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ContentsquareLinkedIn

Your team didn’t spend last year short on data.

Source

LinkedInJan 7, 2026
likes
4

Post

Your team didn’t spend last year short on data. They spent it short on answers. 14 tabs open. 3 reports pulled. And still the same question: “Where’s the drop happening?” That’s the real problem. Not the numbers. The chase. In 2026, teams won’t spend hours tracking a drop. Sense, Contentsquare’s AI layer, points straight to what’s going wrong, even if it’s one small form field causing users to drop off. → Less hunting for problems → More fixing them It’s resolution time: How is your team planning to use AI to cut to the chase this year? 💬 #NewYearsResolution #AI #Analytics

linkedin.com/feed/update/urn:li:activity:7414652136266813444Read the full source

Extracted by Autobound

From the Signal API record
Signal
LinkedIn

What this signalsCompany posts often show what the team is pushing right now.

The full record

From the Signal API record

Topics and mentions

Tags

  • Artificial Intelligence
  • Analytics & Insights
  • User Experience
  • Pain Point

Extraction

Detected
Jan 8, 2026
signal_type
linkedin-post-company
signal_subtype
linkedinPost

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The API returns more than this page shows

This page shows a preview. The full linkedin-post-company record in the Signal API and MCP can also have these 8 fields. Some fields are empty for some signals.

Company

  • linkedin_urlValue in the API
  • industriesValue in the API
  • employee_count_lowValue in the API
  • employee_count_highValue in the API
  • revenueValue in the API
  • descriptionValue in the API

Signal

  • signal_nameValue in the API
  • associationValue in the API
Show the full JSONThe record on this page and the API request

GET /v1/signals/f138d448-4ec5-4022-ba60-4b69182fb4c0 returns this record as JSON. POST /v1/companies/enrich returns every signal for contentsquare.com.

{
  "signal_id": "f138d448-4ec5-4022-ba60-4b69182fb4c0",
  "signal_type": "linkedin-post-company",
  "signal_subtype": "linkedinPost",
  "detected_at": "2026-01-08T11:49:51.83+00:00",
  "company": {
    "name": "Contentsquare",
    "domain": "contentsquare.com"
  },
  "data": {
    "tags": [
      "Artificial Intelligence",
      "Analytics & Insights",
      "User Experience",
      "Pain Point"
    ],
    "post_url": "https://www.linkedin.com/feed/update/urn:li:activity:7414652136266813444/",
    "num_likes": 4,
    "post_text": "Your team didn’t spend last year short on data. They spent it short on answers.\n\n14 tabs open.\n3 reports pulled.\nAnd still the same question:\n“Where’s the drop happening?”\n\nThat’s the real problem. Not the numbers.\n\nThe chase.\n\nIn 2026, teams won’t spend hours tracking a drop.\n\nSense, Contentsquare’s AI layer, points straight to what’s going wrong, even if it’s one small form field causing users to drop off.\n\n→ Less hunting for problems\n\n→ More fixing them\n\nIt’s resolution time: How is your team planning to use AI to cut to the chase this year? 💬\n\n#NewYearsResolution #AI #Analytics",
    "posted_date": "2026-01-07T13:00:26.861Z"
  }
}

Long text fields are shortened on this page.

Looking up one signal by its id is free. Enrich costs 2 credits per signal returned; a call with no results is free.