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Intent, months ahead

Pinterest Data, Delivered

Public Pinterest content and engagement metrics, normalised to the same schema as every other platform we cover and pushed into your stack. Nothing to build, nothing to maintain.

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source specification

Source
pinterest.com
Objects
Pins, Boards, Public profiles, Search and category feeds, Save counts
Formats
JSON · CSV · Parquet
Refresh
Continuous to daily
Scope
Public content only

about the platform

What makes Pinterest different

Pinterest is the only major social platform where the dominant behaviour is planning rather than reacting. People save things they intend to buy, build, cook, or wear at some point in the future, which means the signal here leads purchase by weeks or months rather than trailing it. The board is the structural unit that makes this legible: a pin saved to a board named for a wedding, a kitchen renovation, or a season tells you not just what someone liked but what project it belongs to. Save counts are consequently a better intent proxy than likes are anywhere else, because saving is an act of planning rather than approval.

why teams track it

Why Pinterest data is worth having

For home, wedding, fashion, food, and interiors categories, Pinterest is the earliest public read on next season’s demand — merchandising teams use rising save volume in a category to plan assortment before the trend is visible anywhere else. It is also the platform where a product image’s own performance can be measured directly, since a pin is the product image and its save count is the response to it.

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applications

What teams build on Pinterest data

Seasonal demand forecasting

Save volume in a category rises months ahead of the buying season it belongs to. For assortment planning, that lead time is the difference between reacting to a trend and being stocked for it.

Board context as intent classification

The board a pin is saved to names the project behind the save. The same product pinned to "first apartment" and to "wedding" is two different customers with two different budgets.

Creative and image performance testing

A pin is a product image, and its save count is a direct measurement of that image’s pull. Comparing save rates across your own imagery is a cheap read on which creative direction works.

Competitive assortment tracking

Which of a competitor’s products are being saved, at what rate, and into what board contexts — a view of their demand curve built from public behaviour rather than guesswork.

Colour and material trend detection

Aggregate pin attributes across a category surface palette and material shifts early, which for interiors and fashion is the input that buying decisions actually turn on.

Referral link analysis

Pins carry destination links, so you can see which retailers a category’s saves are pointing at and how that distribution moves over time.

what you get back

Fields in the Pinterest feed

Platform-specific detail on top of the unified social schema, so a dashboard built on one platform works on the next without a rewrite.

  • Pin title, description, and destination link
  • Save and reaction counts per pin
  • Board name, description, and pin count — the planning context around a save
  • Public profile attributes — follower count, bio, verified merchant status
  • Category and topic assignment
  • Image dimensions and dominant colour where exposed

Public surfaces only

We collect what a logged-out visitor can see. No authentication is bypassed, no private content is touched, and no attempt is made to deanonymise anyone.

One schema across platforms

A post from any network arrives with the same field names, so adding a platform does not mean rewriting anything downstream.

Deletion propagates

When content is removed at source it drops out of subsequent deliveries, and retention windows on delivered data are configurable per engagement.

10+
Platforms covered
<60s
Detection latency
3
Delivery formats
99.9%
Uptime SLA

related sources

Tracked alongside Pinterest

One platform is a partial view. These deliver on the same schema, so combining them is a configuration change rather than a project.

Snapchat

The youngest audience, publicly

Threads

Text conversation, Meta side

Instagram

Where visual commerce starts

All platforms

Instagram, TikTok, X, YouTube and more

49 retail sources

Pair social signal with catalogue and price data

AI datasets

Use this feed as model training data

See every data product

questions

Frequently Asked Questions

Everything you need to know before you send us your first request.

Because the behaviour is different, not because the audience is bigger. A save is a planning action rather than a reaction, so the signal leads purchase instead of trailing it. For home, wedding, interiors, food, and fashion categories, that lead time is worth more than a larger volume of engagement data collected after the fact.
Yes, and it is the field most people underestimate. Board name and description are collected alongside the pin, which is what turns "this product was saved" into "this product was saved into a kitchen renovation project" — a difference that matters for anything intent-related.
We deliver image URLs and metadata by default rather than the binaries. Image collection is available where the use case supports it and is scoped explicitly in the engagement, since storage and licensing considerations both change once media is included.
Daily is the usual cadence and it is sufficient for almost every use case here. Because the underlying behaviour is planning rather than reaction, the aggregate picture does not move fast enough to justify continuous collection — spending the budget on category breadth is a better trade than spending it on refresh rate.
Into the systems you already run. Normalised records land as JSON, CSV, or Parquet in S3, GCS, or Azure Blob, or straight into Snowflake or BigQuery, on whatever cadence you set. Webhooks push matches to your endpoints as they are detected, which is how most customers wire alerting. A solutions engineer sets the schema, cadence, and destination with you during onboarding.
Keeping the collector working is our responsibility. Extractors are monitored continuously, and when the platform changes its structure we patch upstream while the schema we deliver to you stays fixed. If a change causes a coverage gap we tell you which window was affected rather than quietly returning fewer records.
Pricing is scoped per engagement — the number of tracked terms, handles, or communities, the refresh cadence, and the delivery destinations involved. Historical backfill is quoted separately. Every engagement starts with a free sample run against your own brand terms, so you can judge the data before committing.

Still have questions?

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Talk to us about your sources and volume. We'll return a sample dataset from your target sites before you commit to anything.

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