Ranking Companies on Sales Culture & Retention

A company’s sales retention rate is a very important indicator of business health. If you have a good gauge on this, you could better answer questions such as: should I join that company’s sales department, will I be able to progress up the ladder, are reps hitting their numbers, are they providing effective training, should I invest money in this business, etc. But how does one measure this rate especially from an outside vantage point? This is where LinkedIn comes to the rescue. I essentially cross applied the approach I took to measuring engineering retention to sales.

sales_ret_2

This chart reveals several key technology companies ranked in reverse order of sales churn – so higher on the chart (or longer the bar) the higher the churn (so from worst at the top to best at the bottom).

So how are we defining sales churn here? I calculated the measurement as follows: I took the number of people who have ever churned in a sales role from the company and divide that by the number of days since incorporation for that respective company (call this Churn Per Day), and then I compute the ratio of how many sales people will churn in one year (the run rate i.e. Churn Per Day * 365) over the number of current sales people employed.

For ex. if you look at the top row, which is Zenefits, the value is 0.40 – which means that 40% of the current sales team size will churn in a one year period. In order to maintain that sales team size and corresponding revenue, the company will need to hire 40% of their team – and sooner than in a year as that churn likely spreads throughout the year as well as given new sales hire ramping periods (if you’re churning a ramped rep and say it takes one quarter to ramp a new sales rep, then you need to hire a new head at least one quarter beforehand to avoid a revenue dip).

A few more notes:

The color saturation indicates Churn Per Day – the darker the color, the higher the Churn Per Day.

Caveats listed in the previous post on engineering retention apply to this analysis too.

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Filed under Data Mining, Economics, Enterprise, Entrepreneurship, Job Stuff, LinkedIn, Non-Technical-Read, Startups, Statistics, Trends, Venture Capital

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