How to Export Bing Maps Data: Excel, CSV & JSON Tips

Automation of Local Intelligence: Exporting Bing Maps Data to JSON, CSV or Excel.

When working in Sales Ops, Growth Engineering, or local search engine optimization, you are already aware of that very particular form of pain which is created by creating lead lists manually. Switching between a spreadsheet and a map interface, then pressing Ctrl + C / CTL + V hundreds of times, is not only mindless but also is a waste of human intelligence.

We should not only think of Bing Maps as a visual application, but as an ordered database to be searched. Whether you are creating a dataset to migrate your CRM or evaluate the saturation of the local market, the objective is to get off of manual entries and into an automated pipeline.

How to Export Bing Maps Data Excel, CSV & JSON Tips

The following is how the engineer can scrape Bing Maps, pick the most appropriate data serialization format, and cleanse the output to be used in the production.

The Data Model: What Have We Extracted?

You should define your schema before executing any script or tool. Our scrapes of Bing Maps are not mere scrapes of Bing Maps, but scrapes of particular fields we want in a structured form do programmatic outreach.

A good scrape will most likely give:

Identity: Business Name, Main division.
Contact: Web address, telephone contact number.
Geo: FULL Addres (broken down into Street, City, State, zip, country).
Social Network: Links to Facebook, Yelp, LinkedIn (where applicable).
Sentiment: Rating and Reviews Count.

Compliance Note: It is important that you know the legal environment of your area before you run any scraper. Public data scraping is not regarded as a problem, although you should not ignore terms of service and personal data policies (such as GDPR/CCPA). Should a primer be required, read Is Scraping Bing Maps Legal? A Complete Guide.

JSON vs. CSV vs. Excel: What to Choose?

The data format you use determines the way you manipulate the data below. Do not fall to CSV because it is a habit.

JavaScript Object Notation (JSON)

Represents a data format variant that presents information as data.

Best Suited: Developers, APIs and NoSQL Databases. When you intend to feed this information into a Python script, a MongoDB instance, or an automation tool, such as n8n or Zapier, then use JSON. It is much more capable of dealing with nested structures (such as a list of opening hours as a part of a business object) than it can with flat formats.

CSV (Comma Separated Values)

Best for: Bulk Import/Export. csv is the universal standard in case you are going to Salesforce, HubSpot, or a SQL database. It is light and fast to parse, but the delimiters can collide (e.g. commas within a business name).

Excel (.xlsx)

Best for: Human Analysis. In case the end-user happens to be a sales manager or a marketing analyst who requires pivot tables, filter columns, and manually inspect leads, bypass the conversion step and export to Excel.

Pro Workflow: Export all three to be sure. Make the JSON your backup source of truth, the imports should be done with the help of CSV, the Excel file should be forwarded to the business team.

The Process: Query to Clean Data.

Although it is possible to create your own scraper with Puppeteer or Selenium, it can take a full-time employee to maintain the selectors against such a complicated platform as Bing Maps. To conduct this walkthrough, we are going to presume the usage of a specialized tool such as Public Scraper Ultimate Edition since it does all the parsing of the DOM and rotating proxies.

Step 1: Input Configuration

You have to reason like a search engine. Imprecise data is obtained through imprecise inputs.

Keywords: Be specific. Referred to as Lawyers, should be changed to Family Law Attorney or Personal Injury Lawyer.
Locations: Set up your geo-fencing. Do you target a specific city, list of zip codes or 10km radius around a convention center?

Single Step 2: Infrastructure (Proxies)

When you are scraping 50 leads, then it is all right. You will be rate-limited or blocked in case you scrape 5,000.

The Solution: Switch on Proxy Rotation.

Configuration: Configure the scraper to turn the IP after each city or after each n requests. This resembles nature in terms of traffic allocation.

Step 3: Execution and Export

Run the scraper. The data will be displayed in real-time grids in a tool such as Public Scraper. Upon completion, choose the format you would like to work with (JSON/CSV/XLSX) and set a naming scheme to use version control (e.g., bingdentistsmanchester2023-10-27.json).

Data Hygiene: Mopping the Result.

Raw data is hardly ever production ready. As soon as you have your export, employ the following principles of engineering to purify it:

Normalization: Split addresses appropriately. You do not want in one cell 123 Main St, New York, NY. You prefer Address, City and State in individual columns/keys.
Deduplication (Deduping):
- The Weak Key: Business Name (Too many Starbucks).
- Firm Key: Domain + Phone Number. When these are the same it is the same entity.
Validation:
- Ensure URLs start with https://.
- Format telephone numbers to E.164 format (ex. +14155552671) in case you intend to use it in a dialer.

Example: The JSON Schema

When you are a developer, and you are putting this into a pipeline, a clean JSON object as created by the scraper would look like this:

{
  "title": "Roofs Plus",
  "category": "Roofing Contractor",
  "contact": {
    "phone": "+1 845-339-3912",
    "website": "[https://roofsplus.com/](https://roofsplus.com/)",
    "social": {
      "facebook": "[https://www.facebook.com/Jancewicz33/](https://www.facebook.com/Jancewicz33/)",
      "yelp": "[https://www.yelp.ca/biz/roofs-plus-kingston](https://www.yelp.ca/biz/roofs-plus-kingston)"
    }
  },
  "location": {
    "address": "295 Foxhall Ave",
    "city": "Kingston",
    "state": "NY",
    "zip": "12401",
    "country": "USA"
  },
  "meta": {
    "rating": "4.0",
    "reviewcount": 4,
    "Source keyword": "roofing in Kingston, NY."
  }
}

Summary

Exportation of the Bing Maps data does not have to be a nightmare. It is nothing more than a data engineering issue: Query -> Extract -> Serialize -> Clean.

To make this workflow work and run without writing the scraping logic itself, scraping tools such as Public Scraper Ultimate Edition offer the engine to go all the way to the data analysis stage.

Ready to start?

Discover the Tool: Public Scraper Ultimate Edition.

Deep Dive: Bing Map Scraper – Leads Made Easy.

Strategy: How-To Scale Your Bing Maps Scraping Projects.

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