How to Use Valley for Post Engagement Campaigns
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Make LinkedIn your Greatest Revenue Channel ↓
Saniya Sood
How Does Valley Turn LinkedIn Post Engagement Into Pipeline?
Your LinkedIn content generates likes, comments, and shares but what happens after someone engages? Most sales teams let that engagement evaporate instead of converting it into conversations. Valley transforms post engagement from vanity metrics into qualified pipeline through systematic identification, qualification, and outreach.
When someone likes, comments on, or shares your LinkedIn post, they signal interest in that topic. This engagement represents far more valuable intent than random cold prospects: they've chosen to associate their professional brand with your content, they've invested time reading and considering your perspective, they're likely facing challenges related to the post topic, and they've given you permission to start a conversation.
Valley captures every post engagement automatically, enriches and qualifies engagers against your ICP, researches why the topic resonates with their role and situation, generates personalized outreach referencing their engagement, and converts attention into meetings. Your content becomes a pipeline engine instead of just a brand-building exercise.
The compound advantage: as you publish more content and grow your LinkedIn presence, Valley automatically scales engagement capture and conversion. Your following directly translates to qualified pipeline without additional manual effort.
► Book a demo and explore how Valley can support your use case
What Types of LinkedIn Post Engagement Does Valley Track?
LinkedIn provides several engagement types, each indicating different levels of interest. Valley captures all engagement forms and prioritizes them appropriately based on signal strength.
Likes/Reactions (Baseline Interest): The lowest-effort engagement—one click to react. Likes indicate passive interest: the person saw your post, found it somewhat relevant, but didn't invest time commenting. Valley captures likes but scores them lower than comments or shares.
Use case: Nurture sequences for prospects who show repeated like activity across multiple posts, demonstrating sustained interest even without active commenting.
Comments (Active Interest): Comments require thought and effort—the prospect invested time crafting a response, put their perspective on public record, and likely read your post carefully. Valley prioritizes commenters highly because they're actively thinking about the topic.
Valley's AI analyzes comment content to understand perspective: Did they agree or disagree? Did they share related challenges? Did they ask questions? This analysis informs message personalization: "Your comment about struggling with cold email deliverability resonated—seems like a challenge you're actively facing."
Shares/Reposts (Endorsement Interest): Sharing your content to their network represents the highest engagement: they found your post valuable enough to amplify it, they're willing to associate their brand with your perspective, and they likely want their network to see this content.
Valley treats shares as premium signals, often generating immediate high-priority outreach. Someone sharing your post about "Why Cold Email Is Dead" to their network of sales leaders is almost certainly evaluating alternatives to cold email.
Multiple Engagement Types: When prospects engage multiple ways—liking, commenting, AND sharing—they signal exceptionally high interest. Valley automatically prioritizes multi-engagement prospects, recognizing their elevated intent.
Engagement on Multiple Posts: Sustained engagement across several posts over time indicates genuine interest in your content and perspective, not just one-off topic curiosity. Valley tracks engagement history and adjusts priority: someone who engaged with five posts this month gets higher priority than someone who liked one post once.
By capturing and differentiating these engagement types, Valley ensures your team focuses outreach on the signals with highest conversion potential.
How to Set Up Valley's Post Engagement Scraping Campaigns?
Configuring post engagement campaigns requires thoughtful setup to ensure you're capturing relevant engagers and converting them effectively.
Step 1: Identify Your Best-Performing LinkedIn Posts Review your recent LinkedIn posts (past 30-60 days) and identify those with highest engagement from your target audience. Look for posts with 50+ engagements from people in your ICP, topics clearly related to pain points your solution addresses, comment threads indicating active interest and discussion, and shares from industry influencers or target personas.
These high-engagement posts provide the best prospects for conversion campaigns.
Step 2: Add Post URLs to Valley Valley's campaign creation includes a "LinkedIn Posts URL" option. Copy the URLs of your best-performing posts and paste them into Valley. The platform automatically scrapes all engagement (likes, comments, shares) from those posts, typically capturing hundreds of prospects per high-engagement post.
Step 3: Configure Engagement Filters Not all engagers deserve outreach. Set filters to focus on high-quality prospects: ICP fit requirements (company size, industry, role), engagement type priority (comments > likes, shares highest priority), minimum account age (exclude brand new accounts likely to be spam), and geographic filters if you have regional focus.
Valley automatically excludes competitors, existing customers, and public profiles based on your DNC lists and settings.
Step 4: Enable Automatic vs Manual Review For high-performing posts with strong ICP alignment, enable automatic campaign creation: Valley captures engagers and launches outreach without manual review. For broader posts with mixed audiences, use manual review mode: Valley captures engagers, you review the list, and approve which prospects should receive outreach.
Step 5: Create Post-Specific Messaging Your outreach should reference the specific post and topic: "I saw your comment on my post about warm vs. cold outbound—your point about deliverability challenges really resonated. Have you explored LinkedIn-first approaches?"
Valley's AI generates these contextual references automatically, but you can provide topic-specific messaging guidelines to ensure proper positioning.
Step 6: Monitor Engagement Continuously Valley can monitor posts automatically going forward, capturing new engagement as it occurs. Enable continuous monitoring for posts that accumulate engagement over time, or use one-time scraping for viral posts that generate immediate bursts of engagement.
Proper setup transforms your content marketing from brand exercise into systematic lead generation.
How Does Valley Prioritize Post Engagers for Outreach?
A viral post might generate 500+ engagements—you can't personally outreach to everyone. Valley's prioritization engine surfaces the prospects most likely to convert based on engagement quality and ICP fit.
Signal Strength Scoring: Comments with thoughtful perspective score highest (8-10/10) Shares to their network score very high (7-9/10) Multiple engagements on one post score high (6-8/10) Simple likes without other engagement score lowest (3-5/10) Multiple posts engaged over time scores highest (9-10/10)
ICP Fit Scoring: Perfect ICP match: right role, company size, industry, geography (9-10/10) Strong ICP match: most criteria met with minor deviations (7-8/10) Partial ICP match: some criteria met but others missing (4-6/10) Poor ICP fit: minimal criteria met (1-3/10)
Combined Priority Ranking: Valley multiplies signal strength × ICP fit to generate final priority scores. A prospect with perfect ICP fit (10/10) who commented thoughtfully (9/10) receives priority score of 90—top tier for immediate outreach.
A prospect with partial ICP fit (5/10) who liked one post (3/10) receives priority score of 15—lower priority for nurture sequences rather than immediate sales outreach.
Automatic Tier Assignment: Based on priority scores, Valley automatically assigns engagers to tiers: Tier 1 (score 70+): Immediate personalized outreach from sales rep, Tier 2 (score 40-69): Automated warm outreach campaign, Tier 3 (score 20-39): Nurture email sequence, Below 20: Excluded from outreach, added to content audience.
This tiered approach ensures your team focuses manual effort on highest-potential prospects while Valley handles automation for good-fit but lower-priority engagers.
Dynamic Re-Prioritization: As prospects engage with additional content, Valley adjusts their scores automatically. Someone who initially scored 45 (Tier 2 automated outreach) but then commented on two more posts might jump to 75 (Tier 1 manual outreach).
This dynamic scoring prevents prospects from being permanently relegated to low-priority based on single interactions.
► Check Out More of Valley's Incredible Outreach: A compilation of real time messages and responses!
How to Measure Content-to-Pipeline Effectiveness With Valley?
Understanding which content drives pipeline helps you create more of what converts. Valley's analytics connect post engagement directly to business outcomes.
Post-Level Attribution: Valley tracks pipeline metrics per LinkedIn post: total engagements captured, ICP-fit engagers identified, outreach acceptance rate, response rate, meetings booked, and pipeline generated.
This allows apples-to-apples comparison: Post A about "Why LinkedIn Beats Email" generated 300 engagements, 45 ICP-fit prospects, 12 responses, and 4 meetings. Post B about "How to Scale SDRs" generated 150 engagements, 60 ICP-fit prospects, 18 responses, and 7 meetings.
Post B delivers better pipeline despite lower total engagement because it attracts more relevant prospects. This insight guides content strategy.
Topic Performance Analysis: Aggregate performance across posts by topic category: warm outbound posts drive X meetings, LinkedIn automation posts drive Y meetings, sales productivity posts drive Z meetings.
Identify which content themes resonate most with prospects who actually convert, allowing you to double down on high-converting topics.
Engagement Type Conversion: Track conversion rates by engagement type: commenters convert to meetings at X%, sharers convert at Y%, likers convert at Z%.
This data validates that higher-effort engagement indicates higher conversion potential, informing how Valley scores and prioritizes different engagement types.
Content Velocity Impact: Measure how posting frequency impacts pipeline: months with 8+ posts generate X pipeline, months with 4-7 posts generate Y pipeline, months with < 4 posts generate Z pipeline.
Understand the minimum content velocity required to sustain your warm outbound motion and hit pipeline targets.
Time-to-Conversion Tracking: Measure how quickly post engagers convert to meetings: average days from engagement to first message, average days from first message to response, average days from response to meeting booked.
Understanding these timelines helps set realistic expectations and optimize follow-up cadences.
By connecting content directly to pipeline outcomes, Valley transforms your LinkedIn presence from brand-building nice-to-have into quantifiable revenue driver.
What Content Strategy Works Best for Valley's Post Engagement Campaigns?
Not all LinkedIn content drives equal engagement from your ICP. Valley's effectiveness depends on publishing content that attracts and engages prospects likely to convert.
Problem-Aware Content Over Educational: Posts addressing specific problems your ICP faces (pipeline generation challenges, SDR scaling difficulties, outbound inefficiency) attract prospects actively experiencing those problems. Educational content about tangential topics generates engagement from broader audiences less likely to convert.
Example high-converting post: "Cold email is dying. Our customers replaced it with warm LinkedIn outbound and saw 5x better response rates." This attracts prospects frustrated with cold email your perfect target.
Example low-converting post: "10 tips for better LinkedIn profiles." This attracts anyone wanting LinkedIn advice mostly job seekers and personal brand builders, not B2B buyers.
Contrarian Takes and Strong Perspectives: Vanilla content generates vanilla engagement. Posts with clear, contrarian perspectives spark discussion and comments from people who either strongly agree or disagree both groups make great prospects.
"Hot take: If you're still hiring SDRs to do manual LinkedIn outreach in 2025, you're wasting $100K+ annually" generates strong reactions and thoughtful comments from sales leaders evaluating their approach.
Data-Driven Claims: Posts citing specific metrics and results generate engagement from data-minded buyers often the most qualified prospects. "We analyzed 10,000 LinkedIn messages. Warm outbound gets 6-10% response rates vs 1-2% for cold email" attracts analytical buyers who make decisions based on evidence.
Question-Based Posts: Posts asking genuine questions spark comment engagement as people share their perspectives. "What's your biggest challenge with LinkedIn outbound?" generates dozens of comments revealing specific pain points you can address in personalized outreach.
Case Study and Results Posts: Sharing customer results (with permission) demonstrates value and attracts prospects seeking similar outcomes. "How [Customer] booked 127 meetings in 6 weeks using warm LinkedIn outbound" attracts prospects wanting to replicate those results.
Posting Frequency: Valley's data shows optimal posting frequency is 4-8 posts monthly (1-2 per week). More than 2 posts weekly risks audience fatigue. Fewer than 4 posts monthly provides insufficient engagement volume for meaningful pipeline.
Consistent publishing on this cadence generates steady engagement flow that Valley converts into continuous pipeline, rather than feast-or-famine based on one-off viral posts.
► Here's the Valley Warm Outbound Launch Video which we spent way too much money on- check it out!
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