Sales Intelligence

Intent Data Buying Signals

Intent data buyers are often dissatisfied with expensive provider data that doesn't convert. They're looking for better signals — and posting about it publicly.

What are Intent Data buying signals?

Intent data discussions happen in RevOps, ABM, and demand gen communities. Buyers question the accuracy of Bombora/G2 intent signals, describe low conversion rates from intent-driven campaigns, or look for community-based intent alternatives.

Where do Intent Data buyers post?

Reddit
Hacker News

SignalPipe monitors these platforms every 10 minutes and scores each post for genuine Intent Data buying intent.

Example Intent Data buying signal posts

"Bombora intent data hasn't been converting for us — what are people using instead?"

Why it's a signal: Competitor dissatisfaction. Active switch.

"Ask HN: does anyone have a good source of real-time B2B intent signals that isn't $50k/year?"

Why it's a signal: Budget-constrained intent data buyer.

"We want to target companies that are actively looking at our category — what signals exist?"

Why it's a signal: Intent-aware buyer. Exact fit for SignalPipe.

"G2 intent signals are too noisy — anyone found better signals from community monitoring?"

Why it's a signal: Community-signal awareness. High fit.

Anchor sentences for detecting Intent Data buying intent

These are buyer phrases written from the buyer's perspective. SignalPipe scores posts against these using embedding similarity. Add them when calling signalpipe_add_product.

1"looking for affordable intent data alternative to Bombora or G2"
2"need real-time buying intent signals from community discussions"
3"want intent data that comes from Reddit and HN posts not review sites"
4"replacing third-party intent data with community-based signal monitoring"

How does SignalPipe detect Intent Data buying signals?

1. Keyword gate

Filters obvious noise — spam, off-topic posts, and low-quality content — before any expensive processing.

2. Embedding similarity

Compares every post against your Intent Data anchor sentences using vector similarity. Catches semantic matches keyword matching would miss.

3. Sarcasm filter

Detects posts that superficially match but are complaints, jokes, or negative mentions — removes false positives before the LLM stage.

4. LLM swarm

Three judges — Skeptic, Analyst, Optimist — vote on the signal. The Skeptic has a hard veto. Only posts that pass all three reach your queue.

About 85% of posts are filtered before you see anything. What reaches your queue is the 15% with genuine Intent Data buying intent. Full pipeline explained here →

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