How Companies Collect Customer Intelligence from Public Platforms (Reviews, Social Data, and Professional Networks)

The best product and growth decisions come from understanding customers deeply—not from gut instinct. Customer intelligence is the practice of systematically collecting, structuring, and activating signals from public data sources: app store reviews, social conversations, professional networks, and competitor content.

This guide shows you how to build customer intelligence infrastructure using public web data. You'll learn the data sources, collection workflows, practical playbooks, and how to activate insights across product, marketing, and sales.

Customer intelligence connects scattered public signals into actionable business insights.

Table of Contents

• What Customer Intelligence Means
• Data Sources Map
• Collection Workflows
• Practical Playbooks
  • Review Mining
  • LinkedIn Signals
  • Competitor Monitoring
• Data Modeling
• Activation: Using Outputs Across Teams
• Where CrawlKit Fits
• FAQ
• Next Steps

What Customer Intelligence Means

Customer intelligence goes beyond traditional market research. It's the continuous collection and analysis of signals that reveal:

Voice of Customer (VoC): What customers actually say about your product, competitors, and the problem space—in their own words, unprompted.

Jobs-to-be-Done (JTBD) Signals: The underlying tasks customers are trying to accomplish, revealed through reviews, forum posts, and support discussions.

Market Signals: Leading indicators of market shifts—hiring patterns, funding announcements, product launches, pricing changes.

1┌─────────────────────────────────────────────────────────────┐
2│              CUSTOMER INTELLIGENCE FRAMEWORK                │
3├─────────────────────────────────────────────────────────────┤
4│                                                             │
5│   VoC Signals          JTBD Signals       Market Signals    │
6│   ───────────          ────────────       ──────────────    │
7│   • Reviews            • Feature asks     • Hiring trends   │
8│   • Social posts       • Use cases        • Funding news    │
9│   • Forum threads      • Workarounds      • Product launches│
10│   • Support tickets    • Pain points      • Pricing shifts  │
11│                                                             │
12│                         ▼                                   │
13│              ┌─────────────────────┐                        │
14│              │  Structured Data    │                        │
15│              │  + Analysis Layer   │                        │
16│              └─────────────────────┘                        │
17│                         ▼                                   │
18│   Product Insights  │  Marketing Intel  │  Sales Enablement │
19└─────────────────────────────────────────────────────────────┘

The challenge isn't finding data—it's collecting it systematically, structuring it consistently, and activating it across teams.

Data Sources Map

Not all data sources are equal. Understanding their characteristics helps you prioritize collection efforts.

Source Comparison Table

Each data source offers unique signals with different freshness, coverage, and bias profiles.

Key Considerations

Freshness: How quickly does new data appear? App reviews flow daily; LinkedIn profiles update monthly.

Coverage: Does the source cover your market? B2B software lives on G2; consumer apps on the App Store.

Bias: Every source has bias. Reviews skew negative (unhappy customers write more). LinkedIn profiles are career-optimized. Account for this in analysis.

Difficulty: Some sources require sophisticated collection (anti-bot, authentication). Others are straightforward.

Collection Workflows

Customer intelligence collection follows a consistent pattern:

1Discovery → Collection → Normalization → Extraction → Analysis → Activation

Workflow Stages Explained

1. Discovery Identify what to collect: which competitors, which review platforms, which LinkedIn segments.

2. Collection Fetch raw data from sources via APIs, crawling, or enrichment endpoints.

3. Normalization Standardize data formats—dates, ratings scales, entity names—across sources.

4. Extraction Pull structured fields from unstructured content: sentiment, topics, entities, feature mentions.

5. Analysis Aggregate, trend, and derive insights: sentiment over time, feature request frequency, competitive positioning.

6. Activation Route insights to the right teams and systems: product backlogs, marketing briefs, sales battlecards.

The six-stage pipeline transforms scattered public data into activated business intelligence.

Practical Playbooks

Let's get specific with three high-impact playbooks.

Review Mining

Goal: Extract themes, bugs, feature requests, and sentiment from app store and review platform data.

Why It Matters: Reviews are unfiltered customer voice. They reveal what marketing copy hides: real frustrations, unexpected use cases, and competitive comparisons.

Data Sources:

• App Store (iOS) / Google Play
• G2, Capterra, TrustRadius (B2B)
• Amazon product reviews
• Trustpilot, Yelp (consumer services)

Workflow:

1. Identify products to monitor (yours + top 3-5 competitors)
2. Set up scheduled collection (daily for high-volume apps)
3. Extract structured review data:
   • Rating (normalized to 1-5)
   • Review text
   • Date
   • Version (if available)
   • Platform
4. Run extraction pipeline:
   • Sentiment classification (positive/negative/neutral)
   • Topic extraction (features, bugs, UX, pricing, support)
   • Feature request detection
   • Competitor mention detection
5. Aggregate and visualize:
   • Sentiment trend over time
   • Topic frequency by product
   • Feature request rankings
   • Bug mention clusters

What to Look For:

Review mining reveals sentiment trends, feature gaps, and competitive positioning.

LinkedIn Signals

Goal: Track company growth signals, hiring velocity, and decision-maker mapping using LinkedIn data.

Why It Matters: LinkedIn is the canonical source for B2B firmographics and professional relationships. Company size changes, new hires, and role distributions reveal strategic intent.

Data Types:

Company Signals:

• Employee count (and change over time)
• Department breakdowns
• Headquarters and locations
• Industry classification
• Recent job postings

Person Signals:

• Current role and tenure
• Career trajectory
• Shared connections
• Content engagement

Workflow:

1. Define target accounts (ICP companies, competitors, prospects)
2. Enrich company profiles:
   • Pull firmographics via LinkedIn enrichment API
   • Store baseline metrics
3. Track changes over time:
   • Employee count deltas (growth/contraction)
   • New job postings (hiring intent)
   • New hires in key roles
4. Map decision-makers:
   • Identify titles matching buyer persona
   • Track tenure and seniority
   • Note reporting relationships where visible
5. Generate alerts:
   • "Company X grew 20% in engineering"
   • "New VP Sales hired at prospect Y"
   • "Competitor Z posting for product roles"

LinkedIn Signal Interpretation:

Competitor Monitoring

Goal: Track competitor website changes, pricing shifts, and content updates via screenshots and content extraction.

Why It Matters: Competitor movements—new features, pricing changes, messaging shifts—are leading indicators you can act on.

What to Monitor:

• Pricing pages — Price changes, plan restructuring, new tiers
• Feature pages — New capabilities, positioning changes
• Homepage — Messaging and value prop evolution
• Blog/changelog — Product updates, thought leadership direction
• Job postings — Strategic priorities revealed by hiring

Workflow:

1. List competitor URLs (5-10 key pages per competitor)
2. Set up scheduled monitoring:
   • Screenshots (daily or weekly)
   • Content extraction (for text-based diffing)
3. Compare against baseline:
   • Visual diff for layout/design changes
   • Text diff for content/messaging changes
4. Classify changes:
   • Minor (typos, small updates)
   • Moderate (feature additions, copy changes)
   • Major (pricing changes, positioning shifts)
5. Alert and distribute:
   • Route pricing changes to sales
   • Route feature updates to product
   • Route messaging changes to marketing

1┌────────────────┐     ┌────────────────┐     ┌────────────────┐
2│   Competitor   │     │   CrawlKit     │     │   Your Team    │
3│   Websites     │────▶│   Screenshot   │────▶│   Alerts &     │
4│                │     │   + Extract    │     │   Dashboards   │
5└────────────────┘     └────────────────┘     └────────────────┘
6        │                      │                      │
7        │                      ▼                      │
8        │              ┌────────────────┐             │
9        │              │  Visual Diff   │             │
10        │              │  + Text Diff   │             │
11        │              └────────────────┘             │
12        │                      │                      │
13        ▼                      ▼                      ▼
14   Pricing Page          Change Type            Sales Team
15   Feature Page          Classification         Product Team
16   Homepage              (minor/major)          Marketing Team

Automated competitor monitoring catches changes before your team misses them manually.

Data Modeling

Consistent data models make aggregation and analysis possible. Here are recommended schemas.

Review Schema

1{
2  "$schema": "http://json-schema.org/draft-07/schema#",
3  "type": "object",
4  "properties": {
5    "review_id": {
6      "type": "string",
7      "description": "Unique identifier (source + native ID)"
8    },
9    "source": {
10      "type": "string",
11      "enum": ["app_store", "google_play", "g2", "capterra", "trustpilot"]
12    },
13    "product_id": {
14      "type": "string",
15      "description": "Your internal product identifier"
16    },
17    "product_name": {
18      "type": "string"
19    },
20    "rating": {
21      "type": "number",
22      "minimum": 1,
23      "maximum": 5,
24      "description": "Normalized to 1-5 scale"
25    },
26    "title": {
27      "type": "string"
28    },
29    "text": {
30      "type": "string"
31    },
32    "author": {
33      "type": "string"
34    },
35    "date": {
36      "type": "string",
37      "format": "date-time"
38    },
39    "version": {
40      "type": "string",
41      "description": "App version if available"
42    },
43    "language": {
44      "type": "string"
45    },
46    "extracted": {
47      "type": "object",
48      "properties": {
49        "sentiment": {
50          "type": "string",
51          "enum": ["positive", "negative", "neutral", "mixed"]
52        },
53        "topics": {
54          "type": "array",
55          "items": {"type": "string"}
56        },
57        "feature_requests": {
58          "type": "array",
59          "items": {"type": "string"}
60        },
61        "bugs_mentioned": {
62          "type": "array",
63          "items": {"type": "string"}
64        },
65        "competitors_mentioned": {
66          "type": "array",
67          "items": {"type": "string"}
68        }
69      }
70    },
71    "collected_at": {
72      "type": "string",
73      "format": "date-time"
74    }
75  },
76  "required": ["review_id", "source", "product_id", "rating", "text", "date", "collected_at"]
77}

Company/Person Schema

1{
2  "$schema": "http://json-schema.org/draft-07/schema#",
3  "type": "object",
4  "properties": {
5    "entity_id": {
6      "type": "string",
7      "description": "Your internal entity identifier"
8    },
9    "entity_type": {
10      "type": "string",
11      "enum": ["company", "person"]
12    },
13    "source_ids": {
14      "type": "object",
15      "properties": {
16        "linkedin": {"type": "string"},
17        "crunchbase": {"type": "string"},
18        "domain": {"type": "string"}
19      },
20      "description": "IDs from source systems for deduplication"
21    },
22    "company": {
23      "type": "object",
24      "properties": {
25        "name": {"type": "string"},
26        "domain": {"type": "string"},
27        "industry": {"type": "string"},
28        "employee_count": {"type": "integer"},
29        "employee_count_range": {"type": "string"},
30        "headquarters": {
31          "type": "object",
32          "properties": {
33            "city": {"type": "string"},
34            "country": {"type": "string"}
35          }
36        },
37        "founded_year": {"type": "integer"},
38        "description": {"type": "string"},
39        "specialties": {
40          "type": "array",
41          "items": {"type": "string"}
42        }
43      }
44    },
45    "person": {
46      "type": "object",
47      "properties": {
48        "name": {"type": "string"},
49        "current_title": {"type": "string"},
50        "current_company": {"type": "string"},
51        "location": {"type": "string"},
52        "tenure_months": {"type": "integer"},
53        "seniority": {
54          "type": "string",
55          "enum": ["entry", "mid", "senior", "director", "vp", "c-level"]
56        },
57        "department": {
58          "type": "string",
59          "enum": ["engineering", "product", "sales", "marketing", "operations", "finance", "hr", "other"]
60        }
61      }
62    },
63    "enriched_at": {
64      "type": "string",
65      "format": "date-time"
66    },
67    "confidence_score": {
68      "type": "number",
69      "minimum": 0,
70      "maximum": 1
71    }
72  },
73  "required": ["entity_id", "entity_type", "enriched_at"]
74}

Deduplication & Entity Resolution

When collecting from multiple sources, duplicates are inevitable. Basic resolution strategies:

1. Exact match: Same email, same LinkedIn URL, same domain
2. Fuzzy match: Similar names + overlapping attributes (company + title)
3. Confidence scoring: Weight matches by signal strength
4. Manual review queue: Flag uncertain matches for human verification

Activation: Using Outputs Across Teams

Collected intelligence only matters if it reaches the right people at the right time.

Question → Source → Output Mapping

Activation by Team

Product Team:

• Feature request rankings → Roadmap prioritization
• Bug mention clusters → Issue triage
• Competitor feature launches → Competitive response planning
• JTBD patterns → Discovery research input

Marketing Team:

• Sentiment trends → Campaign messaging
• Competitor positioning changes → Differentiation strategy
• Customer language patterns → Copy optimization
• Thought leadership gaps → Content calendar

Sales Team:

• Account growth signals → Outbound prioritization
• Decision-maker maps → Multi-threading strategy
• Competitor pricing intel → Negotiation prep
• Recent hires → Trigger-based outreach

Customer intelligence feeds product, marketing, and sales with different outputs from shared data.

Where CrawlKit Fits

Building customer intelligence infrastructure requires reliable data collection at scale. CrawlKit provides the foundation:

Relevant Endpoints