Deep Place Enrichment - SpiderIQ by Di-Atomic

Overview

SpiderMapsEnrich is a second-stage enrichment worker designed to extract detailed data from individual Google Maps place pages. It complements SpiderMaps by providing deep data extraction that isn’t available from bulk search results.

Two-Stage Architecture:

SpiderMaps discovers businesses via search queries (100+ results per query)
SpiderMapsEnrich enriches individual places with reviews, photos, and analysis

Why Use SpiderMapsEnrich?

SpiderMaps searches Google Maps and extracts basic business info from feed results. However, detailed data like reviews, photos, and popular times requires visiting each place page individually. SpiderMapsEnrich:

Visits individual place pages to get full details
Uses IP isolation to avoid rate limiting (different VPS from discovery)
Extracts deep data including reviews, photos, and sentiment analysis
Hosts photos on SpiderMedia for permanent storage

Data Comparison

Field	SpiderMaps	SpiderMapsEnrich
Name, Address, Phone	Yes	Yes
Website, Rating	Yes	Yes
Review Count	Yes	Yes
Place ID / CID	Yes	Yes
Individual Reviews	No	Yes (up to 200)
Review Analysis	No	Yes
Negative Reviews	No	Yes
Owner Responses	No	Yes
Photo URLs	No	Yes (up to 50)
Review Tags	No	Yes
Menu Link	No	Yes
Street View URL	No	Yes
Popular Times	No	Yes
Related Places	No	Yes (snowball)

Quick Start

1. Get a CID from SpiderMaps Results

SpiderMaps returns a cid field for each business. This is the most reliable identifier for enrichment.

// SpiderMaps result
{
  "name": "Joe's Pizza",
  "cid": "0x87c9f95545605943:0x6bad649f33559c94",
  "place_id": "ChIJN1t_tDeuEmsRUsoyG83frY4",
  "rating": 4.5
}

2. Submit Enrichment Job

import requests
import time

API = "https://spideriq.ai/api/v1"
TOKEN = "<your_token>"
headers = {"Authorization": f"Bearer {TOKEN}", "Content-Type": "application/json"}

# Submit enrichment job
response = requests.post(
    f"{API}/jobs/spiderMapsEnrich/submit",
    headers=headers,
    json={
        "payload": {
            "google_cid": "0x87c9f95545605943:0x6bad649f33559c94",
            "enrich_options": {
                "reviews": {"enabled": True, "max_count": 50},
                "photos": {"enabled": True, "max_count": 10},
                "store_images": True
            }
        }
    }
)

job = response.json()
print(f"Job submitted: {job['job_id']}")

# Poll for results
while True:
    status = requests.get(
        f"{API}/jobs/{job['job_id']}/status",
        headers=headers
    ).json()

    if status['status'] == 'completed':
        break
    elif status['status'] == 'failed':
        print(f"Job failed: {status.get('error_message')}")
        break

    time.sleep(5)

# Get results
results = requests.get(
    f"{API}/jobs/{job['job_id']}/results",
    headers=headers
).json()

print(f"Business: {results['data']['data']['name']}")
print(f"Reviews extracted: {len(results['data']['reviews'])}")
print(f"Photos hosted: {len(results['data']['photos'])}")

Review Analysis

SpiderMapsEnrich automatically analyzes extracted reviews to provide actionable insights.

Rating Distribution

See how reviews break down by star rating:

{
  "review_analysis": {
    "rating_distribution": {
      "5_star": 35,
      "4_star": 8,
      "3_star": 4,
      "2_star": 2,
      "1_star": 1
    }
  }
}

Negative Review Detection

Automatically identify and categorize negative reviews (1-2 stars):

{
  "review_analysis": {
    "negative_count": 3,
    "negative_percentage": 6.0,
    "negative_reviews": [
      "Service was terribly slow...",
      "Food came out cold and overpriced..."
    ],
    "common_complaints": ["service", "food_quality", "value"]
  }
}

Complaint Categories

Category	Keywords Detected
`service`	slow service, rude, ignored, unfriendly, poor service
`food_quality`	cold food, stale, undercooked, bland, tasteless
`cleanliness`	dirty, filthy, unclean, bugs, smell, disgusting
`value`	overpriced, expensive, not worth, ripoff, small portions
`wait_time`	long wait, slow, took forever, hour wait
`management`	manager, owner, refused, no refund, unprofessional

Owner Response Tracking

Track how businesses respond to negative feedback:

{
  "review_analysis": {
    "owner_response_rate_negative": 66.7
  }
}

Competitor Research: Use negative_only: true to focus exclusively on competitor weaknesses for competitive analysis.

Photo Storage

Photos are automatically uploaded to SpiderMedia (per-client storage) and returned as permanent URLs.

Hosted URLs

{
  "photos": [
    "https://media.spideriq.ai/client-cli_xxx/gmaps/gmaps_enrich_0x87c9f95545605943_1.jpg",
    "https://media.spideriq.ai/client-cli_xxx/gmaps/gmaps_enrich_0x87c9f95545605943_2.jpg"
  ],
  "photos_original": [
    "https://lh5.googleusercontent.com/p/...",
    "https://lh5.googleusercontent.com/p/..."
  ]
}

SpiderMedia Integration: Each client has a dedicated storage bucket. Photos are organized in the gmaps/ folder with consistent naming for easy management.

Disable Storage

To skip photo hosting and get original Google URLs only:

{
  "payload": {
    "google_cid": "0x...",
    "enrich_options": {
      "photos": {"enabled": true, "max_count": 10},
      "store_images": false
    }
  }
}

Two-Stage Workflow

Complete Pipeline: Discovery + Enrichment

import requests
import time

API = "https://spideriq.ai/api/v1"
TOKEN = "<your_token>"
headers = {"Authorization": f"Bearer {TOKEN}", "Content-Type": "application/json"}

# Stage 1: Discover businesses with SpiderMaps
print("Stage 1: Discovering businesses...")
discovery_job = requests.post(
    f"{API}/jobs/spiderMaps/submit",
    headers=headers,
    json={
        "payload": {
            "search_query": "pizza restaurant New York, NY",
            "max_results": 20
        }
    }
).json()

# Wait for discovery
while True:
    status = requests.get(f"{API}/jobs/{discovery_job['job_id']}/status", headers=headers).json()
    if status['status'] in ['completed', 'failed']:
        break
    time.sleep(3)

discovery_results = requests.get(
    f"{API}/jobs/{discovery_job['job_id']}/results",
    headers=headers
).json()

businesses = discovery_results['data']['businesses']
print(f"Found {len(businesses)} businesses")

# Stage 2: Enrich top-rated businesses
print("\nStage 2: Enriching top businesses...")
top_businesses = sorted(businesses, key=lambda x: x.get('rating', 0), reverse=True)[:5]

enriched_data = []
for biz in top_businesses:
    cid = biz.get('cid')
    if not cid:
        continue

    print(f"  Enriching: {biz['name']}...")

    enrich_job = requests.post(
        f"{API}/jobs/spiderMapsEnrich/submit",
        headers=headers,
        json={
            "payload": {
                "google_cid": cid,
                "original_data": biz,  # Merge with discovery data
                "enrich_options": {
                    "reviews": {"enabled": True, "max_count": 50},
                    "photos": {"enabled": True, "max_count": 5}
                }
            }
        }
    ).json()

    # Wait for enrichment
    while True:
        status = requests.get(f"{API}/jobs/{enrich_job['job_id']}/status", headers=headers).json()
        if status['status'] in ['completed', 'failed']:
            break
        time.sleep(5)

    if status['status'] == 'completed':
        results = requests.get(f"{API}/jobs/{enrich_job['job_id']}/results", headers=headers).json()
        enriched_data.append(results['data'])

        analysis = results['data'].get('review_analysis', {})
        print(f"    Reviews: {analysis.get('total_extracted', 0)}")
        print(f"    Negative: {analysis.get('negative_percentage', 0):.1f}%")

print(f"\nEnriched {len(enriched_data)} businesses with detailed review data")

Snowball Expansion

Discover related places by enabling snowball mode. SpiderMapsEnrich extracts “People also search for” and similar sections.

# Enable snowball discovery
job = requests.post(
    f"{API}/jobs/spiderMapsEnrich/submit",
    headers=headers,
    json={
        "payload": {
            "google_cid": "0x87c9f95545605943:0x6bad649f33559c94",
            "snowball": {
                "enabled": True,
                "max_depth": 2,      # How many levels deep
                "max_places_per_seed": 10  # Places per level
            }
        }
    }
).json()

Snowball Diagram

Seed Place (depth 0)
    +-- Related Place 1 (depth 1)
    |       +-- Related 1.1 (depth 2)
    |       +-- Related 1.2 (depth 2)
    +-- Related Place 2 (depth 1)
            +-- Related 2.1 (depth 2)

IP Isolation: Each snowball level automatically uses a different VPS to avoid rate limiting. The current VPS is added to exclude_vps for child jobs.

Review Options

Control how reviews are extracted and returned:

`include_all`	`negative_only`	Behavior
`true`	`false`	Default - Returns all reviews + analysis
`true`	`true`	Returns only 1-2 star reviews
`false`	`false`	Empty reviews array, only analysis
`false`	`true`	Negative reviews from analysis only

Example: Negative Reviews Only

# Focus on competitor weaknesses
job = requests.post(
    f"{API}/jobs/spiderMapsEnrich/submit",
    headers=headers,
    json={
        "payload": {
            "google_cid": "0x...",
            "enrich_options": {
                "reviews": {
                    "enabled": True,
                    "max_count": 100,
                    "negative_only": True  # Only 1-2 star reviews
                }
            }
        }
    }
).json()

IP Isolation

For large-scale enrichment, use IP isolation to avoid rate limiting:

# Discover from one VPS, enrich from another
discovery_vps = "llm3"  # Known from metadata

enrich_job = requests.post(
    f"{API}/jobs/spiderMapsEnrich/submit",
    headers=headers,
    json={
        "payload": {
            "google_cid": cid,
            "exclude_vps": [discovery_vps],  # Don't use same VPS
            "use_proxy": True  # Use mobile proxy
        }
    }
).json()

Use Cases

1. Competitor Review Analysis

Extract and analyze competitor reviews to identify weaknesses:

competitors = [
    "0x87c9f95545605943:0x6bad649f33559c94",  # Competitor A
    "0x47c708e4cc62845f:0x2d80199bcc9f805c",  # Competitor B
]

for cid in competitors:
    job = submit_enrich_job(cid, reviews_max=100, negative_only=True)
    results = wait_for_results(job['job_id'])

    complaints = results['data']['review_analysis']['common_complaints']
    print(f"Competitor weaknesses: {', '.join(complaints)}")

2. Visual Content Collection

Build a gallery of business photos:

job = requests.post(
    f"{API}/jobs/spiderMapsEnrich/submit",
    headers=headers,
    json={
        "payload": {
            "google_cid": cid,
            "enrich_options": {
                "reviews": {"enabled": False},  # Skip reviews
                "photos": {"enabled": True, "max_count": 50},
                "store_images": True  # Host on SpiderMedia
            }
        }
    }
).json()

3. Market Research Report

Combine discovery + enrichment for comprehensive market analysis:

# 1. Discover all pizza restaurants in area
# 2. Enrich top 20 by rating
# 3. Aggregate review analysis

report = {
    "total_businesses": len(businesses),
    "avg_rating": sum(b['rating'] for b in businesses) / len(businesses),
    "common_complaints": aggregate_complaints(enriched_data),
    "owner_response_rate": avg_response_rate(enriched_data)
}

Processing Time

Configuration	Typical Time
Basic enrichment (no reviews)	15-30 seconds
With 50 reviews	30-60 seconds
With 200 reviews	60-120 seconds
With proxy	Add 15-30 seconds

Best Practices

Use CIDs over Place IDs: CIDs (hex format like 0x...:0x...) are more reliable and stable than Place IDs.

Chain with SpiderMaps: Use SpiderMaps for bulk discovery, then SpiderMapsEnrich for targeted deep dives on high-value results.

IP Isolation: For large batches, use exclude_vps to distribute requests across different servers.

Rate Limiting: Google Maps has rate limits. Process enrichment jobs in batches with delays between submissions.

Next Steps

SpiderMapsEnrich API

Complete API reference

SpiderMaps Discovery

Bulk business discovery

SpiderMedia Storage

Photo and video hosting

Error Handling

Handle job failures

Guides

​Overview

​Why Use SpiderMapsEnrich?

​Data Comparison

​Quick Start

​1. Get a CID from SpiderMaps Results

​2. Submit Enrichment Job

​Review Analysis

​Rating Distribution

​Negative Review Detection

​Complaint Categories

​Owner Response Tracking

​Photo Storage

​Hosted URLs

​Disable Storage

​Two-Stage Workflow

​Complete Pipeline: Discovery + Enrichment

​Snowball Expansion

​Snowball Diagram

​Review Options

​Example: Negative Reviews Only

​IP Isolation

​Use Cases

​1. Competitor Review Analysis

​2. Visual Content Collection

​3. Market Research Report

​Processing Time

​Best Practices

​Next Steps

SpiderMapsEnrich API

SpiderMaps Discovery

SpiderMedia Storage

Error Handling

Overview

Why Use SpiderMapsEnrich?

Data Comparison

Quick Start

1. Get a CID from SpiderMaps Results

2. Submit Enrichment Job

Review Analysis

Rating Distribution

Negative Review Detection

Complaint Categories

Owner Response Tracking

Photo Storage

Hosted URLs

Disable Storage

Two-Stage Workflow

Complete Pipeline: Discovery + Enrichment

Snowball Expansion

Snowball Diagram

Review Options

Example: Negative Reviews Only

IP Isolation

Use Cases

1. Competitor Review Analysis

2. Visual Content Collection

3. Market Research Report

Processing Time

Best Practices

Next Steps