"We already have intent data. Why would we need buying signals?"
I get this question at least once a week. And every time, it tells me the same thing: the sales intelligence market has done a terrible job of explaining what these terms actually mean.
Intent data and buying signals measure fundamentally different things. They work on different timelines, they come from different sources, and they're useful in different situations. Most teams treat them as interchangeable. They aren't, and confusing the two leads to wasted budget and wasted rep time.
What Intent Data Actually Measures
Intent data tracks content consumption patterns across the web.
When someone at Acme Corp reads three articles about "CRM software comparison" and downloads a whitepaper on sales automation, intent data providers detect that activity and flag Acme Corp as "showing intent" for CRM tools.
The mechanics are straightforward:
- Publishers install tracking pixels on their sites
- Visitors consume content (articles, guides, comparison pages)
- The intent provider aggregates that activity by company, usually through IP-to-company matching
- Companies with above-baseline activity on a topic get flagged as "in-market"
The major providers include Bombora (which runs the largest intent data co-op), G2 and TrustRadius (review site intent), and 6sense (which blends proprietary data with third-party sources).
What you learn from intent data is directional. A company is researching a topic. The research intensity is above normal. You might know which topics they're looking at. Sometimes you can identify specific individuals, though that's rarer than vendors admit.
It's useful information. But it's incomplete information.
What Buying Signals Actually Measure
Buying signals track business events and structural changes that indicate a company is in a position to make a purchase.
When Acme Corp raises a Series B, brings in a new VP of Sales from Salesforce, and posts five SDR roles in the same week, those are buying signals. They tell you something concrete about the company's situation, regardless of what content anyone there is reading.
The main categories:
Funding events. New capital means new budgets. Post-funding companies typically increase software spend by 30-50% within six months.
Executive changes. New leaders come in with mandates to make changes. A new VP of Sales will evaluate the existing tool stack, identify gaps, and make purchasing decisions within their first 90 days.
Hiring patterns. A company posting five roles in your target department is telling you exactly where they're investing. They need tools to support that growth.
Tech stack changes. When a company adds or removes technology, they're in active evaluation mode. Adjacent purchases often follow.
Organisational changes. M&A activity, restructuring, new office locations. These events break existing workflows and create demand for new solutions.
Growth indicators. Revenue milestones, customer wins, market expansion. Growth breaks systems. What worked at 50 employees stops working at 200.
Buying signals tell you that a company has budget, new priorities, scaling pressure, or active change happening. These are the conditions that precede purchases.
Why This Distinction Matters More Than You Think
The core difference is simple.
Intent data tells you someone at a company is researching. Buying signals tell you the company is structurally positioned to buy.
Research does not equal purchase intent. People research things they will never buy, all the time:
- Competitors running market analysis
- Analysts writing industry reports
- Students working on projects
- Junior employees with no buying authority exploring options
I've seen intent data flag a company as "high intent" for sales engagement tools because a marketing intern was writing a blog post comparing vendors. That's not a buying signal. That's noise.
Buying signals, on the other hand, map directly to the conditions that create deals. Think BANT, but observed rather than self-reported:
- Budget comes from funding events
- Authority shows up as executive changes
- Need appears as growth pain and hiring surges
- Timeline is created by the urgency of change
When a company just raised $30M and hired a new CRO who's posting roles, you don't need to guess whether they have budget or authority. You can see it.
Where Intent Data Earns Its Keep
I'm not here to trash intent data. It has real uses, just not the ones most teams default to.
Advertising targeting. This is where intent data genuinely shines. If you're running ABM campaigns, knowing which companies are researching your category lets you put ads in front of them at the right moment. You don't need high precision for ad targeting. Directional is good enough.
Volume plays. If your SDR team needs to fill the top of funnel with thousands of accounts, intent data delivers volume. It casts a wide net. For teams that need quantity and have a process to qualify downstream, that works.
Content and messaging strategy. Intent data tells you what topics your market is researching. That's valuable for marketing teams planning content calendars and shaping messaging. It's market intelligence, not sales intelligence, and that's fine.
Topic-level insight. Sometimes you want to know not just that a company might be interested, but what specifically they're looking into. Intent data captures the topic dimension in a way that buying signals don't.
The pattern is clear: intent data is strongest when you need breadth, directionality, and volume. It weakens when you need precision and actionability.
Where Buying Signals Pull Ahead
For direct sales outreach, buying signals win. It's not close.
Conversion rates tell the story. Signal-verified accounts convert at 3-5x higher rates than intent-only accounts. When I talk to sales teams that have used both, they consistently report the same thing: signals produce meetings, intent produces emails that disappear into the void.
Personalisation becomes real. Signals give your reps something specific and genuine to reference. "Congrats on the Series B, I'd love to talk about how we help post-funding teams scale their outbound" is a fundamentally different email than "I noticed your company might be interested in sales tools." One sounds like a human who did their homework. The other sounds like a bot.
Timing is baked in. A new VP is most receptive in their first 30 to 60 days. A funding round creates urgency that fades within 90 days. Signals carry an inherent timestamp. Intent data, by contrast, often arrives with a 2-4 week delay and no indication of where the company is in their evaluation process.
Your ICP gets sharper. If you know who buys your product, signals help you find those exact companies at the exact moment they're ready. Intent data tells you who's looking. Signals tell you who's ready.
For outbound teams, especially smaller ones that can't afford to burn cycles on low-probability accounts, signals are the higher-leverage investment.
Combining Them: The Triangulation Effect
The strongest account intelligence comes from layering both data types. Neither one alone gives you the full picture.
Consider three scenarios:
Intent only: "Acme Corp is researching sales tools." Could be anyone at the company, for any reason. Maybe a buying committee is evaluating vendors. Maybe someone's updating an internal wiki. You don't know. Roughly 40% of the time, it means something real.
Signal only: "Acme Corp raised a Series B and hired a new VP of Sales." That company is clearly positioned to buy. But buy what? They might need sales tools, or they might be investing in marketing, or product, or infrastructure. You know they're spending. You don't know on what. Relevant about 70% of the time.
Signal plus intent: "Acme Corp raised a Series B, hired a new VP of Sales, AND is researching sales engagement platforms." Now you have structural readiness combined with active research. That's a company with budget, a new decision-maker, and demonstrated interest in your category. Confidence jumps above 90%.
The math isn't complicated. Each data source eliminates a different type of uncertainty. Signals confirm the company can and will buy. Intent confirms what they're looking to buy. Together, they give you something close to certainty.
The Quality Problems Nobody Likes to Talk About
Both data types have real quality issues. Vendors on both sides tend to gloss over these.
Intent data's weaknesses:
IP-to-company matching is inherently imprecise, especially as remote work scatters employees across residential IPs. Most providers can't reliably identify the individual doing the research, just the company. The data typically lags 2-4 weeks behind actual behaviour. False positives are common, and competitive research is indistinguishable from buying research.
The biggest issue is that intent data vendors have a structural incentive to flag more accounts, not fewer. A provider that returns ten "high intent" accounts feels less valuable than one that returns a hundred, even if the smaller list is more accurate. This pushes the industry toward sensitivity over specificity, which means more noise for sales teams.
Buying signal weaknesses:
Not all signals are equally predictive. A Series C funding round is a stronger signal than a junior hire. Context matters enormously. A single signal alone can mislead, which is why stacking multiple signals matters so much. Some signals are hard to detect in real time, and coverage varies by geography and company type. Public companies are easier to monitor than private ones.
The quality solution for both is the same: triangulation. One signal is interesting. Two signals are worth investigating. Three signals from different categories is a Tier 1 account.
Practical Guidance Based on Where You Are
If you're building your sales intelligence stack from scratch, start with buying signals. They give your reps something to say, something specific to reference, and a reason to reach out now rather than next month. Intent data can come later once your outbound motion is established.
If you already have intent data and it's underperforming, the most common reason is that your reps are treating intent flags as qualified leads. They aren't. Layer buying signals on top to separate the accounts that are genuinely positioned to buy from the ones that are just browsing. Use intent as a prioritisation filter within your signal-verified accounts, not as the primary trigger for outreach.
If you already have buying signals and want more precision, adding intent data helps you prioritise which signal-verified accounts are actively researching your category. It's the difference between knowing a company can buy and knowing they're actively looking.
If you're resource-constrained and can only pick one, use buying signals for outbound and intent data for inbound and marketing. Signals drive direct revenue through outreach. Intent data drives indirect revenue through smarter advertising and content strategy. If your bottleneck is pipeline from outbound, signals are the higher-impact choice.
So Which One Is "Better"?
Depends on what you're doing with it.
For advertising and content strategy, intent data. For direct outreach and pipeline generation, buying signals. For maximum confidence, both.
But if I'm being honest about what I see in the market, most B2B sales teams would get more value from investing in buying signals first. Intent data has been the default for years because the major providers have bigger marketing budgets and more brand recognition. That doesn't make it the right starting point for a team trying to book meetings and close deals.
Buying signals tell you something observable and verifiable about a company's situation. Intent data tells you something inferred and probabilistic about anonymous web activity. Both are useful. One is more actionable.
At HighTempo, we built our platform around buying signals for exactly this reason. We found that when reps have specific, verified events to reference in their outreach, the conversations that follow are fundamentally different. Not because signals are magic, but because they give you a real reason to call and your prospect a real reason to listen.
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