Key Takeaways
- → An ICP can include accounts at different stages. A single signal may be incomplete or unrelated; verify it in context.
- → Intent activity is an inference, not proof that an account or person is ready to buy. Verify signals and account fit before outreach.
- → No fixed number of signals confirms buying intent. Review each source, date, relevance, and limitation.
- → For 1 Solar Direct, a project-data signal layer drove a 6.35% reply rate and 8 meetings with big solar project operators.
Intent data should work in theory. If a company is actively researching your product category online, the logic says they are closer to buying. You reach out at the right moment.
You win the deal.
The problem is that theory breaks down immediately when you look at the actual mechanics of what "intent" is measuring, who buys intent data, and how vendors are incentivized to present it.
This article looks at why B2B intent data fails most teams that buy it, and what the teams it works for do differently.
The short version: intent alone is wishful thinking. The fix is not a better vendor; it is signal stacking.
What Is B2B Intent Data?
B2B intent data is behavioral data from a company's digital activity, such as searches, content reads, site visits and ad clicks.
Sales teams use it to spot accounts that seem to be researching a purchase.
Most B2B intent data has one of two sources. Third-party intent data is collected across publisher sites and aggregated by vendors like Bombora, 6sense or TechTarget.
When a company's employees keep reading about "cold email automation", that activity is flagged as intent for the category.
First-party intent data is behavioral data you collect yourself from your own website, emails, and ads - pages visited, time on site, content downloaded, emails opened.
This is higher-quality signal because the person interacted with your brand specifically rather than a general content network.
Intent data promises a shortlist of accounts that are "in market" right now.
Depending on provider and settings, a report may include activity that needs further validation. Inspect the topic, source, time window, and account match.
Why Most B2B Intent Data Fails (The Honest Breakdown)
Intent data has a real problem, and it is not the data itself. It is the four structural failures that make it nearly useless when deployed as a standalone system.
The Single-Signal Trap
One keyword search does not mean a company is buying. It means someone typed a phrase into a search bar.
An account-level signal may not identify who performed the activity or their role. Do not infer that an authorized buyer searched.
Some providers aggregate topic activity across sources and time periods. Check vendor documentation for the signal definition, collection method, and limits.
That does not solve context. For example, activity from several people may reflect research rather than a buying decision; an activity score alone may not identify role or authority.
The Vendor Incentive Problem
Treat labels such as “in market” as a vendor-defined interpretation. Review the underlying activity and coverage before using it to prioritize an account.
Instead, anything touching a relevant keyword shows up as "in market", until that label covers much of your ICP.
This is what that AE meant when he said "companies are lying about intent data." The data is technically real.
Commercial labels vary by vendor. Check how a score is generated and what its evidence can and cannot show.
The Timing Problem
Intent data shows yesterday's research.
Signal age and delivery timing vary by source and product. Review timestamps and freshness information before deciding whether an event remains relevant.
An older activity signal may no longer reflect current plans. Verify its date and avoid treating it as evidence of an active evaluation.
Signal freshness varies by provider, source, and product. Ask vendors how collection, update, coverage, and delivery timing are defined for the specific signal.
The age of a signal depends on the source and the buyer’s workflow; check timestamps and refresh policies rather than assuming one general lag.
The Context Problem
Generic catch-all keyword topics - "marketing automation," "sales software," "lead generation" - trigger intent scores for umbrella topics that flag most of your ICP simultaneously.
Some systems may assign an account-level score from aggregated activity. The score may not show who acted, their authority, budget, or current plans.
The same keyword activity from a 5-person startup and a 500-person company with a Q2 budget cycle looks identical in most intent platforms.
The result is what one sales leader described to me as "it's no wonder sales called BS on intent" - the lists marketing hands over are so broad that reps stop trusting them.
And once sales stops trusting the data, the entire system collapses into a sourcing disagreement.
The Multi-Signal Approach That Actually Works
Examples of account signals; relative value depends on the source, context, and use case.
Intent data can be useful as one research input. Its value depends on relevance, accuracy, coverage, and how it is used.
Teams may combine it with other evidence to develop account context. The number and type of inputs depend on the use case.
A team may define a “buying context” as several relevant observations about one account. This is an internal research concept, not proof of purchase intent.
A funding announcement, relevant hiring, and review-site activity could be combined as a hypothetical research example. These observations do not prove an evaluation or purchase intent.
Do not infer a change in purchase probability from this example without validated data.
Examples of signal categories to investigate include:
Intent data is an inference about activity, not confirmation that a specific person is evaluating or ready to buy. Combine it with account context and verify before outreach.
Evaluate whether the signals help you prioritize research and whether the resulting outreach supports your stated goals.
Why Most Intent Data Finds the Wrong People
Accounts in an ICP may have different priorities and timelines. Observed activity does not reveal whether there is a budget or an active evaluation.
Intent data without context finds the wrong accounts. It surfaces the companies with the most keyword activity, including competitors, journalists, consultants and researchers without budget.
The real buyers may leave a small footprint because they are in conversations, not reading articles.
With signal stacking you find the right accounts, by requiring several independent signals from different categories before any account enters the outreach queue.
Signal stacking narrows the list to the accounts actually in a buying cycle. Your reps spend their effort on real buyers, and your domain avoids the damage of over-sending to uninterested targets.
We build signal layers for B2B companies.
A narrower research list may be easier to review. The appropriate size depends on available evidence and campaign capacity.
How to Actually Use Intent Data (The Operational Guide)
If you already have an intent data subscription, do not cancel it. Redirect it. Here is how to build a signal-stacking system on top of your existing intent data in 5 steps.
Define 5-7 Signals Specific to Your ICP's Buying Context
Review previous wins as one source of hypotheses. Look at the account context before a conversation, while accounting for differences and incomplete records.
Funding, hiring, tool changes, and leadership changes may be useful research prompts; confirm their relevance for each account.
Define the 5-7 most common ones and build your signal monitoring around those specific events, not generic keyword categories.
For a cold email agency, possible research prompts might include a funding announcement, leadership change, relevant hiring, or a public statement.
Verify source and recency before drawing conclusions.
That is fresh budget, new leadership and active evaluation. Not one signal. Five.
Set Up Signal Monitoring With the Right Tools
Apollo for firmographic filters and funding alerts. Clay to stack hiring, tech stack and intent signals in one workflow.
Sales Navigator for decision-maker activity and job changes. Bombora or your current vendor for keyword intent, as one input, never the main filter.
A shared account record can help organize source, date, and context. If you score inputs, document the model and its limits.
A scoring model may help sort records for review, but should be validated and should not be treated as evidence of intent.
Score by Signal Count, Not Intent Score
If you use an internal score, document its components, validate it against suitable outcomes, and avoid a universal threshold for outreach.
If a team uses an internal score, it can be one way to prioritize manual review. Set any categories locally and validate them; no universal point band determines whether outreach is appropriate.
A high vendor score may be based on limited or indirect activity. Review the underlying signal and its source rather than treating the score as proof of a purchase.
A numeric score or count does not confirm buying context. Review the underlying sources and definitions.
Build Trigger-Based Sequences That Reference the Signal
When an observed event seems relevant, verify it and explain its connection to the reason for contact. "Saw you raised your Series A last month - congrats.
We work with a few similar-stage companies who were building outbound at the same time." That is a relevant observation.
Avoid implying knowledge of a person’s private activity. Use only information that is appropriate to reference and relevant to the message.
The signal is your reason for reaching out. It is not your opening line. Use it to earn relevance in the first sentence - then let the offer carry the conversation.
A verified trigger may provide useful context for a message. Whether it improves results is a hypothesis to test against a defined baseline.
Review Signal Freshness Before Outreach
Signal freshness depends on the source and event.
A hiring post and funding announcement may prompt further research. They do not establish a buying cycle, budget, or a fixed period of interest.
Review when the information was published and whether it still applies. A date alone does not reveal a company’s evaluation stage or decision.
Choose alert and review timing based on the source’s freshness, provider delivery, and whether the event remains relevant; do not use a universal response window.
How Deep-Y Uses This
The 1 Solar Direct engagement is the clearest illustration. 1 Solar Direct is a U.S. solar distributor for large, utility-scale projects.
Their lead intelligence challenge was finding the companies behind active utility-scale projects - not just any energy company, but the ones with a project that fits right now.
We built a periodic scraper that pulls utility-scale project data from U.S. government sources, filters projects by fit, and enriches the matching companies.
Each account then gets role-aware email sequences, with a LinkedIn connect-and-DM follow-up for engaged prospects who did not book.
The case describes a project-related data source. It does not establish that every outreach message had a valid reason or that the data caused its results.
The linked case reports 8 meetings with project operators. It describes project-driven targeting, but does not isolate its contribution or compare it with another approach.
The 1 Solar Direct case study describes the workflow and reported campaign results; it does not establish that signal stacking will produce the same outcome elsewhere.
Our campaign planning may begin with a review of target criteria and available signals; scope and timing vary by client.
Account research can inform message relevance, but the source data, offer, copy, and timing all matter. Evaluate them separately where possible.
Frequently Asked Questions
What is b2b intent data?
Is intent data worth the cost?
What is the difference between first-party and third-party intent data?
How many signals are enough to trigger outreach?
Can a small company use intent data effectively?
The Honest Summary
Most B2B intent data fails not because the data is fake - it is real behavioral data.
It fails because vendors sell it as a shortcut to pipeline, and buyers use it as a substitute for the harder work of defining what a buying context actually looks like for their specific ICP.
Intent data may be considered alongside hiring, funding, technology changes, public activity, or first-party engagement. These signals can inform research but do not establish that an account is buying.
The teams getting real replies from cold outreach are not using better intent data.