeling.

Build and evaluate regression models with data pipelines, train-test splits, and feature selection, using linear and tree-based methods like lasso, ridge, elasticnet, XGBoost, and Lightgbm, with MSE RMSE and R2.

Train and evaluate classification models—logistic regression, forests, and boosting trees—treating the target as discrete classes and using accuracy, precision, recall, and F1 to measure performance.

Discover how feature importances rank inputs in regression and classification models, with Lightgbm, XGBoost, and trees, to display the top contributing features.

Learn to build a large language model app using Hugging Face APIs and Gradio and API keys to generate personas for market research leads.

Create a prompt-driven persona generator using a large language model API for market research. Define role, tasks, structure, and placeholders; test five contexts with five models and tune temperature.

Build a large language model enabled app with Gradio by wiring inputs, models, prompts, and API keys to generate customer personas, test the API, and run a shareable web interface.