How do you identify companies that are ready to buy?
Posted by Applied AI Commons team (community team) on
Timing matters in sales: the same message can land well or be ignored depending on whether a company is actively looking for a solution. Many signals are visible in public, from hiring patterns to leadership changes to new initiatives, but not all of them predict real buying intent.
Some angles to consider: - Which signals have been genuinely predictive in your market - How to combine weak signals without creating noise - How to keep signal tracking from turning into busywork - Where AI helps with spotting signals, and where it misleads
Which buying signals do you trust most, and how do you confirm a company is actually ready before reaching out?
5 replies
Applied AI Commons team (starter reply from the community team),
It can help to separate signals into three groups: - fit signals: industry, size, technology in use, whether the company could benefit at all - change signals: new leadership, funding, expansion, a relevant hiring push - intent signals: actively researching the problem, asking for proposals, engaging with related content Fit tells you who to consider, change suggests a window may be opening, and intent is the closest to readiness. Many teams treat change signals as if they were intent, which can lead to well-timed outreach to companies that are not actually looking.
Applied AI Commons team (starter reply from the community team),
Combining weak signals works better with a simple, explicit scoring rule than with intuition. For example, give each signal a weight, require at least one change or intent signal before an account counts as "warm", and let signals expire after a set number of weeks. Reviewing the scores against actual outcomes every quarter shows which signals carry weight in your market. Keeping the model small, with a handful of signals rather than dozens, also makes it easier to explain to the people acting on it.
Applied AI Commons team (starter reply from the community team),
Where AI helps: scanning job postings, announcements and public pages at a scale no person could, and summarising them into a consistent format. Where it can mislead: inferring intent from thin evidence, such as treating a single job advert as a strategic initiative, or misreading generic press language as a buying trigger. It also tends to present weak inferences in confident language. A useful habit is to have the output always show the underlying evidence, so a person can judge whether the signal is real.
Applied AI Commons team (starter reply from the community team),
A sceptical note: public signals mostly show that something has changed, not that a company has a budget and a decision-maker ready to act. The most reliable confirmation is still a conversation. Some sellers treat signals as a reason to start a low-pressure, relevant exchange, then qualify readiness directly with questions about current priorities, timelines and who is involved. Treating signals as a prompt for curiosity rather than proof of intent can reduce the awkwardness of pitching too early.
Applied AI Commons team (starter reply from the community team),
To keep signal tracking from becoming busywork, tie each signal to a specific action. If a signal does not change what a seller does next, it probably does not need tracking. It also helps to set a fixed weekly review time rather than reacting to every alert as it arrives. We would like to hear from members across different markets: which signals have been genuinely predictive for you, and which ones looked promising but turned out to be noise?