I like podcasts. I suppose it’s because it feels productive while I’m unable to do much else – driving, sitting on planes, etc. There are a few I like in particular, all of which I’d recommend: From Our Own Correspondent from the BBC,Surprisingly Awesome from Gimlet Media, and This American Life, from Chicago Public Radio.
Today I listened to last week’s This American Life (I’m usually running something of a podcast backlog). It was Episode #585, if you’re interested. In Act II of the program, there was a discussion of an academic study of how people rate their own performance at certain tasks. It turns out that less competent people, when asked about the performance in a given task, consistently rate themselves as much better than they are. An example cited was someone who performed in the 11th percentile range (where 89 out of 100 people would perform better), but rate themselves in the 65th percentile.
Interestingly, highly competent people rate themselves lower than actual performance, but for a different reason. The competent work on the assumption that everyone else is at their level of competence (under which scenario the basis for the percentile increments is radically different, of course).
The academics who discovered and proved this psychological effect gave their names to it: Dunning-Kruger. They have, understandably, mixed feelings about having their names so intimately associated with the concept of unconscious incompetence.
There’s a lesson to be learned for sales leaders here. Clearly, sales teams have a range of competence within them. But the less-competent are not always aware of their shortcomings. For this reason, PROS strongly advocates for enablement for salespeople to achieve potential price performance. Many large organizations invest in sales automation, CRM, and other technologies to help sales people do their jobs — but that will all come to naught if the underperformers in the organization give away pricing and undercut deal value.
We solve this by providing optimized price guidance right inside CPQ. When a salesperson has to determine the price for a particular offering, PROS leverages data science and micro-segmentation to determine what will be the best, winning price. The factors are specific to each of our customers, their needs and their marketplace, but it’s fair to say that it will take into account wining pricing for similar products sold to similar customers, may include a strategy component (“turns” and “earns” for a distributor, for example), as well as product lifecycle, and perhaps considerations of customer “stickiness.”
A lot of companies rely on teams upskilling themselves through various forms of sale coaching: mentorship, on-the-job training, “shadowing’ and that staple of ill-prepared team-meetings: “Share Best Practices.” The trouble with employing these methods to reach revenue and profit potential, is that the highly competent largely underrate their abilities, so it’s hard to pick the high-performer to bring up the rest of the team. And another potential “high performer” pitfall you end up with someone who excels at closing deals at all any cost but fails to protect margin and sell value. Worst case scenario, a highly charismatic quota buster who over discounts on every deal is chosen to be the one from whom the others learn… and drags everyone down to their level. This is where data science-backed sales and pricing technology can really step in to provide sound, prescriptive recommendations that can drive revenue and profit realization.
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