How We Build Search-Based Market Research

Ioana Ciudin
January 29, 2026
A real example using La Sportiva, climbing, and the UK market. Market research is often treated as a report. In reality, it should be treated as a system. In this article, I’ll walk through a real Market Insights project, from setup to first insights, to show how search data can be used responsibly to understand a market, not just describe it. This is not a theoretical example. It’s a real setup, built and refined step by step.
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1. Everything starts with the objective

Before looking at any data, I define the scope.

Let’s assume we’re working for La Sportiva.
The first decision is not about tools or keywords, but about focus.

In this case:

  • I’m interested strictly in climbing
  • not the full brand portfolio
  • and more specifically, climbing footwear

Even within a single brand, not everything is relevant for every decision.
A good market research starts by being deliberately narrow.

For geography, I chose United Kingdom, in English.
The reason is simple: it’s a mature market and allows me to work with clean, international search semantics.

2. Defining how the data will be read

Once the scope is clear, the next step is deciding how I want to analyze the data.

Before collecting anything, I define the segmentation logic.

For this project, I set up:

  • Brands
    La Sportiva, plus relevant competitors (Scarpa, Tenaya, Ocun, Black Diamond, 5.10, etc.)
  • Categories
    Footwear, clothing, equipment
  • Person / audience
    Generic, women, men, kids
  • Consumer Decision Journey (CDJ)
    Awareness, consideration, purchase
  • Optional retailer layer
    Amazon, Bergfreunde, and other major UK retailers

Anything that doesn’t match a rule is automatically grouped into Other.
That’s important: no data is dropped, only structured.

At this stage, nothing is “right” or “wrong”.
The goal is to create a clear reading framework.

3. Research topics: the heavy lifting

This is the most time-consuming part, and also the most valuable.

Instead of relying on generic keyword lists, I build research topics by combining:

  • La Sportiva’s site structure
  • Google search results
  • large specialty retailers (for semantic discovery)

For example, for Climbing Shoes, I include:

  • the generic term
  • La Sportiva product models
  • competitor models
  • real variations people actually search for (bouldering, beginner, performance, etc.)

The goal is not perfection from day one.
The goal is semantic coverage.

A strong setup is built once, then refined over time.

4. First look at the data

Once the setup is complete, I move into the dashboards.

The first thing I look at is market size.

For the defined universe in the UK, the data shows roughly 2 million searches per month.

When I zoom into Footwear, the picture becomes very clear:

  • “climbing shoes” dominates the category
  • well-known models appear consistently
  • and brand distribution starts to tell a story

One insight stands out immediately:

👉 Based on search demand, Scarpa appears larger than La Sportiva on climbing shoes in the UK.

This is not something I would have confidently assumed without data.

5. Stopping before drawing conclusions

This is where methodology matters.

Search data shows interest and demand, not market share.
So before treating this as a conclusion, I do two things.

First: I validate the setup itself

While reviewing the dashboards, I notice:

  • some queries that should be awareness are classified as consideration
  • men / women segmentation shows distortions

I go back into the settings and find the issues:

  • an incorrect CDJ rule pushing “shoes” into consideration
  • rule order causing women queries to fall under men

I fix the rules, reprocess the data, and review the results again.

This step is critical.
Early insights help you improve the setup, not just read it.

6. Validating insights with the client

After corrections, the pattern remains:
search demand for Scarpa is higher than for La Sportiva in the UK, in climbing footwear.

At this point, the next step is not more dashboards.

👉 The next step is validation with the client.

I always confirm insights like this using:

  • sales data
  • distribution coverage
  • known market share information

This removes blind spots that search alone cannot cover, such as:

  • offline sales dominance
  • distribution gaps
  • availability differences

Search creates strong hypotheses.
Business data confirms or contextualizes them.

7. What this enables next

Once the setup is stable, the same data can be used for many decisions:

  • brand vs competitor evolution over time
  • seasonality and peak demand periods
  • identifying hero products vs long-tail products
  • building negative keyword lists to avoid irrelevant traffic in Google Ads
  • aligning SEO, paid search, and content strategy around real demand

All without rebuilding the research from scratch.

8. Final thought

Market research should not be a one-off report.

It should be a living system:

  • grounded in real demand
  • transparent in its logic
  • adjustable when assumptions are wrong
  • and always validated against business reality

That’s what search-based Market Insights is designed to be.

Not an answer generator, but a framework for better decisions.

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