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According to the Ehrenberg-Bass Institute, less than 5% of your ideal customer profile is actively looking to buy at any given moment. Yet, most teams waste resources targeting the full 100%.

You see this play out daily:   

• Blasting cold emails to companies that won't need their solution for years
• Running expensive ad campaigns to audiences with the right demographics, but zero buying intent
• Having SDRs burn through call lists of "perfect fit" accounts that couldn't care less about switching vendors right now
• Retargeting website visitors who were just doing competitive research for their current vendor

Traditional outreach treats every ICP-fit company equally. If a company has 500 employees, uses Salesforce, and sits in your target industry, they get the same treatment whether they're actively evaluating solutions or completely satisfied with their current setup.

Signal-based marketing and sales help you find and target that 5%. Companies ready to buy leave traces through actions like repeated website visits, buyer guide downloads, and competitor comparisons.

You can build playbooks around these behaviors to reach prospects when they're ready to buy. This guide shows you exactly how.

What is signal-based marketing?

The problem with traditional marketing is that it relies on static firmographic data that tells you who might buy, but NOT when they're ready. 

You run campaigns on predetermined schedules, score leads with arbitrary point systems, and reach the right person at the wrong time.

Signal-based marketing, on the other hand, uses real-time buyer and account intelligence to spot accounts ready to buy right now. 

You track behavioral data, trigger events, and relationship changes to prioritize outreach and automate engagement. Every ICP-fit company gets different treatment based on the specific signals they show.

So while traditional marketing targets your entire serviceable available market (SAM) equally, signal-based marketing zeros in on a much smaller, more valuable group:

This approach runs on three main components:

• Intelligence layer: Combines intent data from third-party platforms, job changes, funding rounds, hiring sprees, and engagement on your website. The more quality data sources you combine, the easier it is to find ready buyers.
• Orchestration layer: You set up workflows that launch when the right signals come together. When a champion joins a company researching your category, your system executes the champion playbook instantly. Every signal combination gets the right response at the right time.
• Execution layer: You coordinate outreach across email, ads, social, and direct mail based on the signals you see. Sales teams get AI-drafted emails with full context, marketing launches targeted campaigns, and every message references why you're reaching out now.

These components change how you execute go-to-market plays across every channel. You know who to target, when to reach out, and what to say. 

Here's what changes from the old way to the new way:

A former champion joins a new company (relationship signal) + that company just raised Series B funding (trigger event) + multiple people from that account are researching your category on G2 (intent signal) = your system automatically alerts the rep, drafts personalized outreach, and launches targeted ads to that account.

Why is signal-based marketing important in 2025?

Traditional spray-and-pray tactics stopped working when buyers started controlling the entire purchase process. Here's why signals matter more than ever:

• Buyers control the process: They complete 70% of the customer journey on their own before contacting sales. Signal-based marketing outlines them early so you can influence their criteria and get on their shortlist.
• Buyers face constant sales bombardment: The average American is exposed to around 4,000 to 10,000 ads each day, plus hundreds of sales emails, LinkedIn messages, and cold calls. Signal-based outreach works better because it's relevant to what they're actively researching right now.
• Traditional lead scoring fails to predict real buyers: Point-based systems built on email opens and content downloads miss the full picture. Signal-based marketing tracks actual customer behaviors across multiple sources for accurate predictions.
• Buying decisions involve more people than ever: According to Gartner, enterprise deals now involve 11+ stakeholders on average. Signals alert you when multiple people from the same account become active, which is a clear sign of a deal opportunity.
• The dark funnel keeps most buyer activity hidden: Prospects research anonymously through peer communities, review sites, and private conversations you can't track. Third-party intent signals help you see this invisible activity.
• Account data decays faster than ever: Contact information degrades at 2% monthly, meaning 20%+ of your database becomes unusable each year. Signal-based approaches use real-time data instead of stale lists, so you always reach the right people.

Signal-based marketing vs. signal-based selling

Signal-based marketing and signal-based selling work together as one system, not separate tactics.

Marketing casts a wide net to find and nurture interested accounts, then sales steps in with targeted, personalized outreach. Both teams use the same intent signals but apply them differently based on their role in the buyer journey.

These signals exist on a spectrum from low-intent to high-intent. Marketing typically handles the broader, early-stage signals while sales focuses on specific, high-intent actions.

Notice how conversion rates increase as you move right on the spectrum.

Marketing works the early signals through automated campaigns and targeted content. Then, sales takes over when individual contacts show customer behaviors like pricing page visits or demo requests.   

Here’s a quick table to make it easier to understand:

Example: Marketing notices employees from Pied Piper searching for "customer success platforms" across review sites. They launch targeted LinkedIn ads with case studies from similar companies and serve blog content about churn reduction.

Two weeks later, sales gets an alert that Pied Piper’s new VP of Customer Success previously championed your product at their last SaaS company. Sales sends a personalized email to congratulate them on the new role and reference their past success with your platform.

The VP responds immediately. They just started to evaluate vendors, and your name came up in their research. Meeting booked.

Related → How Many AI Sales Emails Can You Send Without Hurting Deliverability? 

Data sources for signal-based marketing

Not all signals are created equal. The best programs combine multiple data sources to build a complete picture of buyer intent. 

Here's where to find the signals that matter, from the data points you already own to the intelligence you can buy:

First-party data

First-party data comes directly from your own platforms and tools. It’s the behavioral signals prospects leave when they interact with your brand.

This data gives you the clearest view of buyer interest since you control it completely and know exactly how fresh and accurate it is.

• Website behavior: Track which pages prospects visit, how long they stay, and what they download. When someone hits your pricing page three times in a week or spends 10 minutes on your implementation guide, it’s a signal that they might be ready to buy.
• Email and content engagement: See who opens your emails, clicks specific links, and returns to read multiple pieces in your buying guide series. Someone who reads three case studies from their industry is telling you exactly what matters to them.
• Demo and webinar attendance: Track who registers, who shows up, and what questions they ask during sessions. Prospects that attend multiple webinars or stay for the entire demo Q&A are invested in learning about your product.
• CRM and sales history: Review past interactions, closed-lost reasons, and previous conversations to spot renewed interest. When a prospect who went dark six months ago suddenly re-engages, you know something changed on their end.
• Product usage data (for PLG/freemium): Monitor how free users engage with your product – which features they test, how often they log in, and when they hit usage limits. Users who invite teammates or repeatedly bump against paywalls are targets for sales outreach.
• Support and community activity: Watch who asks pre-purchase questions in your Slack community or submits support tickets about integration capabilities. These "how does this work" questions often point to active evaluation.

Second-party data

Second-party data is another company's first-party data that they share with you through a direct partnership or agreement.

It helps you spot buying signals happening outside your own properties but within your extended ecosystem.

• Integration partner signals: Your integration partners can share when mutual customers start exploring specific use cases or connectors. If someone starts testing your Salesforce integration heavily in their HubSpot instance, they might be considering a switch.
• Channel partner intelligence: Resellers and implementation partners see which accounts are asking about your solution during their own sales calls. They know when a client starts complaining about their current vendor or asking if there are better alternatives.
• Event and conference data: Event sponsors share attendee lists and session attendance data from their conferences. You can see which accounts sent multiple people to sessions about problems your product solves.
• Co-marketing campaign engagement: Partners who run joint webinars or content campaigns share engagement data about mutual prospects. When someone downloads three co-branded guides about the same topic, both companies benefit from that signal.
• Customer referral networks: Partners in formal referral programs share context about accounts they're sending your way. They tell you why the prospect is looking, what problems they face, and who the internal champion is.

Third-party data

Third-party data comes from specialized vendors who track and aggregate online behavior from millions of buyers across thousands of websites.

They sell you intelligence about which companies are researching your category, even when those companies have never heard of you.

• Intent data platforms: These services track which companies are surging on specific topics across vast publisher networks and content sites. When five people from the same company start reading articles about "customer churn solutions," you know they have an urgent problem.
• Review site activity: Software review platforms share data about which companies are comparing vendors in your category. Accounts that read multiple competitor reviews and check pricing pages are building a shortlist right now.
• Technographic data: Providers detect what technologies companies currently use through website tags, job postings, and public data sources. You can spot accounts using a competitor's product that's being sunset or companies running outdated versions ripe for replacement.
• Job posting intelligence: Recruitment data platforms track when accounts post roles that signal buying intent. If a company posts for three new administrators for a platform they don't currently use, they're probably making a switch.
• Funding and news triggers: Business intelligence platforms alert you to funding rounds, acquisitions, and leadership changes. Companies that just raised funding or hired new executives often need new tools to support their growth.
• Social media and community signals: Monitoring tools track discussions across professional networks and industry communities for buying signals. When someone asks for vendor recommendations in a large industry forum, multiple vendors can see that signal and respond.

Learn more → Types of Intent Data (+How To Use Them) in B2B Marketing 

Your blueprint for implementing a signal-based GTM strategy

You don't need to overhaul your entire tech stack or change everything at once to start with signal-based GTM. 

Here's exactly how to build your system step by step, from your first signal choices to a full revenue team rollout:

1. Start with a single, high-impact signal

Most teams fail at signal-based GTM because they try to track everything at once. 

They connect five intent tools, monitor ten different triggers, and end up with thousands of signals, but no clear plan for what to do with them.

That’s why you should pick your first signal based on three criteria:

• Volume: You need enough signals to learn what works, but not so many that you can't handle them. Aim for 20-50 signals per week to start.
• Intent level: Target signals that show prospects are deep in their research process. Reading customer reviews and checking integration documentation means they're past casual browsing.
• Actionability: You need a clear, specific action to take when the signal fires. Vague signals like "showed interest" don't tell reps what to do next.

For most B2B companies, tracking when past champions change jobs checks all three boxes perfectly.

The data tells you exactly who moved where, giving you an obvious reason to reach out. These leads convert at 3x the rate of cold outreach because you already have a relationship and a proven value story.

Your champions know your product works because they've seen the results firsthand. When they join a new company, they often inherit the same problems your product solved at their previous job.

They trust you, they know the implementation process, and they can sell internally without your help. The hardest part of the sale is already done.

Here's how to run your first champion job change play:

• Where to find the data: Set up job tracking through your sales intelligence platform or LinkedIn Sales Navigator. You can also use specialized tools that monitor job changes for specific contact lists.
• What your first message should say: Congratulate them on the new role, reference specific wins from their last company, and ask what initiatives they're tackling in the first 90 days. Just reconnect for now.
• When to reach out: Send your first message within 30-60 days of their start date. Too early, and they're still learning the business. Too late, and they've already picked vendors.
• Who else to loop in: After reconnecting with your champion, ask for intros to other stakeholders. Your champion becomes your internal guide to the new organization.

Once you've booked 10+ meetings from your champion signal and fine-tuned your playbook, you can bring your second signal.

Most teams wait 60-90 days before layering in website intent or competitive displacement plays. Build the muscle memory with one signal first, and then expand your system.

Learn more → How to respond to buying signals and turn them into sales 

2. Define the end-to-end workflow

The signal fires — your past champion just started a new job at a target account.

Most teams stop here, fire off a generic "congrats on the new role" message, and wonder why their signal-based plays don't convert. The teams that succeed have mapped out exactly what happens next, step by step.

Every signal-based workflow moves through four stages. Skip any stage and your process breaks down. You either miss signals entirely, waste time on poor-fit accounts, or send messages that don't resonate.

Build all four stages properly, and you create a repeatable system that converts signals into pipeline.

For example, let’s say your champion Mary leaves her director role and joins a Series C startup as VP of Operations. Your system detects the move, enriches it (the new company uses a competitor and just raised $50M), and routes it to her previous AE.

The AE sends a personalized email within 48 hours and references how you helped Mary reduce operational costs by 30% at her last company. Mary responds the same day, books a call, and closes in 45 days.

💡 PRO TIP: UserGems' Gem-E AI agent automates this entire workflow. When a champion changes jobs, Gem-E detects the signal, enriches it with company data and competitor usage, and writes personalized emails that mention your history with that champion. The messages appear directly in your sales engagement platform, ready to send with one click.

3. Align and enable your revenue team

Marketing says sales ignores their best leads, sales says the buyer intent signals are worthless, and opportunities slip away while both teams argue about who's at fault.

Success comes when both teams own the signal process together, with shared dashboards, joint playbooks, and mutual accountability for outcomes.

Here are some of the complaints you’ll hear most often and how to respond:

• "I don't have time to check another dashboard." → Build signals directly into their existing workflow. Send Slack alerts, create CRM tasks, or update opportunity records. Deliver the signals where they already work so they don’t have to hunt for them.
• "These signals aren't real buying intent." → Show them historical data. Pull closed-won deals from the last year and show how many exhibited these exact signals before buying. When reps see that 40% of last quarter's wins started with a champion job change, they pay attention.
• "My relationships are better than any signal." → Agree with them. Signals make relationships stronger, but they don't replace them. Show how signals give them reasons to reach out to dormant champions or perfect timing for their outreach.
• "This is just another thing that won't last." → You need to manage change fatigue head-on. Commit to a 90-day pilot with clear success metrics. Show them this isn't another random initiative but a proven system that can make their job easier.

Share every win publicly. When Kate books three meetings from champion job changes, post her exact message in Slack. Your team will ask for access when they see peers win with signals they don't have.

And once your pilot team proves the model, expand gradually. Bring five new reps monthly and let demand build naturally. By month six, signal-based plays become standard practice, not some new experiment they're trying out.

Learn more → Revenue alignment: How sales and marketing can drive growth together 

4. Measure, iterate, and scale

Your signal-based program moves through three phases in the first 90 days. Month one proves the concept, month two sharpens your approach, and month three gets you ready to scale.

And each phase has clear benchmarks that tell you whether to proceed or pivot.

Note: These benchmarks assume an enterprise B2B model with $50K+ ACVs. For smaller deal sizes, focus on the conversion rates and timeframes rather than absolute pipeline numbers.

Run weekly optimization sessions with your pilot team. You can pull the data every Friday to see which signals converted and which messages worked.

If champion job changes convert at 40% but competitor pain signals only hit 10%, double down on what works. Test different outreach timing and message angles.

Your reps know which signals feel warm versus which waste their time, so listen to their feedback alongside the numbers.

💡 PRO TIP: UserGems can push signal performance data directly into your CRM, so it’s easy to track which signals drive pipeline during your pilot. Since the platform already integrates with Salesforce and HubSpot, you can run your weekly optimization reports using familiar tools. 

Best practices for a successful signal-based marketing and sales

Most signal-based programs fail because teams overcomplicate them from the start. The best programs stay lean, move fast, and prioritize seller experience over perfect data.

These practices will keep your signal system running smoothly while you scale from pilot to full deployment:

Make the signal's context visible to the rep

Why it matters: A naked signal like "Company X visited your website" tells reps nothing useful about what to do next. But rich context about the visitor's role, their company's situation, and their specific interests helps them come up with an action plan.

First steps you can take:

• Include the complete signal history in every alert (what the prospect did, when they did it, and how it connects to previous interactions)
• Pull in relevant CRM data automatically so reps see past deal history, closed-lost reasons, and previous conversations without switching systems
• Insert competitive intelligence directly into the notification, including which competitor they use, contract end dates, and known pain points
• Outline mutual connections, past champions at the account, and any warm paths the rep could leverage
• Create one-click access to relevant sales assets, battle cards, and case studies based on the signal type and account profile

Example: Your rep doesn't just see "pricing page visit from Pied Piper." They see that the visitor is a new VP Sales, the company just raised Series B, they're using a competitor with an upcoming renewal, and there's a champion inside who can help. Every signal comes with a story behind it.

Orchestrate signals, don't just collect them

Why it matters: Your reps can't process 500 signals a week manually, and they shouldn't have to. When you orchestrate signals into automated plays with clear next steps, reps can use them and book more meetings.

First steps you can take:

• Map each signal type to a specific action with an "if this, then that" playbook that your team can follow without too much thinking
• Set up automatic routing rules so champion job changes go straight to the previous account owner, while competitor pain signals route to your competitive displacement specialist
• Build enrichment into your workflow to automatically pull company size, tech stack, and recent news before any rep sees the signal
• Create signal combinations that kick off only when multiple triggers sync, like when a past champion joins a company that's also showing intent on review sites
• Define SLAs for each signal type so everyone knows champion changes get touched in 24 hours, while funding rounds can wait 72 hours
• Connect your signals directly to sales engagement tools so reps get pre-drafted emails with context instead of raw data dumps

Example: When Pied Piper triggers three signals simultaneously (new VP from a customer account, and pricing page visit), your system creates a priority task for the enterprise rep, writes a personalized email mentioning the mutual connection, and launches a targeted LinkedIn campaign to the buying committee. The rep just has to review and hit send.

💡 PRO TIP: UserGems lets you set rules for signal combinations. You can specify that when three signals hit the same account (champion change + intent data + funding), it triggers a different workflow than single signals. Your reps see only the highest-priority opportunities that need immediate action.

Build signal fatigue prevention into your cadences

Why it matters: Multiple signals for the same prospect usually mean multiple separate emails that annoy the recipient. Prevention rules help you send one well-timed, right message that covers all the signals instead of bombarding them.

First steps you can take:

• Set up signal suppression rules so prospects can't receive outreach from more than one signal type within a 14-day period
• Create combination plays that combine related signals (funding + job change + intent) into single, comprehensive messages
• Build cooling-off periods where prospects get removed from all signal-based outreach for 30 days after any meaningful engagement
• Track signal frequency at the account level and flag companies that trigger more than 3 signals per month for manual review
• Use signal priority rankings so high-intent actions (demo requests) override lower-intent signals (website visits) in your cadences

Example: David from Notion triggers both a champion job change and competitor research signals in the same week. Your system waits 3 days, then sends one email congratulating him on the new role, while mentioning you noticed his team is evaluating alternatives.

Set up signal decay rules (when to stop acting on old signals)

Why it matters: Acting on old signals makes you seem out of touch and hurts your credibility with prospects. Decay rules prevent embarrassing situations where you congratulate someone on a job change from four months ago.

First steps you can take:

• Set champion job changes to expire after 90 days, since people settle into new roles and build vendor preferences quickly
• Remove website intent signals after 30 days because browsing behavior from last month doesn't point to current buying interest
• Keep funding announcements active for 6 months since companies typically start major purchases 3-4 months after raising capital
• Expire competitive research signals after 45 days because evaluation cycles move fast, and old research loses its urgency
• Build manual override options so reps can extend or refresh high-value signals that deserve longer attention

Example: Mike from Shopify triggered a competitor research signal in Q1, but your decay rules archived it after 45 days. When his team starts evaluating vendors again in Q3, your rep sees only the new signals and can focus on his current buying process.

Turn buying signals into predictable pipeline

Your reps get bombarded with signals all day long. CRM alerts, intent data notifications, job change updates - it never stops. The problem is connecting these alerts to action before the opportunity goes cold.

UserGems handles this with an AI outbound platform built specifically for signal-based GTM. 

While other tools just dump signals in your lap, UserGems' AI agent Gem-E finds your best prospects, ranks them by conversion probability, and writes personalized outreach.

Here are just some of the key features that UserGems brings to your team:

• Gem-E AI Agent handles end-to-end campaign execution: Gem-E automatically builds target account lists, outlines multiple signal types, and writes personalized email sequences and call scripts. Your reps wake up to pre-written outreach that mentions specific signals like job changes or competitor research without any manual research.
• Pre-built signal campaigns activate in minutes, not months: Choose from proven playbooks for champion job changes, new hire outreach, and intent-based targeting. Each campaign includes automated workflows, messaging templates, and scoring logic that you can customize for your ICP.
• Proprietary data quality delivers 95% match accuracy: UserGems maintains its own verification processes and training models without relying on third-party data providers. You get sub-5% email bounce rates and accurate contact information that keeps your sender reputation intact.
• Native CRM integration keeps your existing workflow intact: Signals, tasks, and AI-generated messages appear directly in Salesforce, HubSpot, Outreach, and Salesloft. Reps don't need to learn new tools or switch between platforms to access signal intelligence and execute campaigns.
• Multi-signal scoring prioritizes your highest-converting prospects: The platform combines champion relationships, website intent, funding events, hiring patterns, and competitive research into unified account scores. Your team focuses on prospects with multiple buying indicators and skips the single-signal noise.

Take the experience of Millennium Alliance as proof of concept. They knew that past customers as their highest-converting prospects, but couldn't scale tracking job changes manually.

After implementing UserGems, they closed five deals worth $360,000 directly from champion job changes. Their email bounce rates dropped below 1% while pipeline generation became completely automated.

UserGems backs this with a revenue guarantee. If you don't generate pipeline worth your investment, you get your money back. No complicated terms or hoops to jump through.

Book a demo today and see how UserGems automates everything from prospect identification to personalized outreach.

Frequently asked questions (FAQs)

What are the prerequisites for implementing a signal-based strategy?

You need clean CRM data and clear definitions of your ideal customer profile and target personas. Most teams can start with basic champion tracking if they have customer contact lists in their CRM.

Does this level of automation replace skilled sales reps?

No, platforms like UserGems make skilled reps more effective because they handle the research and data work that kills their selling time. 

Reps still build relationships, handle objections, and close deals – they just get better prospects and more context.

How do you attribute revenue to a specific signal?

Track which signal first triggered the prospect's entry into your CRM and follow that contact through your sales cycle to closed-won.

Most CRM systems can tag the original source and maintain that attribution even when multiple signals fire for the same prospect.

Which type of signal typically has the highest ROI?

Past champion job changes typically generate the best returns because the relationship and product knowledge already exist. The relationship and trust already exist, so sales cycles move much faster.