Data Engineering Local SEO Bing Maps Extraction Playbook
In case you are in Local SEO or data analysis, you understand that clean data is an oxymoron. Listings of businesses are sloppy, chaotic, and crumbling. Although Google Maps takes the credit, Bing Maps has a well-structured, accessible dataset which can be easily accessed, although rarely used effectively, by the developer or technical marketer.
Scraping Bing Maps: What is the Data Structure?
Before we can write even a single line of code, we need to define our schema. When we search Bing Maps we are not searching a business name. Every location will have a composite object being constructed:
A typical Bing Maps scraper is a strong body of targeting these DOM elements to develop a standardized JSON object:
"id": "uniquehashorurl",
"name": "Business Name",
"category": ["Primary", "Secondary"],
"contact": {
"phone": "+1-555-0199",
"website": "[https://example.com](https://example.com)",
"addresscomponents": {
"street": "123 Main St",
"city": "Seattle",
"zip": "98101"
}
},
"metrics": {
"rating": 4.5,
"reviewcount": 128,
"haswebsite": true
},
"metadata": {
"hours": "Mon-Fri 09:00-17:00",
"coordinates": [47.6062, -122.3321]
}
}
When you look at the data in such a manner, the sales use cases are engineering problems. Here is how to solve them.
1. Programmatic Lead Generation (The Filtering Pipeline)
The Problem: Sales teams require leads.
The Technical solution: Scrape the Local Pack.
Filter: Apply logic during the scrape.
if (website == null) -> High priority (Needs digital services).
if (rating < 3.0) -> High priority (Needs reputation management).
Enrichment: Run a domain through an API (such as Hunter.io or similar) to resolve emails.
This transforms a list into a qualified database.
NAP Consistency Audits (Data Normalization)
The Idea: Name, Address, Phone (NAP) should be the same in the web.
The Engineering strategy: This is a diffing issue. You possess a “Source of Truth” (your CRM or Client Database), and the Wild Data (Bing Maps).
Normalization: You have to normalize before comparing. “St.” and “Street” must be the same tokens. Eliminate formatting of phone numbers (555) 123-4567 -> 5551234567.
Fuzzy Matching: Processing algorithm such as the Levenshtein Distance to match the business name on Bing with those in your database.
Output: A report is generated with rows matching the distance greater than a safety threshold.
Geospatial Competitor Analysis
The Objective: Know who owns the market.
The Engineering Process: This is a cluster analysis.
Grid Search: rather than searching by city, create a grid of latitude/longitude points.
Density Mapping: Scrape the top 5 results of each point on the grid.
Visualization: Plot the results on a map to see the lines of territory. In case Competitor A is at the top of 80 percent of the grid points, they control that territory.
4 Scaling: Scaling the Architecture
Scaling a single page is unproblematic. Scraping 50,000 places needs architecture.
The Alternative: Anti-Bot and DOM Modification
Bing is like any other modern SPA (Single Page Application): it works with dynamic classes and loads pages asynchronously.
Rotating Proxies: It is necessary to avoid IP bans. You require a pool of residential IPs.
Headless Browsers: Headless requests or curl will not work since the JavaScript has to run. You require utilities such as Puppeteer, Playwright or Selenium.
Concurrency: To accelerate this, you use workers concurrently. Nevertheless, to be a polite bot and not provoke aggressive CAPTCHAs, you need to use rate limiting.
Being a full-time job
Building a maintenance-free scraper will consume half your time. The DOM is modified, classes are obfuscated, and CAPTCHAs get improved.
To teams that wish to work on analysis, not maintenance, over-the-counter tool stacks such as Public Scraper simplify the complexity. They take care of the proxies, the DOM parsing and solving the CAPTCHA and emit the clean JSON/CSV which we have defined above. This is sometimes the wiser path to lean engineering staffs.
The Workflow: Query to Database
Should you implement this nowadays, you can follow this Sprint plan:
Phase 1: Schema and Targeting
Define your Primary Key. Typically, a combination of Phone Number + Postal Code forms a solid unique identifier to deduplicate.
Phase 2: The Smoke Check
Write your script (or tool) on a single city/category pair.
Check: Did I get the phone number?
review: Did the address format into columns, or does it come out as a big string?
Check: Are the characters encoding correctly (UTF-8 issues when listing internationally)?
Phase 3: The ETL (Extract, Transform, Load)
Extract: Run the batch job.
Transform: Standardize the open hours into a readable format.
Flag records with It drives the windows system and voice search scenarios where the data should be accurate. Opening this stream means not only that you are scraping maps but that you are constructing a formal index of the physical world that can be looked up by your business.
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