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For B2C brands

Know which leads convert before you buy more of them.

Validate every record at ingest, put a price on why the rest do not work out, and see which supplier is responsible — before the next invoice, not after it.

Checks at ingest
Rejections priced
Source-level reporting
Rejection reasons dashboard showing why leads were rejected and the revenue lost to each reason
The problem

Sound familiar?

Buying leads is easy. Finding out which ones were worth buying is the hard part.

You pay for the lead either way

A dead number, a disposable email, someone on a do-not-call list. The invoice arrives regardless, and the cost only shows up as a sales team that is busy and not closing.

Nobody can name the supplier at fault

Conversion is down. Three suppliers are feeding you. Everyone has a theory, nobody has the number, and the conversation ends with “let us watch it another month”.

Your reps find the bad leads for you

The cheapest place to catch a worthless lead is the moment it arrives. Instead it goes into the queue, and a rep spends twenty minutes discovering what a check could have told you in one second.

How Datahubb helps

Catch it at the door, then follow the money

Check the lead before your team ever sees it

IP, email, phone, blacklist and credit checks run during ingest, before the record reaches anybody. Fraud scores, disposable addresses, dead numbers, DNC and suppression hits — you decide which ones are a rejection and which ones are just a note on the file.

Rules you write, not a black box

Each check is a condition and an action: reject when the fraud score is above your line, accept below it, combine conditions with AND or OR. The first rule that matches decides. What a check returns is written onto the lead, so you can filter and report on it later.

A price on why leads do not work out

Rejections are grouped — source quality, duplicates, validation, technical — and ranked by the money attached to them rather than by volume. That tells you which problem to fix first, which is rarely the loudest one.

Reporting that names the source

Slice the same rejections by traffic source, sub source, affiliate or supplier. When one of them is sending you rubbish, it stops being a hunch you cannot act on and becomes a row you can export and send them.

Reporting

One view of what you bought and what it did

Posted, accepted and rejected against what you spent, filtered by campaign and date. It is the number you take into the supplier call — not a spreadsheet somebody assembled the night before, and not three tools that disagree with each other.

  • Accepted and rejected counts against what you spent
  • Filter to a campaign, a date range, or a single supplier
  • Export exactly what is on screen
Datahubb dashboard showing posted, accepted and rejected lead counts with profit and trend charts
What it looks like day to day

Stop paying twice for a bad lead

Reject invalid phones, disposable emails and DNC hits at ingest, before they queue
Enrich every lead with what the checks found, and keep it on the record
See which rejection groups carry the most money, not just the most volume
Break the same numbers down by supplier and take the evidence into the renewal call
FAQ

Frequently asked questions

Whatever you switch on: IP fraud and bot detection, email validity and disposability, phone validity and line type, do-not-call and blacklist scrubs, and a soft credit check where that is relevant. Each runs during ingest, and each is governed by your own accept and reject rules. See fraud & verification.

Only if you say so. A check with reject rules is a gate; a check with no rules simply enriches the lead with what it found. Plenty of teams start by enriching and watching, then turn the gate on once they can see where their real line is.

The same rejection data can be sliced by traffic source, sub source, affiliate or supplier, sorted by the money attached to each one, and exported exactly as it appears on screen. That export is the conversation. See rejection insights.

No, and we will not pretend otherwise. It is an expected value: the leads that were lost, multiplied by what a lead actually sold for in that window, multiplied by the rate at which rejected leads still went on to sell. It sizes the problem honestly rather than assuming every lost lead would have converted.

Find out which of your leads are worth buying.

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