> [!NOTE]
> **Note:** The brands dataset is available for the United States, Canada, the United Kingdom, and Australia.

Places Insights provides brand information for many categories of places. For
example:

- For the category of "ATMs, Banks, and Credit Unions", the brands data contains an entry for each of the brands PNC, UBS, and Chase banks.
- For the category "Automotive Rentals", the data contains an entry for each of the brands Budget, Hertz, and Thrifty.

A typical use case for querying the brands dataset is to analyze brand
presence or join it with the places dataset to answer questions such as:

- What is the count of all stores by brand in an area?
- What is the count of my top three competitor brands in the area?
- What is the count of brands of a specific category, such as "Fitness" or "Gas Station", in the area?

## About the brands dataset

The brands dataset is supported in the US, the United Kingdom, Australia,
and Canada.
For example, the brands dataset for the US is named
`places_insights___us.brands`.

### Brands dataset schema

The schema for the brands dataset defines three fields:

- `id`: The brand ID.
- `name`: The brand name, such as "Hertz" or "Chase".
- `category`: The brand type, such as "Gas Station", "Food and Drink", or "Lodging". For a list of possible values, see [Category
  values](https://developers.google.com/maps/documentation/placesinsights/brands#category-values).

## Query brands data

The recommended method to analyze brands data is using [Places Count
Functions](https://developers.google.com/maps/documentation/placesinsights/get-started#select_the_optimal_places_count_function).
Places Count functions natively support filtering by brand IDs using the
`brand_ids` parameter, returning exact counts (including zero) and sample Place
IDs without requiring complex SQL `JOIN` operations.

### Step 1: Find the brand ID

The [places dataset schema](https://developers.google.com/maps/documentation/placesinsights/reference.md.txt)
defines the `brand_ids` field. If a place in the places dataset is associated
with a brand, its `brand_ids` field contains the corresponding brand ID.

To filter by a brand, first query the `brands` table to find the corresponding
brand ID for the brand of interest:

```googlesql
SELECT id, name
FROM PROJECT_NAME.places_insights___us.brands
WHERE name = "McDonald's"
```

### Step 2: Use brand_ids in a Places Count function

After obtaining the brand ID, pass it into the `brand_ids` filter parameter.

In this example, you query the density of McDonald's restaurants within 2,000
meters of the Empire State Building in New York City using
`PLACES_COUNT_PER_H3`:

```googlesql
SELECT *
FROM PROJECT_NAME.places_insights___us.PLACES_COUNT_PER_H3(
  JSON_OBJECT(
      'geography', ST_BUFFER(ST_GEOGPOINT(-73.9857, 40.7484), 2000),
      'business_status', ['OPERATIONAL'],
      'brand_ids', ["13992718219781496405"], -- McDonald's brand ID
      'h3_resolution', 8
  )
);
```

You can also use `PLACES_COUNT_V2` to batch-count brand locations across custom
search areas:

```googlesql
SELECT *
FROM PROJECT_NAME.places_insights___us.PLACES_COUNT_V2(
  TABLE PROJECT_NAME.YOUR_DATASET.my_search_areas,
  JSON_OBJECT(
      'geography_radius', 2000,
      'business_status', ['OPERATIONAL'],
      'brand_ids', ["13992718219781496405"]
  )
);
```

## Category values

The `category` field for a brand can contain the following values:

| Category type value |
|---|
| `ATMs, Banks and Credit Unions` |
| `Automotive and Parts Dealers` |
| `Automotive Rentals` |
| `Automotive Services` |
| `Dental` |
| `Electric Vehicle Charging Stations` |
| `Electronics Retailers` |
| `Fitness` |
| `Food and Drink` |
| `Gas Station` |
| `Grocery and Liquor` |
| `Health and Personal Care Retailers` |
| `Hospital` |
| `Lodging` |
| `Merchandise Retail` |
| `Movie Theater` |
| `Parking` |
| `Telecommunications` |