The disparaging label Junk Science is a well-known adjunct to biased, rigorous or plain flawed findings based on what cannot be considered "fact".
The pandemic served up a slew of such.
One stubborn problem with their inevitably clickbait apocalyptic proclamations, is that true insight was swamped. Actual data-led hypotheses, policy and actions derived which may well prove beneficial befell suffocating undiscovery.
Junk Science definitions apply labels ranging from spurious to the downright fraudulent.
With two years of coronavirus now behind us, is it now time to propose Junk Selling definition?
How might we both avoid falling into the pit of discredit down which nearly all scientists skydived, and also be able to call out such misplaced 'findings' when our prospect is exposed to them from elsewhere?
In the world of finance, the tag 'junk' describes anything from a country's credit status, to a company stock value or debt as having practically little or no value.
Similarly then, any Junk Selling is that without any merit for the potential buyer.
You could even produce a checklist around it, such as:
How to spot it (have competitor trap alerts)
How to expose it (set challenger traps yourself)
How to avoid spouting the like yourself (know your data & its veracity)
I might venture that the most common Junk Selling trick is to overstate how wonderful buyer life will become once signed up. Specifically, when linked to some enormous eye-popping top-line expansion.
This fairly vacuous macro level does not, in my experience, fly.
Especially when the numbers to back it up look at best, tenuous.
Yet salespeople remain prone to the outlandish impact tale.
I saw early in my career how getting excited about helping a client make millions extra gets greeted with prospect silence. Whereas talk of a micro value, such as the relief a process gains from pressing a single new button each day, generates many intrigued questions.
In my present endeavours, I ask for examples of their main frustrations with video meetings. Twenty-four months in with this, I've an example fixing pretty much anything confided in me.
It also gives a neat escalation ladder effect. As someone can start off by moaning about surface level frustrations of nostrilcams, un-mutings and parallel working participants. Yet these can lead into the layers beneath that really make the difference. Such as lifting general engagement, video meeting positionings and the intricacies of truly getting what you want out of such calls.
So although such starting place may at first feel a touch broad, you can then start to highlight and assess a value associated with removal of each gripe that narrows focus beautifully towards a deeper gain.
Which in turn becomes extremely attractive. Especially when the remedy is straightforward to both take and measure.
Whenever confronted with this combination of scarcely believable lazy global headline number, an examination of the assumptions usually removes the mask of deceit.
Especially if there's previous examples of prediction errors which likely expose both model and modeller proclivity towards bias.
Ones you hopefully have evidence of.