Intent-based Targeting Not Working? When Intent Signals are Misleading and How to Better Interpret Them
Introduction: The Hidden Risks of Misreading Intent Signals Intent signals are one of the most powerful tools in modern B2B marketing. They allow teams to prioritize accounts based on behavior instead of guesswork, helping marketers deliver the right message at the right time. But despite their value, intent signals are not foolproof. They can be noisy, misleading, and even counterproductive when interpreted in isolation. Why intent is powerful—but not foolproof Intent-based targeting gives marketers behavioral context beyond traditional demographics or firmographics. But relying on any single action—whether a content download or a pricing page visit—sets teams up for false assumptions. Intent is directional, not definitive. The danger of chasing “false positives” in B2B A spike in engagement may look like buying intent when in reality it comes from job seekers, students, bots, or competitors. Misreading this data can waste sales resources, inflate pipeline expectations, and strain SDR bandwidth. Why marketers must blend data with context and judgment Intent signals work best when interpreted through a combination of: Behavioral patterns ICP fit Buying stage Frequency and recency Human judgment Without these layers, marketers end up chasing noise instead of true opportunities. The Difference Between High-Quality and Low-Quality Intent Signals Not all signals have equal weight. Understanding the differences helps marketers score leads accurately and prioritize high-value accounts. Signal depth: how strong or weak the action is Examples of strong signals: Pricing page visits Competitor comparison views Demo interactions Examples of weak signals: Blog post views Email opens One-time website visits Depth matters because some behaviors clearly indicate evaluation, while others merely reflect curiosity. Signal frequency: how often the activity occurs Multiple return visits, repeat searches, or sequential content engagement are far more telling than one-off interactions. Frequency suggests: Growing interest Internal discussions Momentum in the buying journey Signal recency: how fresh the behavior is A prospect who binged your content six months ago but has been inactive since does not hold the same value as an account showing activity in the last three days. Signal relevance: whether it aligns with your ICP and solution You can’t call it meaningful intent if: The account is outside your vertical The persona is irrelevant The behavior does not relate to your core use case Relevance ensures you don’t misinterpret activity that has nothing to do with actual buying readiness. Common Situations Where Intent Signals Mislead Marketers Even the most sophisticated systems can produce false positives. Here are the most common cases. 1. High content engagement that has nothing to do with buying Students, researchers, and job seekers driving up views Educational audiences often download content for learning purposes—not purchasing. Educational interest vs commercial interest Just because someone reads your whitepaper doesn’t mean their company is evaluating vendors. 2. Pricing page visits from existing customers or competitors Renewal research, benchmarking, or competitive intelligence Customers might be checking upgrades, while competitors often analyze pricing models. This can look like buying intent but has zero pipeline value. 3. Webinar registrations that never get attended Low commitment vs true evaluation intent Registrations reflect curiosity, not intent. Attendance with active participation is what matters. 4. Website traffic spikes from bots or low-quality referral sources How to filter spam or irrelevant traffic Traffic from suspicious geographic regions, unknown referral sites, or automated browsing patterns should be excluded from scoring. 5. Funding announcements that don’t relate to your solution Why new capital doesn’t always mean new buying needs Funding is a directional signal. But unless your solution aligns with their new initiatives, it means nothing for your pipeline. 6. Job postings that don’t imply actual buying readiness Hiring ≠ purchasing; understanding the nuance A company hiring a scientist doesn’t necessarily need new lab tools. Context matters. How to Properly Interpret Intent Signals Without Making Wrong Assumptions Avoiding misinterpretation requires structured analysis rather than reacting to isolated behaviors. Looking for patterns, not isolated signals A single action means little. A sequence is meaningful. Layering multiple signals for stronger predictions For example: Pricing page visit Multiple return visits Case study downloads LinkedIn engagements This stack is far more reliable than any one action alone. Using behavioral sequences to understand true intent A prospect who moves from educational content → product pages → demo tour is clearly progressing in the buying journey. Aligning signals with buying stage (ToFu, MoFu, BoFu) Mapping engagement to funnel stages prevents premature outreach. The Role of ICP Alignment in Avoiding False Positives Intent only matters when it comes from the right type of account. Why intent without the right ICP = wasted effort A non-ICP company reading your content is not a real opportunity. Ensuring industry, role, and workflow fit Lead scoring should penalize engagement from: Non-target industries Junior roles Unrelated domains Avoiding the trap of “chasing every signal” More signals do not equal better signals. Quality beats quantity every time. Timing Sensitivity: When Intent Signals Look Real but Are Too Early (or Too Late) Timing can distort your perception of intent. Early-stage research disguised as buying behavior Some prospects dive deep into content long before they have budget, authority, or urgency. Late-stage evaluations where you’re already out of the running If the account has already done vendor comparisons, you may be too late. When to follow up, slow down, or disengage Signals should dictate pacing: Early stage → nurture Mid stage → educate Late stage → engage immediately How to Fix Misleading Intent Data in Your Lead Scoring Model Improving your model reduces noise and strengthens pipeline quality. Adjusting weights for signal strength and relevance Pricing page visit > blog view Case study download > social like Score accordingly. Adding negative signals (drop-offs, time gaps, bounces) Intent decays. Your model should reflect that. Setting thresholds before passing leads to sales Do not send a lead to sales unless it crosses a multi-signal threshold. Best Practices for Validating Intent Before Outreach Before contacting a prospect, validate the signal using simple checks. Confirming behavior with soft-touch emails or LinkedIn interactions Examples: “Saw you were exploring X topic. Happy to
