Use Cases

Pricing & Discount Intelligence

Track competitor prices and promotions in real time, and turn raw price movements into a pricing strategy you can act on.

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The challenge

Beauty retailers change prices constantly — flash sales, seasonal markdowns, loyalty-tier discounts, and one-off promotions can all shift a product's price multiple times in a single week. For a brand or retailer trying to stay competitive, manually checking competitor pages for price changes doesn't scale past a handful of SKUs, and it's easy to miss a markdown until it's already cost you sales or margin.

The problem compounds across regions. A brand selling through Sephora, Ulta, and Amazon in the US, UK, and India needs to track pricing consistently across all of them, in the right currency, without spending analyst hours refreshing product pages by hand.

There's also a strategic cost beyond the operational one. Without a reliable read on where competitors sit at any given moment, pricing decisions end up reactive rather than planned — a brand discovers it's been underpriced for weeks, or over-discounted relative to the category, only when a quarterly review finally surfaces the gap.

How it helps
How BeautyFeeds helps
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BeautyFeeds records a price, original_price, currency, and discount_percentage for every tracked product on every check — and because each check is stored as its own timestamped snapshot rather than overwriting the last value, you get a full price history per product instead of just today's number.

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That history makes it possible to distinguish a genuine markdown from a temporary coupon, spot recurring promotional cadences (for example, a competitor who discounts every first weekend of the month), and catch a price increase the same day it happens rather than weeks later. Availability is tracked alongside price, so you can also see when a discount coincided with a restock or a sellout — useful context that price alone doesn't give you.

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Because every retailer in the feed uses the same schema, a pricing comparison across Sephora, Ulta, and Nykaa doesn't require separate parsing logic per source — the same query works whether the underlying product came from three different sites or thirty.

See it in action
A typical workflow

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

Step 1

A pricing analyst on a beauty brand's revenue team pulls the daily API feed for their own SKUs plus a defined set of competitor products. They compare current prices against the stored price history to flag anything that moved more than a set threshold since the last check, and cross-reference discount_percentage to separate real promotions from list-price noise.

Step 2

From there, the team sets internal alert rules — for example, notify pricing leads if a key competitor drops below a certain price point, or if three or more competitors in a category discount within the same week, which often signals a broader seasonal promotion the brand should match or plan around.

Step 3

Over a full quarter, the accumulated price history also supports a retrospective: which competitors discount most aggressively, how long a typical promotion lasts, and whether a brand's own promotional calendar is out of step with the rest of the category.

Relevant data fields

The fields that matter most for this use case are price, original_price, currency, discount_percentage, availability, product_status, timestamp, and last_checked — all part of the Pricing & Availability group in our field reference. Pairing this with product_name, brand_name, and category fields lets you group and compare pricing at the category or brand level rather than product-by-product.

For teams tracking MAP compliance specifically, product_status and detected_changes are useful for catching unauthorized resellers who list below an agreed floor price, since a sudden price change on an otherwise stable listing is often the first sign of a compliance issue.

Who uses this

This use case is most common among pricing and revenue management teams at beauty brands, marketplace sellers managing MAP (minimum advertised price) compliance, and category managers at multi-brand retailers who need to benchmark their assortment against competitors on a recurring basis.

It also shows up on the finance side — teams building revenue forecasts often want a historical price signal to model how sensitive demand is to a discount, rather than assuming a flat price throughout a forecast period.

Getting started

Start by identifying the specific products and competitors you need to track — a focused list of 20 to 50 SKUs is usually enough to see meaningful patterns without overwhelming your team with noise. From there, decide on a refresh cadence that matches how fast your category moves; a daily check is common for fast-moving categories like makeup, while weekly may be enough for more stable categories.

If you're not sure which plan covers the fields and update frequency you need, our team can help you map this use case to the right setup before you commit to anything.

It also helps to agree internally on what counts as an actionable price change before you turn on alerts — a one percent fluctuation is very different from a ten percent markdown, and setting that threshold up front keeps the signal useful instead of turning into noise your team starts to ignore.

Common questions

How far back does price history go? History starts accumulating from the moment a product is added to tracking — we don't backfill historical prices from before a product was tracked, since that data simply wasn't collected yet.

Can I track products that aren't yet in the feed? Yes — if a product you need isn't already tracked, it can typically be added as part of setting up your monitoring, whether it's your own catalog or a competitor's.

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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