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Today, access to information is fundamental for making decisions and understanding the world around us. Collecting, filtering, and analyzing data are essential for any investigation.

In this article, I will share techniques learned in Brand, Market, Product, and Self-taught research. Research is divided into three basic pillars:

Problem Definition
Data Collection
Data Analysis

1. Problem Definition

This is the foundation of research and usually stems from a hypothesis or a previous objective. It must answer key questions to filter tools and scope:

What do we want to discover or achieve? Define the main objective.
Why is it important? Justify the relevance of the study.
What will be the population or object of research? Delimit the subject of study.
What is the scenario or environment to be measured? Contextualize the problem.
How are we going to measure it? Define the metrics or KPIs.
How much time do we have to do it? Establish a schedule.

2. Data Collection

You must determine the types of data needed before choosing the right tool.

Types of Data

There are two main categories:

Qualitative (Descriptive)

Focuses on qualities and non-numerical characteristics.

Nominal: Grouped by category (Genders, Brands, Colors).
Ordinal: Grouped by hierarchy (Satisfaction level, Social class).
Binary: Only two possible answers (Positive/Negative, Yes/No).
Textual: Subjective information (Comments, Descriptions).

Quantitative (Quantities)

Focuses on numbers and statistics.

Discrete: Whole numbers (Votes, Units sold).
Continuous: Precise measurements or ranges (Temperature, Weight, Time).

Ways to Extract Information

Primary Collection

Obtaining raw data directly from original sources.

Survey: Ideal for large-scale simple selection qualitative data.
Interview: Allows obtaining direct and deep information from experts or users.
Hotjar: Tool to evaluate user behavior on websites (heatmaps, recordings).
GazeRecorder: Online eye tracking technology for usability studies.

Open-Source Intelligence (OSINT)

Use of public information available on the internet.

Perplexity: AI-powered search engine that builds arguments with cited references.
Youtube Summary: Extension to summarize YouTube videos using ChatGPT.
Bing: Search engine with integrated AI to combine information and visual inspiration.

Advanced Search Operators

Commands to filter results in search engines like Google:

filetype:pdf + your query - Search for files in specific formats.
site:example.com + your query - Search only within a specific domain.
related:example.com - Find sites with similar content.
Quotes "your phrase" - Search for exact phrases.
Hyphen -term - Exclude terms from the search (e.g., jaguar -car).

3. Data Analysis

It is the moment to obtain conclusions based on the initial approach and the collected data. It is divided into four levels:

Descriptive Analysis: Describes characteristics, patterns, and past trends.
Diagnostic Analysis: Seeks to understand the underlying causes of events.
Predictive Analysis: Uses historical data and models to predict future scenarios.
Prescriptive Analysis: Suggests the best action to take based on predictions.

Data Visualization

To facilitate decision-making, it is crucial to represent data visually:

Frameworks: Tools like User Persona, Customer Journey Map, Blue Ocean, or Empathy Map.
Mind Maps: To organize and connect ideas logically.
Line or Bar Charts: Ideal for showing growth and temporal relationships.
Radial Charts: Pie Charts or Spider Charts for comparisons.
Pyramid Diagrams: To show hierarchies or process funnels.

Mastering these documentation and research techniques will allow you to approach any project with a solid foundation, ensuring that your decisions are based on real data and deep analysis. Whether to develop a product, study a market, or learn a new skill, the method is the key to success.

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