Are buying signals and intent data enough?

Buying signals are data points that suggest a company might be ready to buy, such as a job post, a funding round, a leadership change or a website visit. On their own they're rarely enough. They show that something changed, but every competitor watching the same feed sees them too, and they rarely prove a problem your product solves.

Common buying signals

Common examples include:

• Job postings and hiring activity
• Funding rounds and acquisitions
• New executives or job changes
• Website visits and content downloads
• Technology installs or removals
• Social activity, such as following a company page

Each one tells you something happened. None of them tells you what hurts.

Why generic buying signals fall short

Every vendor watching the same feed sends the same "congrats on the raise" email. The signal is common, so the message is too.

Some signals aren't intent at all. In his Sculpt talk, Jordan Crawford showed a real cold email to someone who works at Granola that opened with "Saw you're following Granola on LinkedIn, are you using them today?" Following a company isn't a buying signal, and the sender didn't filter out the company's own employees.

Jordan's advice is to work backwards from why your customers bought and why they didn't, then find what you could have seen beforehand, instead of buying signals a vendor wants to sell you.

What a pain signal is

A pain signal is narrower. It's a piece of data that shows a specific, current problem your product solves. It's harder to find, which is why it works. Fewer competitors see it, and the message practically writes itself.

Examples from the Blueprint homepage:

• A crane with no DOT transit permit on file, half a mile from a newly filed wind turbine permit
• Seven providers whose PECOS enrollment has lapsed, so their Medicare claims are being denied
• 127 roofing contractors who use some GAF product but give 70% of their volume to Owens Corning, found in permit data

Another Blueprint project built three campaigns from federal data, using CMS quality alerts, OSHA violations and HRSA audit findings. These are the kinds of facts Jordan means when he says the data is the message.

Reading signals for pain instead of change

The same source can be a generic signal or a pain signal, depending on how you read it. Job posts are a good example. Jordan built a tool called JoJo that reads about 947,456 public job postings from 16 hiring systems. Commercial hiring tools tell you a company is hiring. JoJo reads the description to work out what the company is struggling with, and it keeps a quote from the posting as evidence. If there's no quote, it makes no claim.

His point is that a job post is a company describing, in its own words, a problem it will pay a salary to fix. Read that way, a common signal becomes a specific one. For more public sources like this, see data sources for prospecting.

When generic signals still help

Generic signals are useful for timing. A job-changer campaign is one example. You monitor a list of people you already know, and when one of them changes jobs, they get a message written for exactly that situation.

So the rule is simple:

1. Build the segment on pain.
2. Use generic signals to decide when to reach out.
3. Write the message around the pain, not the signal.

Jordan's point of view

Jordan's view is that firmographic targeting treats buyers as rows in a database, when they actually live in messy, discoverable situations. Two companies with the same title, industry and headcount can have completely different problems. Stop asking which row in your CRM to target, and start asking which situation in the real world to catch. That's the idea behind a Pain-Qualified Segment.

To see segments built from pain signals, browse the Blueprint Showcase.