Lays Potato Chips. Movie Theater Popcorn. Toll House Chocolate Chip Cookies. BBQ Ribs. Fudge Brownies. Rolos (a personal favorite from years ago). All junk food which, after having the first one, you just can't stop there. You must have more. Lays even had that as a slogan back in the late 60's - "Bet you can't eat just one." Back then I couldn't stop at one.
Last week I wrote an article that said companies are hiring the wrong salespeople 77% of the time. It was very popular and there was a great discussion on LinkedIn but similar to the junk food, you couldn't read that one article and move to another subject. You need to have some more.
That article was filled with data to illustrate the differences between good salespeople versus those who actually get hired most of the time. It was ugly and there were questions about the 77% like, "Where does that come from?"
Some of the supporting data came from the CSO Insights 2018 Sales Talent Study. Some of it came from Objective Management Group's evaluations and assessments of 1.8 million salespeople. And I'm going to show you some data that most people never get to see. Take a look at these wild numbers!
In the first graph, you can see the overall recommendation rate from 2014 through mid-November of 2018 from OMG's Sales Candidate Assessments.
While the overall rate varies by no more than 4 percentage points over the past 5 years, from a low of 37% to a high of 41% the overall rate is very deceiving.
OMG has 5 levels of difficulty and the criteria for a recommendation becomes more rigorous as the difficulty of the role increases. There are as many as 11 second-level customizations that could cause a candidate to be not recommended if their sales DNA doesn't support a required selling activity. And there is a third-level of customization that can override the criteria and customizations above to alter a recommendation.
Between the sliding scale and two additional levels of customization, it's very impressive that the overall rate hasn't varied by more than 4% over the past 5 years. Let's review the recommendation rates for all 5 difficulty levels.
The first two columns on the left show the overall recommendation rates that appeared in the graph above. The overall rates are the averages across all ten columns for each year. There are 2 types of recommendations - recommended (continue with the interview process) and worthy of consideration (continue if there aren't enough candidates that were recommended) - for each difficulty level. So that's 10 ratios to track per year. These are some of the ratios that stand out for me:
Sure, it takes patience and discipline to attract, assess, interview, select and on board salespeople who will succeed in their roles. But patience and discipline aren't strangers to finance, manufacturing, operations, marketing, R & D, engineering, design, fulfillment, quality control, IT, IS, or most of the other functions and departments in a successful business. So isn't it time that we stop fooling ourselves and continuing to believe that sales is different and we have to accept the hand we are dealt? That thinking causes executives to have Cause a Rationalization for Aggravating Performance. CRAP. You can read more about CRAP in sales. More importantly, you can have access to the most accurate and predictive sales candidate assessment on the planet. Named Top Sales Assessment Tool for 7 consecutive years, you can be as confident about the salespeople you select as all of our clients are.
When a great salesperson is recommended by Objective Management Group's (OMG) Sales Candidate Assessment, and this star has a great track record, and great references, should we expect this person to succeed?
Most executives do.
But even though salespeople will tell you that "If you can sell, you can sell anything", that statement is only true some of the time. Here are some examples of salespeople who are successful in one environment, but usually fail in another:
For example, if you go back and take another look at #4, this is where great salespeople, selling the exact same thing, can suddenly fail because they aren't able to succeed when working remotely from a sales manager who doesn't manage her salespeople very closely.
I reviewed OMG's data on a random set of 4,500 recent sales candidate assessments and only 12% were suitable for working remotely. BUT…upon closer look, 12% was not representative of the findings for any one company!
Of the companies that required both a remote seller and had enough candidates to make up an appropriate sample size, the distribution of candidates suitable for working remotely ranged from 2% to 75%. I thought that was rather strange and looked again, but with different filters. I found that the variations in suitability had more to do with the company, and the difficulty level of the role, than anything else. When the role was more difficult and their job postings reflected that difficulty, stronger candidates applied and were assessed. When the role was less difficult and the job postings reflected it, all kinds of qualified and unqualified candidates applied and the assessments reflected that change in candidate quality. For example, look at these 5 companies, their percentage of suitable candidates, and the difficulty level of the role:
CompanyDifficulty LevelSuitable for Remote
A Considerable 75%
B Considerable 67%
C Some 50%
D Moderate 25%
E Moderate 2% If you throw out company E, the average is 60% suitable, but we also lose 75% of the candidates in the sample, so you can’t do that…
When the role is not very difficult, the company will attract lower level salespeople, and they will be much less likely to be suitable for working remotely than their much stronger peers.
When you look at all 10 of my examples, you should be able to recognize why it is so important to use a sales-specific candidate assessment that is customized to your company's requirements, determines whether candidates possess the required selling skills, digs into the Sales DNA to determine whether candidates will succeed in your business, and in this role, and makes an accurate, predictive recommendation.