Signal Based Marketing

Signal-Based Marketing

Overview

This skill covers how B2B marketers and sales teams can identify, score, and act on buying signals — from first-party behavioral data to third-party intent, social engagement, and product usage — to improve outbound conversion, ABM effectiveness, and paid media efficiency. All practices are sourced exclusively from Exit Five podcast guests across 13 contributing episodes. No general marketing knowledge has been added.

Building a Signal Framework

Define Your Signal Layers Before Selecting Tools

When helping a user build a signal-based marketing program, start by establishing what types of signals they should be tracking. Recommend the three-layer framework:

1. Business catalysts — leadership changes, strategy shifts, acquisitions, product launches
2. AI-driven intent signals — what accounts are searching for and on what themes
3. First-party behavioral signals — content consumption, event participation, engagement with the brand

When all three layers align for an account, treat that as a high-priority buying signal warranting immediate action. (Source: Lisa Cole, Episode #315)

Prioritize First-Party Signals Over Third-Party

When building account scoring models, weight first-party intent signals (pricing page visits, guided tour interactions, content downloads from your own site) more heavily than third-party intent data. First-party signals indicate direct engagement with your brand and are more reliable predictors of buying intent. Use third-party intent as a secondary layer to expand reach, but let first-party signals drive budget allocation and retargeting decisions. (Source: Richard Meyer, Episode #341)

Identify Industry-Specific Signals Beyond Standard Providers

Beyond Bombora and Sixth Sense, look for industry-specific signals that indicate buying intent: job board activity (new hires at target accounts), 10-K filings and earnings calls (for public companies), commercial real estate transactions, lease signings, or other vertical-specific events. Use tools like Clay to automate collection of this data. This gives you proprietary intent signals that competitors may not be using. (Source: John Short, Episode #201)

Select Signals Using a Volume and Conversion Rate Framework

When choosing which buying signals to prioritize, evaluate each signal on two dimensions:

1. Volume — how many instances of this signal do you see per month?
2. Conversion rate — what percentage of people showing this signal convert to the next stage (e.g., booking a demo)?

Stack-rank candidate signals by these two metrics. Start with the highest-potential signals and scale from there. This prevents wasting effort on low-volume or low-converting signals. (Source: Kevin White, Episodes #286 and #179)

Building Account Scoring Models

Derive Scoring Criteria from Historical Win Data

To set up signal-based outbound targeting, start with quantitative analysis of historical data. Look back at closed deals and identify the company-level and contact-level signals that were present when those companies became customers. Use this analysis to define ICP criteria and fit signals. Create a weighted scoring model based on these signals. Before deploying to your target account list, validate the scoring with sales leaders and your CEO to ensure it reflects current market conditions and any recent shifts in your ICP. (Source: Trinity Nguyen, Episode #306)

Establish a Tiered ICP Universe with Quarterly Account Selection

Establish a tiered ICP universe (e.g., 15,000 accounts), then score all accounts using buying signals (past champions, new hires, funding, hiring activity) and rank them in a CRM report by fit and signal strength. Select the top 500–600 accounts per quarter to focus outbound efforts. This creates a repeatable, data-driven mechanism for prioritizing which accounts to target and when, replacing ad-hoc prospecting. (Source: Trinity Nguyen, Episode #219)

Use Buying Group Activity to Route Accounts to Human vs. AI Outreach

When multiple people from the same account engage with your content (webinar, event, etc.), flag that account for human-led sales outreach rather than AI-first outreach. Buying group engagement indicates higher intent and warrants direct human conversation. Use this as a decision rule to allocate limited sales resources to the highest-probability accounts. (Source: Morgan Cole, Episode #315)

Executing Signal-Based Outbound

Start Manually Before Investing in a Platform

Do not recommend that users immediately purchase a software platform to execute signal-based marketing. Instruct them to start with a spreadsheet and manual process: identify signals relevant to their business, monitor them manually (e.g., by searching LinkedIn, reviewing website analytics), and run outbound plays against them. Once they validate that the approach works and generates pipeline, then invest in technology to aggregate and scale. This reduces upfront cost and lets them prove ROI before tooling investment. (Source: Kevin White, Episodes #286 and #179)

Prioritize Outbound Using Signal + ICP Fit Layering

Have SDR teams prioritize outbound based on two layers:

1. Buying signals — website visits, product signups, LinkedIn engagement, integration connections
2. ICP fit — is this an economic buyer, sales leader, etc.?

Combine these to create context for outbound plays. This approach delivers measurably higher conversion than pure ABM or cold outbound because SDRs are reaching out to people showing intent signals, not just accounts that fit the profile. (Source: Kevin White, Episodes #286 and #179)

Give Cold Calling Reps a Specific, Visible Reason to Call

Cold calling fails when reps lack context. Give reps a specific, visible reason why they're calling a prospect (e.g., "Your company has these signals, and I'm calling you specifically because you know John who knows us"). Display this reason in front of the rep during the call so they can reference it naturally. This transforms cold calling from a rejection-heavy activity into a contextual conversation, improving both answer rates and rep morale. (Source: Trinity Nguyen, Episode #306)

Personalize Outbound Messaging Around the Specific Signal

Combine signals into personalized email messaging that references the specific reason for outreach (e.g., "John, congrats on your new role. I noticed your company has X signal, and you know Sarah who talked to us about Y"). This approach yields measurable reply rates (1%+ is viable) and works because it gives reps a real reason to call and gives prospects a real reason to respond. (Source: Trinity Nguyen, Episode #306)

Use Engagement Signals to Trigger Next-Step Content in Sequences

Rather than sending the same cold outreach to all accounts, identify what content prospects have engaged with and follow up with next-step content based on their demonstrated interest. For example, if a prospect read a blog post on community as a marketing strategy, follow up with content on how to build a community. Use engagement signals to determine what to send next, increasing relevance and response rates. (Source: Mason Cosby, Episode #186)

Use AI to Automate Prospect Selection and Email Generation at Scale

Use AI to automatically identify prospects within target accounts based on buying signals (new hires, job changes, etc.) stored in your CRM, automatically add them to outreach sequences, and generate personalized outbound emails. This augments ADR capacity without proportional headcount increase. Implement when you have a forcing function (e.g., capacity loss due to team changes) to test and validate the approach before full rollout. Measure conversion and spend impact quarterly. (Source: Trinity Nguyen, Episode #219)

Build a Custom GPT Sales Coach for Signal-Based Messaging

Create a custom GPT trained on your company's positioning, messaging, and the signal types you track (business catalysts, intent, first-party behavior). Use it as a sales enablement tool to provide guidance on appropriate messaging, offer strategy, and outreach approach for accounts showing specific signal combinations. The GPT can recommend value-first offers (e.g., content library access) with no ask, helping sales know what to do when marketing surfaces qualified accounts. (Source: Lisa Cole, Episode #315)

LinkedIn and Social Engagement Signals

Monitor LinkedIn Post Engagement as a Buying Signal

Instruct users to monitor comments and reactions on relevant LinkedIn posts — from their own company, competitors, or influencers — to identify people in-market for their solution. Extract the list of commenters and reactors, filter for ICP fit, and run personalized outbound plays that reference the specific post and quote comments from other engaged users. (Source: Kevin White, Episodes #286 and #179)

Run Outbound to Influencer Post Commenters

After an influencer posts about your product, identify people commenting and asking questions, then reach out to them via outbound with a personalized message. This generates high reply rates (40%+ reported) because people are already engaged with the topic. Advanced tactic: reach out on behalf of the influencer (with their agreement) so people receive a message from their sales hero, creating a win-win where the influencer gets more engagement and you get qualified conversations. (Source: Domi de Saint-Exupéry, Episode #332)

Webinar and Event Signals

Track Multiple Signals Across the Webinar Attendee Journey

Use a combination of tools to track and score webinar attendees across multiple dimensions:

1. Person-level enrichment (e.g., Apollo, RocketReach) to understand who is attending
2. Ad exposure tracking (e.g., HockeyStack, Demandbase) to see if someone has been receiving your ads
3. CRM data (e.g., HubSpot) to track historical engagement — how long they've known about you, previous webinar attendance, website pages visited
4. Recent activity signals

Combine these signals to understand each attendee's position in their journey and tailor follow-up accordingly. (Source: Eoin Clancy, Episode #326)

Use Website Visitor Identification to Follow Up with Conference Attendees

When running ads to a conference attendee list, use a website visitor identification tool (e.g., Vector) to track which attendees visited your website. After the conference, send personalized follow-up emails to those who visited, referencing their site visit and offering to meet. This creates a warm follow-up based on demonstrated interest rather than cold outreach. Example result: 65% of conference attendees visited the site; those visitors received personalized emails offering coffee meetings. (Source: Tess Pfeifle, Episode #341)

ABM and High-Touch Tactics

Reserve Expensive ABM Tactics for Engaged Accounts Only

Do not recommend high-touch, expensive ABM tactics (fancy dinners, event suites, lunch-and-learns) for cold, unengaged accounts. Reserve these tactics for accounts that already have some relationship with you or that show behavioral engagement signals. Use lower-cost tactics (direct mail, landing pages, digital) for top-of-funnel awareness, then graduate to high-touch tactics once engagement is evident. (Source: Brian Kotlyar, Episode #331)

Paid Media and Intent-Driven Ad Targeting

Build an Intent Flywheel Across Multiple Ad Channels

Create a closed-loop system where first-party intent signals (website visits to pricing page, guided tours) trigger data enrichment (e.g., via Clay), which syncs back to your CRM and feeds into ad channels (Facebook, Google Display, LinkedIn) via an audience sync tool (e.g., Vector). As accounts show intent, increase ad budget allocation to those accounts, creating a compounding effect. Validate targeting accuracy on one channel (e.g., LinkedIn) before scaling to others. Use 13+ intent sources (first-party and third-party) to score accounts on ICP fit and intent. Reported results from this approach: 2x impressions, 75% CPM decrease, 3.5x average monthly pipeline, 5.2x pipeline from paid channels directly. (Source: Richard Meyer, Episode #341)

Segment Website Visitors by Behavior for Targeted Ad Campaigns

Use a website visitor identification tool (e.g., Vector) to identify and segment website visitors by behavior (e.g., webinar registrants who didn't convert, pricing page visitors, content downloaders). Create separate ad campaigns for each segment with tailored messaging. For example, if someone visited your webinar page but didn't register, create a segment and promote related content (e.g., a guide on the same topic) to that segment. This allows re-engagement of warm audiences with relevant next-step offers rather than generic retargeting. (Source: Cindy Dubon, Episode #341)

Integrate Outbound and Marketing Tools for Shared Visibility

Use shared tooling between marketing and outbound teams to create visibility across campaigns and intent signals. Implement tools that give both teams visibility into website visitor behavior and campaign performance, enabling more targeted and higher-converting outbound. Experiment with tools like Vector (for website visitor optimization) and Clay (for data enrichment and phone number accuracy). Phone calls remain highly effective when data hygiene is strong and phone numbers are accurate. (Source: Kelly Cheng, Episode #297)

Product Signals

Use Product Usage Signals to Drive Upsell and Expansion

Monitor product usage signals to identify upsell and expansion opportunities. Track moments when users are approaching feature limits (e.g., AI credit usage) or demonstrating readiness for team collaboration. Identify the "next best action" for each user segment based on their usage patterns — e.g., moving from free to paid, or from individual to team plans. This requires close collaboration between product and marketing to surface and act on these signals. (Source: Emma Robinson, Episode #277)

Use AI to Extract Buying Signals from Unstructured Sales Data

Leverage AI to analyze unstructured data (sales call transcripts, email threads, chat logs) to identify patterns that predict deal wins. Look for signals like multiple mentions of your brand across different sources (peer recommendations, analyst mentions, LLM citations), references to your company in the context of solving specific problems, and evidence of multiple buying committee members discussing your solution. This reveals buying signals that would be invisible in structured CRM data alone. (Source: Lisa Cole, Episode #315)

Where Experts Disagree

No disagreements were identified among the contributing guests on this topic.

What NOT To Do

• Don't use expensive ABM tactics (dinners, events, suites) for cold, unengaged accounts. These tactics only work if there's already some level of awareness or engagement. Using them on cold accounts wastes budget without generating pipeline. (Source: Brian Kotlyar, Episode #331)

• Don't invest in a signal aggregation platform before validating the approach manually. Start with a spreadsheet. Prove that acting on signals generates pipeline before spending on tooling. (Source: Kevin White, Episodes #286 and #179)

• Don't send the same cold outreach to all accounts. Generic outbound ignores the engagement signals that make outreach relevant and effective. (Source: Mason Cosby, Episode #186)

• Don't let cold calling reps call without a specific, visible reason tied to a signal. Reps without context face higher rejection rates and lower morale. Always equip them with a signal-based reason for the call. (Source: Trinity Nguyen, Episode #306)

• Don't rely solely on third-party intent data for budget allocation. Third-party intent is a secondary layer. First-party signals should drive the primary decisions. (Source: Richard Meyer, Episode #341)

• Don't chase signals that look interesting but have low volume or poor conversion rates. Always evaluate signals on both volume and conversion rate before committing resources to acting on them. (Source: Kevin White, Episodes #286 and #179)

• Don't skip validation of your scoring model with sales leadership before deployment. Scoring models built from historical data may not reflect current market conditions. Validate with sales leaders and your CEO before applying to your target account list. (Source: Trinity Nguyen, Episode #306)

Sources