Use Cases

Market Research & Trend Analysis

Analyze category trends, new product launches, and pricing patterns across regions to spot whitespace and inform product strategy.

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The challenge

Spotting a genuine category trend — a new ingredient gaining traction, a shift toward smaller pack sizes, a pricing tier that's growing faster than others — requires looking across many retailers and enough time to separate signal from noise. Doing that manually means browsing hundreds of product pages across multiple countries and retailers, and repeating the exercise every time you want an updated read.

Point-in-time research also misses the trend as it develops. Without a historical record of prices, launches, and category structure, it's hard to say whether something is a genuine emerging trend or a one-off blip, and by the time a report is finished, the market has often already moved.

Cross-market comparisons add another layer of difficulty — a pattern that looks like a clear trend in the US market might be flat or reversed in the UK or India, and without consistent regional data, it's easy to draw a conclusion that only holds in one market.

How it helps
How BeautyFeeds helps
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BeautyFeeds tracks structured product data across 52 retailers and 5 countries with a consistent schema, so category-level analysis doesn't require normalizing each source's taxonomy by hand. Because every price and stock check is stored as its own timestamped snapshot, you get a genuine time series to work with — not just a single current-state snapshot — making it possible to track how pricing in a category has moved over weeks or months, not just where it sits today.

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New product detection surfaces launches as they're picked up, which is useful for tracking how fast a category is expanding or which brands are pushing into it. Consistent category_1/2/3 fields make it straightforward to roll up analysis at whatever level of granularity a research question needs — a broad category like skincare, or a narrow one like vitamin C serums.

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Because the same schema applies across every country in the feed, comparing a category's pricing or launch activity between the US and UK, for example, is a matter of filtering by country rather than reconciling two differently structured datasets.

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Reviews data adds a demand signal on top of pricing and launch activity — a rising category with strong review volume and ratings reads very differently from one growing on discounting alone, and that distinction matters for any strategy recommendation built on top of the research.

See it in action
A typical workflow

How this typically plays out for a team using BeautyFeeds data.

Step 1

A market research analyst pulls a category export across all tracked retailers and countries, then segments by price tier, brand, and launch date to identify where growth is concentrated. Price history lets them check whether a category's average price is trending up or down over the analysis window, and whether that trend holds across regions or is specific to one market.

Step 2

The output typically feeds into a quarterly category report, a competitive landscape brief for a product team considering a new launch, or due diligence work supporting an investment or acquisition decision in the beauty space.

Step 3

Some teams also set up a recurring version of this analysis — re-running the same category export monthly or quarterly so trend reports are based on consistent, comparable snapshots rather than being rebuilt from scratch each time.

Relevant data fields

This use case draws on category_1/2/3, brand_name, country, price, original_price, and timestamp, spanning the Product Info and Pricing & Availability groups in our field reference, along with detected_changes for tracking new product launches over time.

average_rating and number_of_reviews from the Reviews & Ratings group are also useful additions, giving a read on how well a growing category or new entrant is actually being received rather than just how fast it's expanding.

Who uses this

Market research analysts, brand strategy teams, and investors or advisors doing category due diligence in the beauty and personal care space are the most common users of this use case.

Consultants and agencies serving beauty clients also use this data to support recurring market landscape deliverables, since a consistent underlying feed makes it easier to update a report on a regular cadence without re-doing the research from scratch each time.

Getting started

Define the category and countries you want to analyze first, then decide whether you need a single point-in-time snapshot or an ongoing feed to support recurring reporting. Both are supported, but they call for slightly different setups.

Reach out and we can help scope which fields, retailers, and countries best fit the specific research question you're trying to answer.

If your research spans multiple categories, it's often worth starting with the one where you have the most existing context, so you can sanity-check the data against what you already know before expanding the analysis further.

Common questions

How far back can I analyze trends? History accumulates from when a product first enters tracking, so the depth of trend analysis available grows over time rather than being backfilled retroactively.

Can I get country-level breakdowns in one export? Yes — country is a standard field on every record, so segmenting a single export by country doesn't require separate requests per region.

Is this suitable for a one-off report or only ongoing tracking? Both — a single export works fine for a point-in-time report, while an ongoing pull supports ongoing trend tracking as new snapshots accumulate.

Ready to put this data to work?

Tell us about your use case and we'll help you find the right plan and fields.

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