Identify Net-New Field Sales Leads with Places Insights

Conventional business to business lead generation often relies on purchasing static directories or industry lists to define territory potential. However, these static points of interest (POI) datasets go stale almost immediately. Because they often lack up-to-date operational status or granular place type categorization, field sales teams risk wasting valuable time chasing businesses that are permanently closed, miscategorized, or irrelevant to their ideal customer profile.

This guide provides a workflow to bridge that gap using Places Insights and the Places API. By mapping your current book of business to the Place IDs, you can use BigQuery to isolate every operational business in a territory that isn't already in your customer relationship management (CRM) database. This guide shows you how to build that exclusion engine to deliver a hyper-targeted, verified lead list to your field reps.

Diagram showing existing CRM data processed using Places API and BigQuery Places Insights to generate verified net-new leads.

Example Application

Consider a Point of Sale (POS) provider planning a field sales expansion in New York City. Typically, the organization would pull a report on the total food and beverage establishments per zip code. This approach carries the risk of sales reps relying on stale data, like permanently closed locations, or irrelevant leads, such as a private catering kitchen without a storefront. Imagine instead a modernized approach using Places Insights, which leverages Google Maps' global scale and up-to-date data validated across multiple sources. Places Insights supports nearly 500 place categories and over 70 attributes, allowing you to refine your prospects with high precision based on specific business types (like scandinavian_restaurant), store hours, and service offerings (such as accepts_credit_cards). By cross-referencing Places Insights with your internal CRM, you can provide your sales team with a hyper-targeted list of high-potential, uncontacted prospective customers.

Solution Workflow

This guide provides a technical framework to create a dynamic "Leads Map" that automatically filters out your current book of business, leaving only net-new, operational leads for your sales team to pursue.

The Four-Step Architecture

  1. Define your Target Place Types: Map your ideal customer profiles to Place Types.
  2. Identify High-Potential Areas: Execute Places Count functions in BigQuery to generate density heat maps of operational target businesses.
  3. Normalize CRM Data to Place IDs: Process unstructured CRM records through a data-cleansing pipeline, utilizing the Address Validation, Geocoding, and Places APIs, to find the Place IDs for your existing customers.
  4. Perform Whitespace Exclusion: Join your CRM Place IDs against your Places Insights data in BigQuery to dynamically filter out existing customers and output a net-new lead list.

Prerequisites

Before you begin, ensure you have the following:

  • Google Cloud Project:

    • A Google Cloud project with billing enabled.

  • Data Access:

    • Places Insights subscription in BigQuery.
    • Your own CRM dataset (e.g., a BigQuery table) containing existing customer business names and addresses to act as your exclusion list.

  • Google Maps Platform:

  • IAM Permissions:

    • Ensure your user or service account has the following IAM roles to execute queries and manage your dataset:
      Role ID
      BigQuery Data Editor roles/bigquery.dataEditor
      BigQuery User roles/bigquery.user

  • Cost Awareness:

    • This tutorial uses billable Google Cloud components. Be aware of potential costs related to:
      • BigQuery: Charged for compute slots used or data processed during query execution.
      • Places Insights: Charged based on query usage.
      • Google Maps Platform: Charged per request for the Address Validation API, Geocoding API, and Text Search API.

Step 1: Define your Target Place Types

Places Insights supports almost 500 place categories and over 70 attributes (such as store hours, payment types, and operational status). Querying the entire dataset indiscriminately is inefficient and costly.

As a foundational step, use an LLM like Gemini to translate your internal customer profiles into Place Types, used when building a query for Places Insights. This macro-level taxonomy definition ensures your subsequent BigQuery searches are highly targeted, reducing compute processing overhead.

For example, if you are designing a workflow for a point of sale system, you can provide Gemini with the list of Place types and use the following prompt:

"Act as a market analyst. Out of the supported Google Maps Place Types, which are the primary targets for a point of sale system provider? Justify your decision."

Based on this prompt, Gemini would analyze the taxonomy and return a targeted subset of relevant place types to use in your BigQuery types filter:

Main Category Justification Key Place Types
Food and Drink Requires fast transaction processing, table management, order ticketing, and tip handling. restaurant, bar, cafe, coffee_shop
Shopping Needs robust inventory tracking, barcode scanning, return processing, and loyalty integrations. clothing_store, grocery_store, supermarket, convenience_store
Services and Health & Wellness Requires integrated appointment booking, scheduling, customer profiles, and commission tracking. hair_salon, beauty_salon, spa, massage
Entertainment, Recreation, and Sports Demands rapid handling of customer rushes, digital ticket scanning, and fast concession sales. movie_theater, amusement_park, bowling_alley, stadium

For the purposes of this guide, we are going to focus on the suggested place types for the Food and Drink category.

Step 2: Extract Business Counts to Identify High-Potential Areas

To pinpoint areas of opportunity, you first need a macro-view of business density. You can achieve this by executing Places Count functions (such as PLACES_COUNT_PER_H3 or PLACES_COUNT_PER_GEO) in BigQuery.

While you can query the dataset directly, Places Count functions are predefined, optimized SQL queries that don't enforce the standard aggregation threshold of 5 minimum places (Standard direct queries will omit rows with 1–4 businesses; however, these functions allow you to see exactly where even a single prospect exists). Crucially, these functions return an array of up to 250 Place IDs per geographic area using the sample_place_ids column. This provides both the statistical heat map for your territory planners and the foundational identifiers needed for lead generation.

The following query demonstrates how to dynamically retrieve a complex polygon (the entire boundary of New York City) using a public dataset, and then passing that geography into the Places Count function. By utilizing the H3 spatial index at a broader resolution (8) across the city, you can generate a macro-level density map.

Furthermore, by selecting all columns (SELECT *), the function returns the geography column, a polygon representing the H3 cell. This lets you immediately import your BigQuery results into business intelligence tools (like Looker Studio) to create filled map visualizations that visually reveal market hotspots.

-- Illustrative logic: Extracting target business counts per H3 cell across New York City
DECLARE geo GEOGRAPHY;

-- Get the geography for New York City using the Overture Maps public dataset
SET geo = (SELECT geometry FROM `bigquery-public-data.overture_maps.division_area`
  WHERE country = 'US' AND subtype = 'locality' AND names.primary = 'New York' LIMIT 1);

SELECT *
FROM `YOUR_PROJECT_NAME.places_insights___us.PLACES_COUNT_PER_H3`(
  JSON_OBJECT(
      'geography', geo,
      'h3_resolution', 8,
      'types',['restaurant', 'bar', 'cafe', 'coffee_shop'],
      'business_status', ['OPERATIONAL']
  )
)
ORDER BY count DESC;

Map visualization showing green H3 hexes of varying opacity over New York City, indicating high business density in Manhattan.

As seen in the resulting visualization, there are distinctly high-density areas of target businesses throughout Manhattan. For the remainder of this document, we will zoom in and focus our lead generation efforts on one of these high-potential zones: the area near Union Square.

Step 3: Normalize your CRM Data to Place IDs

To perform an exclusion analysis, you first need to translate your CRM records into Place IDs. Since CRM data is often unstructured, passing raw text into search APIs yields poor match rates. Use this two-step pipeline to cleanse addresses, account for regional API coverage, and guarantee you extract the correct Establishment Place IDs for BigQuery.

Assume you have the following 5 restaurant customers in your CRM located in New York City:

Place Name Address
Boucherie Union Square 225 Park Ave S, New York, NY 10003, United States
Gramercy Tavern 42 E 20th St, New York, NY 10003, United States
Barn Joo Union Square 35 Union Square W, New York, NY 10003, United States
LOS TACOS No.1 200 Park Ave S, New York, NY 10003, United States
Union Square Cafe 101 E 19th St, New York, NY 10003, United States

Because these records consist of unstructured text, you cannot join them directly against Places Insights data in BigQuery. Instead, process each row through the following pipeline to standardize the text and extract the Place ID.

Step 3a: Address Cleansing and Direct Matching

Standardize your address data first. Choose your API based on the target country:

Option 1: Address Validation API

For supported regions, pass the concatenated CRM Business Name and Address into the API. Inspect the response's result.geocode.placeTypes array:

  • Establishment Match: If it contains establishment or point_of_interest, the API successfully resolved the business. Append this placeId to your dataset and move to the next CRM record. No further API calls are needed for this entry.
  • Non-Establishment Match: If it does not contain those business types, the API couldn't definitively confirm the business entity. The returned Place ID represents a geographic feature (such as a building, street, or city). Don't use this Place ID for BigQuery, as it will fail your exclusion joins. Instead, save the result.address.formattedAddress and proceed to Step 3b.

Option 2: Geocoding API

For regions not supported by Address Validation, pass only the CRM Address into the Geocoding API. Don't include the business name, as the Geocoding API may return unpredictable results. Extract the resulting formattedAddress and proceed to Step 3b.

Advanced Architecture: Handling Unstructured Data with LLMs

If your CRM data is extremely poor, such as business names and addresses mixed into a single free-text notes field, pre-process the records with an LLM like Gemini. You can prompt Gemini to cleanly parse the business name from the location before feeding them into this pipeline.

Step 3b: Resolve the Business Entity

If Step 3a only returns a cleansed address, concatenate it with the original CRM Business Name and pass it to the Text Search API. Standardizing the address first significantly improves your match rate.

To optimize for performance and cost, use a Field Mask (X-Goog-FieldMask: places.id) and set "pageSize": 1 to ensure only the top match Place ID is returned.

Example Text Search Request:

curl -X POST -d '{
  "textQuery" : "Gramercy Tavern 42 E 20th St, New York, NY 10003-1324, USA",
  "pageSize": 1
}' \
-H 'Content-Type: application/json' -H 'X-Goog-Api-Key: YOUR_API_KEY' \
-H 'X-Goog-FieldMask: places.id' \
'https://places.googleapis.com/v1/places:searchText'

Pipeline Output

After processing your CRM records through this two-step pipeline, whether the ID was successfully extracted in Step 3a or resolved using Text Search in Step 3b, your final goal is to append a new place_id column to your dataset. This resulting table is now ready to be uploaded into BigQuery as your exclusion list.

Place Name Address Place ID
Boucherie Union Square 225 Park Ave S, New York, NY 10003, United States ChIJc1Vf7KFZwokR1YL2Rn9oxi8
Gramercy Tavern 42 E 20th St, New York, NY 10003, United States ChIJvSQIgqFZwokRFYQbJdzceSs
Barn Joo Union Square 35 Union Square W, New York, NY 10003, United States ChIJQ7XpyqNZwokRQpVfvGEViWM
LOS TACOS No.1 200 Park Ave S, New York, NY 10003, United States ChIJFZh0PABZwokRVzoJu0o-mLY
Union Square Cafe 101 E 19th St, New York, NY 10003, United States ChIJxTHke6JZwokRCLWVd99eDBw

Step 4: Perform Whitespace Exclusion Analysis in BigQuery

With your existing customers mapped to Place IDs, use the Places Count functions to find the net-new leads.

In this example, we will search for operational target businesses (restaurants, bars, cafes, and coffee shops) within an 850-meter radius of Union Square (40.73595, -73.99043). To get a more granular view for street-level routing, we will increase the PLACES_COUNT_PER_H3 function to resolution 10.

Because the function returns the Place IDs as an array in the sample_place_ids column, we must UNNEST the array to put each prospective business on its own row. Then, we perform a LEFT JOIN against our known customer Place IDs.

To prove the exclusion logic works for this demonstration, the query below uses a CASE statement to flag the results rather than filtering them out entirely. It also explicitly sorts the existing customers to the very top of the results table so you can verify they were successfully matched.

The SQL Query

WITH existing_customers AS (
  -- 1. Simulate the uploaded CRM table
  SELECT * FROM UNNEST([
    'ChIJc1Vf7KFZwokR1YL2Rn9oxi8', -- Boucherie Union Square
    'ChIJvSQIgqFZwokRFYQbJdzceSs', -- Gramercy Tavern
    'ChIJQ7XpyqNZwokRQpVfvGEViWM', -- Barn Joo Union Square
    'ChIJFZh0PABZwokRVzoJu0o-mLY', -- LOS TACOS No.1
    'ChIJxTHke6JZwokRCLWVd99eDBw'  -- Union Square Cafe
  ]) AS place_id
),

target_area_businesses AS (
  -- 2. Query Places Insights for target businesses in the radius
  SELECT
    h3_cell_index,
    place_id
  FROM `places_insights___us.PLACES_COUNT_PER_H3`(
    JSON_OBJECT(
      'geography', ST_GEOGPOINT(-73.99043, 40.73595),
      'geography_radius', 850,
      'h3_resolution', 10,
      'types',['restaurant', 'bar', 'cafe', 'coffee_shop'],
      'business_status', ['OPERATIONAL']
    )
  ),
  UNNEST(sample_place_ids) AS place_id
)

-- 3. The "Proof" Output: Flag them instead of filtering them out
SELECT
  t.h3_cell_index,
  t.place_id,
  -- Flag whether the LEFT JOIN found a match in the CRM table
  CASE
    WHEN e.place_id IS NOT NULL THEN 'Existing Customer (To Be Excluded)'
    ELSE 'Net-New Lead'
  END AS lead_status,
  CONCAT('https://www.google.com/maps/search/?api=1&query=Place&query_place_id=', t.place_id) AS actionable_maps_url
FROM target_area_businesses t
LEFT JOIN existing_customers e
  ON t.place_id = e.place_id
ORDER BY
  -- Explicitly sort the existing customers to the top (0 comes before 1)
  CASE WHEN e.place_id IS NOT NULL THEN 0 ELSE 1 END ASC;

Query Results

Here is an excerpt of the query output, showing how the existing customers are successfully identified and separated from the net-new leads within the same granular H3 cells.

Notice how the query utilizes a CONCAT statement to construct a cross-platform Maps URL, using the place_id. This automatically generates the actionable_maps_url column, providing your sales team with an instant, clickable link to load the exact business in the Google Maps mobile app or a browser.

h3_cell_index place_id lead_status actionable_maps_url
8a2a100d2767fff ChIJQ7XpyqNZwokRQpVfvGEViWM Existing Customer (To Be Excluded) https://www.google.com/maps/search/?api=1&query=Place&query_place_id=ChIJQ7XpyqNZwokRQpVfvGEViWM
8a2a100d20effff ChIJvSQIgqFZwokRFYQbJdzceSs Existing Customer (To Be Excluded) https://www.google.com/maps/search/?api=1&query=Place&query_place_id=ChIJvSQIgqFZwokRFYQbJdzceSs
8a2a100d2397fff ChIJc1Vf7KFZwokR1YL2Rn9oxi8 Existing Customer (To Be Excluded) https://www.google.com/maps/search/?api=1&query=Place&query_place_id=ChIJc1Vf7KFZwokR1YL2Rn9oxi8
8a2a100d2397fff ChIJFZh0PABZwokRVzoJu0o-mLY Existing Customer (To Be Excluded) https://www.google.com/maps/search/?api=1&query=Place&query_place_id=ChIJFZh0PABZwokRVzoJu0o-mLY
8a2a100d23b7fff ChIJxTHke6JZwokRCLWVd99eDBw Existing Customer (To Be Excluded) https://www.google.com/maps/search/?api=1&query=Place&query_place_id=ChIJxTHke6JZwokRCLWVd99eDBw
8a2a1072c96ffff ChIJ6atD-WRZwokRULgcZ4TWin8 Net-New Lead https://www.google.com/maps/search/?api=1&query=Place&query_place_id=ChIJ6atD-WRZwokRULgcZ4TWin8
8a2a1072c96ffff ChIJ09yg-llZwokRKAgp0jg6TCU Net-New Lead https://www.google.com/maps/search/?api=1&query=Place&query_place_id=ChIJ09yg-llZwokRKAgp0jg6TCU

Visualize Leads with the Places UI Kit

Instead of providing a raw Maps URL, you can pass the place_ids directly into the Places UI Kit to build a rich, internal lead-generation dashboard for your sales team. Available across platforms, you can drop in the prebuilt components for Web, Android, and iOS. These components automatically display rich POI data, like photos, ratings, and operational hours, without requiring you to write frontend UI code or handle API responses manually.

Data Limits

The Places Count functions return a maximum of 250 Place IDs per geographic cell in the sample_place_ids array. If an area is extremely dense, your generated lead list for that specific cell will be capped at 250. To ensure you capture all leads in highly dense markets, consider the following strategies:

  • Use specific query filters: Instead of grouping multiple types into one query (like the example above), run separate queries for each individual place type.
  • Reduce the spatial scope: Decrease the overall search area using a smaller geography_radius, or divide the area into smaller, more granular buckets by increasing the H3 resolution (up to resolution 11).
  • Adjusting Resolution by Density: When analyzing territories with varying population densities, dynamically adjust your search size to avoid hitting the 250 Place ID limit. Use a broader H3 resolution (e.g., 6 or 7) or a larger geography_radius in rural areas where businesses are spread out. Conversely, use a highly granular resolution (e.g., 10 or 11) in dense urban areas to ensure you capture every prospective lead without truncating your list.

Production Query

Once you verify that the existing customers are successfully identified, you can revert to the production version of the query. Replace the final SELECT block with the following WHERE clause to permanently filter out your existing book of business:

SELECT
  t.h3_cell_index,
  t.place_id,
  CONCAT('https://www.google.com/maps/search/?api=1&query=Place&query_place_id=', t.place_id) AS actionable_maps_url
FROM target_area_businesses t
LEFT JOIN existing_customers e
  ON t.place_id = e.place_id
WHERE e.place_id IS NULL; -- Filters out the CRM matches

Architectural Governance and Compliance

To maintain a higher-performance and compliant system, adhere to these standards:

  1. Place IDs as your Persistent Identifier: Aside from Place ID, Google Maps Terms of Service prohibits storing or caching of the individual POI data returned from the Places API (such as phone numbers and contact details).Use Place IDs as your persistent identifier for recurring whitespace analysis.
  2. Ensure Attribute Freshness with Real-time API calls: Use Place IDs to make "just-in-time" calls to the Place Details API to make sure that your sales person has the most up-to-date business and contact information for the place. Alternatively, as demonstrated in the query output, you can dynamically construct Google Maps URLs to provide your sales team with direct links out to the business profiles on Google Maps.

Conclusion

By standardizing on the Place ID as your primary key, you successfully bridged the gap between high-level market analysis and actionable, ground-level sales operations. This architecture bypasses the inaccuracies of conventional population-based targeting, utilizes serverless data warehousing for heavy computational joins, and strictly adheres to cost-management and compliance best practices at the API layer.

Next Actions

  • Request access to the Places Insights sample dataset.
  • Subscribe to the Places Insights dataset using BigQuery data exchange listings to access the sample or full country data.
  • Review the filter parameters reference to fine-tune your BigQuery SQL queries based on business attributes and types.
  • Implement dynamic Places API lookups in your CRM or sales routing application to expose up-to-date, compliant contact details for the generated net-new leads.

Contributors