The “Problem-Aware” Social Scan

What It Is

The Problem-Aware Social Scan monitors social media and professional communities for posts that explicitly express pain points your product solves. Unlike brand monitoring (mentions of your company) or competitor monitoring (mentions of competitors), this playbook focuses on problem-aware intent—people asking questions like 'Why is forecasting so hard?' or 'Anyone have a solution for X?' These are golden opportunities for helpful, non-salesy engagement.

Why It Matters

When someone publicly asks for help with a problem you solve, they've done your prospecting for you. They've self-identified as having the pain, being in buying mode, and being open to solutions. The key is reaching them with genuine value—not a pitch—while the question is still fresh. Most companies miss these signals entirely or respond too late. This playbook systematizes the capture and response.

Who It's For

• Companies solving problems that people actively discuss online
• SDR teams with social selling components to their workflow
• Marketers building thought leadership in professional communities
• Brands active in Slack communities, Discord, Reddit, or LinkedIn

Required Tools

• PhantomBuster or similar (for social scraping)
• LinkedIn Sales Navigator (for LinkedIn monitoring)
• Slack API access (for community monitoring, if applicable)
• GPT-4 (for response drafting)
• Clay (for enrichment and workflow)

Required Fields/Properties

• Pain Point Keyword Library (phrases that indicate your problem space)
• Target Persona Titles (to filter for relevant posters)
• Platform Priority List (LinkedIn, Twitter, Slack communities, etc.)
• Response guidelines by platform

Definitions Required

• Which pain points are strong vs. weak buying signals
• Engagement rules: comment vs. DM vs. connection request
• Response tone: helpful expert vs. friendly peer
• Handoff criteria: when does marketing engagement become sales lead

LinkedIn Comment: Forecasting Pain

Great question—forecasting accuracy is one of those problems that seems simple but gets complicated fast.

One thing that helped teams I've worked with: separating "pipeline coverage" from "weighted forecast." They measure different things but often get conflated.

If you're open to it, I can share a one-pager on the framework. No strings attached—just dealt with this a lot and happy to help.

LinkedIn Comment: Tool Recommendation Ask

Saw your question about [tool category]. A few things to consider before picking:

1. Integration depth with your existing stack (CRM, etc.)
2. Whether you need flexibility or opinionated workflows
3. Implementation support—some tools are DIY, some aren't

Happy to share what I've seen work for teams at similar stage if helpful. What's the biggest pain point driving the search?

DM Follow-up Template

Hi {{first_name}},

I commented on your post about {{pain_point}} yesterday—hope the suggestion was helpful.

I work with {{persona_type}} teams on exactly this kind of challenge. Not trying to sell you anything, but I put together a quick resource on {{topic}} that might be relevant based on what you described.

Want me to send it over?

{{sender_name}}

Pain Point Keyword Categories

| Pain Category | Example Keywords | Signal Strength | Response Type |
|---------------|------------------|-----------------|---------------|
| Explicit Need | "looking for a tool", "need a solution" | Very High | Comment + DM |
| Frustration | "so tired of", "why is X so hard" | High | Comment first |
| Question | "how do you handle", "best practice for" | Medium-High | Helpful comment |
| Discussion | "what do you think about", "debate: X vs Y" | Medium | Add perspective |
| Mention | mentions problem keyword in passing | Low | Monitor only |

Slack Alert Format

💬 *Problem-Aware Signal Detected*

*Platform:* {{platform}}
*Posted:* {{time_ago}}

*Poster:*
• {{poster_name}} ({{title}} at {{company}})
• {{company_size}} employees | {{industry}}
• LinkedIn: {{linkedin_url}}

*Original Post:*
> "{{post_excerpt}}"

*Pain Category:* {{pain_category}}
*Signal Strength:* {{signal_strength}}

*Suggested Response:*
{{ai_generated_response}}

<{{post_url}}|View Post> | <{{crm_link}}|View in CRM>

⏰ *SLA:* Respond within 24 hours

Test Cases Checklist

• Post containing pain keyword detected → pushed to Slack with full context
• Poster enriched with LinkedIn profile and company info
• Non-ICP poster (wrong company size/title) → filtered out
• AI-generated response is contextual and non-salesy
• CRM lead created with post URL and pain category logged
• Duplicate posts from same person → deduplicated
• Old posts (>7 days) → excluded from alerts

Common Failure Modes

• Generic responses: If your response could apply to any post, it's too generic. AI responses should reference specific language from their post.
• Pitching in public: Never mention your product in a public comment on their problem post. It looks desperate and damages trust.
• Delayed response: Responding to a post from 2 weeks ago is weird. Set freshness limits and prioritize recency.
• Keyword false positives: Keywords like 'pipeline' catch both sales ops and DevOps posts. Add persona filters to reduce noise.

Troubleshooting Tips

• If too few signals: Expand keyword library, add more platforms, loosen persona filters
• If too much noise: Tighten persona filters, require multiple keywords, exclude common false positives
• If low engagement: Review response quality—are they genuinely helpful? Test different tones
• If no DM replies: Wait longer before DM, ensure your profile looks credible, soften the ask

KPIs to Track

• Signals Detected: 20-50 qualified signals per week across platforms
• Response Rate: >90% of signals receive response within SLA
• Engagement Rate: >25% of responses get likes/replies/follows
• DM Reply Rate: >30% of follow-up DMs receive reply
• Meeting Book Rate: >10% of engaged leads book meetings

Suggested Dashboard Widgets

• Signals by Pain Category: Bar chart showing volume of detected signals by problem type
• Engagement Funnel: Funnel: Signals → Responded → Engaged → DM'd → Meeting
• Platform Performance: Table comparing engagement rates across LinkedIn, Twitter, Slack
• Top Performing Keywords: List of keywords with highest engagement-to-meeting conversion