Filtering by Signal Type (Pricing Page Visits, Likes etc.)
Stairoids collects dozens of real-time buying signals from across your ecosystem — but the real value comes from filtering these signals to find the ones that matter most to you. Whether you're looking for companies who visited your pricing page, liked your content, or showed signs of job change, Signal Type filters help you focus fast.
This article shows how to filter by specific signal types and use them to create sales and marketing workflows that convert.
🎯 What Are Signal Types?
Signal types are individual data points that indicate a specific kind of behavior or event — like:
| Signal Type | What It Tells You |
|---|---|
| Pricing page visit | Buyer is actively researching cost and value |
| Post like/comment | Interest in your content or positioning |
| LinkedIn profile view | Someone is evaluating you or your team personally |
| Job change detected | New stakeholder or potential trigger for timing |
| Website page depth | Engagement beyond awareness — deeper interest |
| Ad engagement | Active response to a paid campaign |
Each of these contributes to the Intentional Wire Score (IWS) and can be filtered independently.
🛠️ How to Filter by Signal Type
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Go to the Filters section in Stairoids
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Click Create New Filter
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Choose Signal Type as your criteria
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Select from available types (e.g. “Pricing page visit”, “Liked LinkedIn post”, “Job change”)
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Add optional timeframes, roles, company data, or IWS scores
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Save your filter and name it clearly (e.g. “Pricing Page Visitors – Last 30 Days”)
💡 You can combine signal types to create even stronger indicators of intent.
🔍 Example Filters to Try
| Filter Name | Signal Criteria |
|---|---|
| Pricing Intent – 30 Days | Pricing page visit + IWS > 80 |
| Warm LinkedIn Leads | Viewed profile + liked post |
| New Decision Makers | Job change detected + assigned contact |
| Post-Event Retargeting | Webinar tag + website visit |
| Multi-Channel Engagement | Ad click + profile view + website visit (7 days) |
📤 What to Do With Signal-Based Filters
Once you have a filter built around a specific signal type:
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Export to LinkedIn Ads for retargeting
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Assign to sales or SDRs for follow-up
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Push to marketing workflows in tools like ActiveCampaign or HubSpot
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Bulk approve or heart companies that fit key intent profiles
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Monitor for conversion as part of Marketing Touched Pipeline tracking
✅ Best Practices
| Practice | Why It Helps |
|---|---|
| Use specific timeframes | Focus on fresh intent (e.g. last 14 or 30 days) |
| Combine 2–3 signal types | Stronger signal = better follow-up results |
| Heart contacts within strong signals | Helps the system learn faster who matters |
| Tag filters by campaign or initiative | Makes them easier to report on and re-use |
🟢 Summary
| You Want To... | Use These Signal Type Filters... |
|---|---|
| Prioritize hot accounts | Pricing visits, profile views, ad clicks |
| Personalize outreach | Post engagement, job changes |
| Build LinkedIn or email audiences | Multi-touch engagement filters |
| Track campaign impact | Specific campaign signal tags (e.g. “Webinar Q2 Visitors”) |
Signal-based filters help you stop guessing and start acting — based on real, relevant behavior.