When lead search finds nothing, I keep the original brief

The question

When a lead search finds no qualified companies, I keep the original brief beside the result. I need to see which part of that search produced the zero before I broaden the market or loosen a rule.

Broadening until a list appears can change the question I asked. A search for a company with a relevant project can become a search for any company hiring a familiar title. The second list may be useful, but I would judge it against its own brief.

My working rule is to make the next run answer one unresolved question. I want Mach to keep the source results, rejection reasons and unfinished work available so a seller can decide what needs changing. Research has to explain the result as well as produce it.

The list does not show what was missed

In an r/LeadGeneration discussion[1], u/Unlucky_Security9365 said the “biggest issue” was “you have no idea if you found everything.” They described finding irrelevant people while missing others they expected to see.

In the same thread, u/FreshProspects[2] said Boolean search “seems to cut out people who would be genuine leads.” These are users’ reported experiences with another product. They are not verified tests of its current behavior or proof that their searches returned nothing.

That completeness concern is what I want an empty-search review to address. A final list cannot tell me whether there were no candidates, a filter excluded them or part of the search failed. I need the work behind the count.

Keep the original question visible

Imagine a software-services team searching for companies that need help with data integration. This example is fictional. The search checks job ads, public posts and news, then returns no companies ready to contact.

The job search might have returned no ads. It might have returned many permanent roles while the seller offers project services. News might have identified companies outside the chosen countries. A source might have failed, or the model might have stopped before trying it.

I would make a different decision in each case. A failed source needs investigation. The wrong kind of work may call for different terms. Relevant companies with no reachable people need a separate ownership search.

If the team decides to include another country or a different kind of project, I would save that as a changed brief. Then the next result has an honest comparison point. Otherwise the team can spend more and get a list while losing track of what the first search was meant to find.

Four things I want from the report

For each source, I want to be able to answer:

Step 1: What did it return?

Step 2: What was new to the team?

Step 3: What was kept or set aside, and why?

Step 4: What did the search spend or fail to finish?

Open examples from the largest rejection group before changing a filter. If results were labeled as companies selling the same work, I need to know whether they are competitors or might also buy that service. If the search finds permanent roles, I need to check whether the offer includes permanent hiring.

I would also review results that passed. A dated event at a real company can still have a weak connection to the offer. An explanation earns its place when another seller can check it against the source.

This is where a readable ranking matters to me. I need to understand which observation changed the decision. A score without that trail gives me little help deciding what to change next.

Zero kept can have several explanations

One Mach report test makes the distinction concrete. It constructs 21 rejected posts: 11 labeled “seller” and 10 labeled “not an ask.” The assertions expect a warning that nothing was kept. They do not expect a warning about one dominant reason, because neither reason reaches the rule’s 80% threshold.

The same test changes the split to 18 and 3. Now one reason accounts for enough of the group to trigger the second check too. These are constructed test inputs; the editorial review did not execute the test.

My read is that those two cases deserve different reviews. In the first, I would inspect several explanations. In the second, I would start with the dominant reason. In both, I still need to decide whether the labels describe the records correctly.

Reviewing rejected candidates can reveal false negatives: relevant companies a rule set aside. Google’s guide to precision and recall[3] explains that distinction. A manual sample can expose a problem, but I would not call it a complete recall measurement without knowing the full set of relevant companies.

What Mach’s report can flag

The inspected report groups graded leads by source, shows drop reasons and reports spending. Its fixed checks include a group with at least 5 graded results and none kept, one reason covering at least 80% of a group, and a tool returning empty results or problems on at least half of 4 or more calls.

Those checks can point a reviewer toward a source or repeated reason. They cannot establish that the source is bad or the rejection was wrong.

The report can also flag a run that stopped with much of its data budget unspent. A separate model-cap check applies to runs with that kind of cap; scout-team runs using a combined budget do not use it. I want those execution limits beside the result before I draw a conclusion about the market.

Give the next run a job

Before spending on another search, I want a written question for the next run to resolve.

If a source failed, the next job may be to establish whether it can return usable evidence. If the sources work but the terms find the wrong work, the next job may be to test a more specific phrase. If the examples fit but are old, the question is whether an older event could still support a relevant approach.

I would keep the offer and audience stable where possible and record the change. If I choose to change the audience, I want that decision visible too.

The companion piece on testing a lead-search instruction shows how I would compare that change. The Mach AI SDR pilot scorecard puts review effort, costs and later outcomes in a broader evaluation.

How this post was made: AI assisted the research and drafting. Quotes were checked against retrieved public records, and implementation details against a source snapshot dated 5 November 2026. The source review covered code and public writing; live searches and customer outcomes were outside its scope.

What would you need to learn from an empty search before changing the brief?

Related reading

This post is part of our guide to finding buyers who are asking for what you sell.

Related reading: Why a lead search drops most of what it finds and The plan had 4,085 phrases. The budget ran 226.