Formulate your own Sales Hiring Formula

Why do we need a sales hiring formula? Because topline/revenue/sales numbers are the only things that matter in an organization at the end of the day. No matter how good the marketing, operations, finance, and HR functions are, the organization would be bankrupt if the salespeople are not continuously bringing in the dollars. In the current scenario requiring continuous growth against competitors, innovation doesn’t give a lot of breathing room, making this book “Sales Acceleration Formula” a must-read for everyone involved in sales, be it a new salesperson, a veteran salesperson, or the top management.

Mark Roberge has broken down the sales acceleration formula into

Sales Hiring Formula

Sales Training Formula

Sales Management Formula

Demand Generation Formula, and

Technology & Experimentation.

List down the ideal characteristics of a salesperson. For Mark, they were: Preparation, Adaptability, Domain Experience, Intelligence, Passion, Prior Success, Brevity, Rapport Building, Voice Quality, Technical Apptitute, Objection Handling, Convincing, Needs Identification, and Closing Ability

Evaluate your top salespeople on the basis of these characteristics and give a numerical score (1-10)

Run a multiple regression analysis correlating the ‘characteristics score’ with the sales numbers of these top salespeople. This would give you an idea of what characteristics you should be looking for in your new hires.

Correlation between Characteristics and Sales Success Sales Hiring Formula

Reiterating the above process on a continuous basis would improve the accuracy of the sales hiring formula. If the 3rd point sounds too technical, it’s not. There is no need to hire a data scientist for this analysis. It can be done in Excel.

Create your dataset with columns: Salesperson, Sales Number, Characteristic 1/2/3/..

Install the free Analysis ToolPak add-on in Excel, populate your dataset, run multiple regression in Excel to see if the characteristics have any impact on the sales numbers

Check the R-squared value of the dataset to see if the regression line is a good fit (anything greater than 0.80 is a good value)

Check the p-value of the dataset to check if your hypothesis is correct

Check the p-value of individual characteristics to eliminate the characteristics which do not follow your hypothesis

Select the characteristics that do impact the sales numbers and then run a correlation analysis to check the correlation (whether it’s positive or negative). Eliminate the characteristics which give a negative correlation.

You can also do the same via Python or R. If you need my assistance, just drop a message here.

This data analysis would lessen the subjectivity in hiring. Once you have the characteristics that you are looking for, ensure that your recruitment screening process is in line with the requisite characteristics. If the sales hiring formula is not followed during searching for the candidate then the previous exercise would go to waste. During the hiring process, score the candidate against these characteristics and see if the candidate is a good fit to be a top salesperson for your organization according to your sales hiring formula.

Hubspot Sales Candidate Assessment | Sales Hiring Formula

For Mark Roberge, the five key traits that correlated to sales success at Hubspot were:

Coachability: Roleplay a sales call, give pointers to the candidate, and then do a second roleplay. It would give an idea if the new hire is open to being coached

Curiosity: Gauge the questions asked by the interviewee. Also, check if the interviewee is listening actively.

Prior Success: This can be academic or extra-curricular

Intelligence: Provide the candidate with a piece of new information and observe the candidate’s ability to absorb the information and use the same information in the later stages of the hiring process

Work Ethics: Observations during the interview, reference checks, and behavioral questions can give an idea of the work ethics

Note: If Zillow cannot value a house correctly after 3 years and millions of dollars on AI/ML tools, maybe some subjectivity in making life-altering decisions is a good thing.

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Categories: Product Management

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