Beyond Automation: Elevating Lead Generation with a Human Edge

Written by Darren Griffin on December 01, 2023

I had a QBR (Quarterly Business Review) with a client recently and the conversation turned to how we verify and validate leads. We delved into the value added by our human ‘eyeballers’ who check every lead after it has gone through all our automated checks. They are our fail-safe measure to ensure everything’s solid and good to go, blending tech efficiency with a bit of human finesse. 

In this blog, let’s dive into the reasons why incorporating human checks for all leads generated is a compelling game-changer. I’ll highlight the unique insights and qualitative edge they bring to the table. 

How the LeadScale Engine works

As a reminder this is the process a lead goes through when it is onboarded by our proprietary software LeadScale Engine. 

    • Deduplication, suppression and repeat frequency checks (have we hit maximum acceptable leads from one company for example). 
    • Custom filter checking – looking for obscenities, fake names etc. 
    • Contact verification – are the phone number and email address’ serviceable (and the email address is not personal). 
    • Eyeballing – this is where a human operator collates evidence that the person being checked exists, that their firmographic details are true and then finally that record fits the campaign and lead specification. 

The practical uses of the Engine

I set out to investigate my client’s particular campaign. 

We rejected 46% of leads that were submitted to us, but this doesn’t paint a fair picture, a lot of the publishers on plan were having trouble utilising the encrypted dynamic suppression lists (which we are working with them on, good encrypted suppression enhances the experience for everyone and often leads to lower CPLs). 

So, let’s strip out the suppressed leads. 

Once we do that, we get a total rejection rate of 23.8% from all the leads that were submitted to us after suppression. When we reject a lead it’s because it failed on one of the above 4 points and isn’t paid for. 

Out of those 23.8% of rejected leads, 16.4% were automatically rejected and 7.4% were rejected after each lead was checked by a human ‘eyeballer’.

Why were leads rejected?

Let’s have a look at what we rejected automatically. As a reminder these leads failed one of the first three points from above.

We can see here that there were a high number of duplicates. This was on one of our larger campaigns with close to a dozen partners on board, so duplicates are always going to be a bit higher. 

There were a handful of personal email addresses given, but it rounded down to 0%. 

Now let’s look at what happened when each lead that passed the automated checks was placed in front of our human ‘eyeballers’ for added reverification and checking to see whether each record fitted with the agreed campaign and lead specification. 

As you can see a significant number of leads were rejected because they weren’t the right job function or the right industry.  

Other reasons why we might reject a record but weren’t a problem on this campaign are 

    • Contact no longer at company 
    • Non-spec country 
    • Generic email address 
    • Not on target list 
    • Excluded company 

The checking of the industry vertical is especially vital to this particular company as they have specific nurture tracks depending on the industry that the record is in. 

They are also unable to properly service clients in certain industries, so there were significant savings there. 

With Job Functions what we quite often see is that an operations manager may have been submitted as an IT manager, CX director or as a finance director. 

It’s vital for our clients that they know exactly who a record is so that they can get the right messaging to them (which reduces unsubscribes) and so that they get routed to the right nurture tracks and sales teams. 

Something additional to note here that’s not represented in the table above is that our human ‘eyeballers’ will correct records before they are evaluated as a fit for the campaign. 


Are you checking every lead by hand as they come in? 

Probably not, it’s far too onerous a job for most marketing departments. But we’ve just shown you that 7.4% of leads on a recent campaign weren’t fit for purpose and should have been rejected and not paid for. 

Then you need to factor in the cost of trying to convert a lead. We know from our 15 years of experience in lead generatoin and servicing many clients, that it can cost anywhere from 3-10 times the cost of a lead to try and convert it. 

If you’d like to find out how we can help implement our gold standard verification and validation into your demand generation programs and see how you can benefit from the value we add without feeling our costs (hint, our buying power and breadth helps us find better value partners), then please do contact me via email, or add me on LinkedIn. 

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