> ## Documentation Index
> Fetch the complete documentation index at: https://docs.orbbit.co/llms.txt
> Use this file to discover all available pages before exploring further.

# CPG Overview

> How brands, products, and prices fit together in consumer packaged goods, and which data set answers which question.

CPG — consumer packaged goods — is everything sold in a package to shoppers: food, drinks, snacks, personal care, household goods. Orbbit's CPG data answers two questions about a brand: **what it makes**, and **what it sells online, and for how much**.

## How the pieces fit

A CPG brand buys ingredients, makes a packaged product, and sells it — in stores, to wholesalers, or straight to consumers online. Each data set covers one part of that path.

| Question                                            | Data set                                              | What you get                                                                                                                            |
| --------------------------------------------------- | ----------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| What does it cost to make?                          | [Commodities](/market-data/commodities) (Market data) | The market price of raw ingredients such as butter, cheese, and milk. Applies to any industry, which is why it lives under Market data. |
| What's in the product?                              | [Branded food](/industry-data/cpg/branded-food)       | The label of a packaged food: ingredients, nutrition, serving size, barcode, and who makes it.                                          |
| What does the brand sell online, and at what price? | [D2C › Shopify](/industry-data/cpg/d2c/shopify)       | A brand's own online store: its profile, every product and variant with its price, and how it groups products into collections.         |

D2C means direct to consumer: the brand sells from its own website instead of through a retailer.

## Putting them together

The data sets share words you can match on — a brand name, an ingredient, a category — so you can connect them in your own code. For example:

* **Ingredient exposure.** Search [Branded food](/industry-data/cpg/branded-food) for products whose ingredients contain `butter`, then pull butter prices from [Commodities](/market-data/commodities) to see how the market price of that ingredient has moved.
* **Price positioning.** Search [D2C › Shopify](/industry-data/cpg/d2c/shopify) stores in the `coffee_and_tea` category and compare their `median_price` to see where a brand sits against its peers.

<Note>
  There is no shared ID that links a packaged food in Branded food to the same item in a Shopify store. Matching across the two is done by brand or product name in your own code, and won't always find a match.
</Note>

## Where the data comes from

| Data set      | `source`   | Publisher                                            |
| ------------- | ---------- | ---------------------------------------------------- |
| Branded food  | `usda-fdc` | USDA FoodData Central, submitted by the brand owners |
| D2C › Shopify | `shopify`  | Public storefronts of online stores built on Shopify |