UPDATED – UBER Classifies Their Drivers: The Secret to Why You’re Not Earning As Much As You’d Like and How to Change How UBER Tiers Your Earning Potential (With Data)

Most employers love to maximize employee output. This utopia is usually achieved by dangling carrots in the rabbit’s face. Bonuses, relocation allowances, year’s end performance based benefits, sign-on bonuses, are some of the types of employee incentives. That is, if one works for a garden variety corporation. What happens when you work for a non-conventional entity who can track your every move? Enter Uber and rideshare corporations.

UBER Knows Quite a Bit About You, The Driver

The dependence on the number of drivers on the road forces the rideshare companies into the behavioral sciences and the field of mathematics. The employer must maintain an adequate number of drivers, for a certain amount of hours, 7 days a week, 24 hours a day to satisfy supply and demand, while the drivers and riders are functioning harmoniously. It is monumental, but manageable, if a few algorithmic criteria are set to document driver performance. Algorithms can be set with thresholds, along with programming, (if then loop statements,) which regulate drivers. Add GPS to the equation so location and algorithms work together, hence a transparent employee. This transparency allows for patterns to emerge, hours worked, location, habitual starting and ending locales, preferences, usage of the app and many other schemes.

Data, Algorithms and Programming Determine a Very Accurate Depiction of Your True Value As a Driver For the Company

The algorithms can reveal output per hour, reveal short and long term comparisons, deregulate and reschedule if the thresholds are not met, slow down and speed up production and at times reward or punish. Since criteria for algorithms are in the hands of programmers and engineers (under the instruction of business owners) the narrative they must include is for peak performance and output for the benefit of the company. And that is what anyone would do to maximize profit, the definite name in the game. It is not manipulative or malicious, it is right. If you did not like math, you may like it now. Uber utilizes math, algorithms, programming, GPS, and metadata to gauge drivers. The measurements and results from satisfying algorithmic thresholds, offers metadata that deems the driver reliable. The reliability may be understood as number of rides per shift, hours worked, long rides taken, cancelation rates, acceptance of pool rides, days of the week preferred, locations where rides were not taken. Every action and reaction of the driver translates into rate of production and propensity of future output. Therefore, the math determines almost everything.

How to Get Awarded Better Rides and Better Earnings As a Result

The new Uber classification of drivers is yet another algorithm that will throw consistent loads of work your way. If an employer knows that a certain employee is capable of X, that employee will get X, as long as the algorithmic criteria of X is satisfied. Simultaneously, the riders who will see that employee X is a diamond is less likely to rate otherwise. Suggestion goes a long way, and therefore, employee X is more likely to stay under X status and consistently perform for maximum output.

UBER Utilizes Organizational Psychology to Separate the ‘Top-Performers’ from the ‘Under-Performers’

Not only math but psychology joins into this equation. Drivers are psychological subjects of organizational psychologists. Under many theories our performance as drivers depends on our met needs and the amounts of rewards we received for completing tasks. Skinner’s theory called operant reinforcement, on reward for certain actions and punishment for the incorrect actions, can reward the drivers who will perform according to company standards and expectation and weed out those who are under-performers. Maslow’s theory includes the need of esteem via accomplishments. Vroom’s expectancy theory claims that we choose behaviors with an expected result. And yet Locke’s theory confirms that when we goal set we are motivated to behave in a way to achieve the goal in mind. Summing up, rewards for driving early in the morning and evening rush hour, completing quests and driving when the rates are higher makes “Jack not a dull boy.”

I Spent 5 Weeks Putting This Hypothetical Structure to the Test. Here Are the Results:

As a driver, and after the realization of so many bar settings, I experimented to prove or disprove the hypothetical structure. I tried to do this by throwing inconsistent data to the system. Some days drove odd hours, when it is not busy, some weeks I did exactly what is expected, I took three days off in a row, worked over 40 hours and the following week worked 20 hours. Off, on, rush hour, non-rush hour, late night, no late night, events, no events, cold days, nice days and mixed it up as much as I could.

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Breakdown of Driving Shifts

Week 1

Week 2

Week 3

Week 4

Week 5

*DND = Did Not DriveThe conclusion is irrefutable. Logarithms, math, metadata and psychological theories work. Whether you are bitter about being a rat on a wheel collecting food pellets, or content that you are responding normally to math and psychological theories, understand you are in good company.

Do you consider yourself a casual or seasoned driver? Have you too noticed the patterns and designations UBER applies based on your driving habits? Does it benefit or hinder you? Do you agree with this ‘hypothetical structure’?

Share your thoughts and comment below!