Most B2B marketers know how to identify the companies they want to reach.
They define an ideal customer profile by industry, size, technology, geography and buying role, then build account lists and pursue them through advertising, content and outbound.
That approach still works, provided your message addresses a problem the buyer recognizes and agrees is worth solving. Engagement then can then help reveal which accounts may be receptive to a conversation.
The big challenge has always been timing.
Chet Holmes's Buyer's Pyramid estimates that only a small percentage of any market is actively buying (like 5%). The harder question is how to identify those accounts, along with the ones moving toward a buying cycle, before they raise their hands.
Finding them through the channels we have traditionally relied on, including advertising, content and direct sales outreach, is becoming increasingly expensive.
Buyers ignore irrelevant ads and delete emails that sound like sales pitches. Every wasted impression and unanswered message raises the cost of reaching someone who may be receptive.
Wouldn’t it be great to know exactly who to call at the moment they begin experiencing a problem you can solve?
That is essentially what direct-response marketers were trying to accomplish when they rented lists of people known to buy in a particular category.
They could not know who would respond, but they reduced the guesswork by purchasing category-specific lists of people whose previous buying behavior made them more likely to purchase the new product.
AI may finally give B2B marketers a way to apply that same discipline to complex sales. But instead of looking for previous purchase behavior, we can look for the conditions that tend to create the problem our product solves.
But that requires understanding the job customers hire our product to do.
We need to know what they were trying to accomplish, what prevented their progress and what changed that made the problem important enough to solve.
Those patterns can become alpha signals that AI researches across the target market. The result is a smaller, more qualified list of accounts where the problem may be emerging, giving marketing a relevant message to put in front of the right people and sales a credible hypothesis to test.
The first step is identifying the conditions that caused your customers to need what you sell.
As Bob Moesta explains in Demand-Side Sales 101, customers tend to enter a market when they are struggling to make progress and realize their current approach will not get them where they need to go.
That struggling moment is the job-to-be-done insight. It usually cannot be observed from the outside, so the best place to begin is with your own customers.
Instead of asking only why they selected you or what they like about the product, go further back:
▪ What was happening before they started looking?
▪ What changed inside the company?
▪ What were they trying to accomplish?
▪ What prevented them from making progress?
▪ What new pressure made the old way of doing things harder?
When the same conditions appear across several customers, you can begin looking for public signals that those conditions may be developing elsewhere:
▪ A company raising funding may suddenly face much more pressure to generate pipeline.
▪ A new CRO may inherit an aggressive growth target while simultaneously building out the sales team.
▪ Expansion into a new market may expose messaging that worked well with one audience but fails with another.
▪ An acquisition may leave two sales organizations, technology stacks and customer stories that need to be brought together.
Those signals become useful when customer research shows that the same observable changes preceded the struggling moments that caused your customers to buy.
The public signal is what you can detect. Your customers’ history helps you anticipate the problem that may follow and gives you a credible reason to reach out with an observation and a question.
For example: “I saw that you’re launching a new product. Is that creating pressure to generate leads for it while much of the market still thinks of you as an X company?”
You are not claiming to know that the problem exists. You are using a visible change to form a reasonable hypothesis and test for problem agreement.
That is what turns an observable event into a potential Alpha signal.
Account-based advertising has become good at putting ads in front of the right companies and people.
The ads themselves, however, still tend to focus on the product, the company or a generic benefit rather than a problem the buyer recognizes.
The people who engage with those messages are likely to be already in market, which may be too late to make their shortlist unless your position is unusually compelling and differentiated.
For everyone else, repeatedly serving irrelevant ads becomes an expensive exercise in being ignored. Every impression costs money, and frequency compounds the waste when nobody has a reason to pay attention.
An Alpha signal gives the advertising a more relevant subject. If an observable change suggests that a problem may be developing, you can put that problem in front of the people most likely to experience it and see whether it resonates.
The advertising can then build problem awareness or problem agreement before the buyer begins an active search and before sales reaches out.
Marketing also needs to give sales more than an engagement alert.
Saying that an account clicked an ad or visited the website does not explain why the account was prioritized or how the salesperson should approach it.
Sales needs to know what changed, what problem may follow and whether the account engaged with messaging about that problem.
Marketing and sales should develop these assumptions together. Sales often recognizes the conditions that appear before an opportunity, while marketing can find those patterns across a larger market and test relevant messages against them.
A salesperson might then say, “I noticed you’re expanding the sales organization pretty aggressively. We’ve seen companies at a similar stage struggle to generate enough qualified pipeline to keep that additional capacity productive. Is that something you’re dealing with?”
The signal gives sales a credible reason to start the conversation, while the advertising may have already made the problem recognizable. Together, they create a more useful path to problem agreement than treating an ad click as evidence that someone wants to buy.
Until recently, this kind of research was difficult to conduct across thousands of target accounts. A salesperson could closely follow a handful of strategic accounts, but no team could manually monitor an entire market for executive changes, hiring patterns, expansion, acquisitions and other relevant conditions.
AI makes that research practical at a much larger scale.
Once you have identified the observable patterns that preceded your customers’ struggling moments, AI can look for those patterns across your ICP and surface the accounts where a similar problem may be emerging.
Marketing can then address that problem, and sales can approach the account with a hypothesis worth testing.
Your ICP tells you who could become a customer. Alpha signals can help you determine when a problem may be emerging and what to talk about when it does.