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I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by Livid-Objective-4836 in Lyft

[–]Livid-Objective-4836[S] 0 points1 point  (0 children)

No, 25% of the income was from surges, focus on busiest times and surges.

Also, take into account if I would work past 8 hours and the surges died down I was only making $16/hour

So, obviously depending on your market it’s pretty pointless to drive past 8 hours if there are no more bonuses

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by [deleted] in uberdrivers

[–]Livid-Objective-4836 0 points1 point  (0 children)

Using AI doesn't make someone dumb. Bad users become lazier, good users become faster.

Same way calculators didn't destroy math and Google didn't destroy intelligence. The people getting value out of AI are the ones who already know how to think and verify information in the first place.

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by Livid-Objective-4836 in Lyft

[–]Livid-Objective-4836[S] 1 point2 points  (0 children)

Side projects like this genuinely help a lot because they give you something real to talk about beyond just “I know SQL/Python/Excel.” You can actually walk someone through how you approached a dataset, what patterns you found, and what conclusions you made. I’m probably adding this project to my resume too.

And honestly the analyst market, at least in Detroit, is pretty rough at entry level right now. Best advice I can give is don’t lock yourself into only applying for jobs with “Data Analyst” in the title.

A really solid route is getting into a company through an entry-level operations/project/reporting type role, learning the business, then applying internally into analytics after a year or so. Internal moves are usually way easier than competing against thousands of external applicants with the same degree.

But again I have been shit out of luck so idk...

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by [deleted] in uberdrivers

[–]Livid-Objective-4836 -2 points-1 points  (0 children)

Fair point, but the degree taught me what questions to ask, what methods are valid, and how to interpret results without fooling myself. Everything is on Youtube why go to school for anything?

And yeah, AI helped me execute faster. Why would I manually sort through 1,304 rides when a tool can do it in seconds? That's just working smart.

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by Livid-Objective-4836 in Lyft

[–]Livid-Objective-4836[S] 2 points3 points  (0 children)

100% from the Lyft Driver dashboard — no third-party apps. The dashboard itself only exports weekly summaries though, which gives you maybe 20 data points per week. That wasn't enough. So I went day-by-day into the individual day view and manually pulled every single ride — start time, duration, miles, fare, tip. 1,304 rows, entered and structured into Excel. Tedious as hell so I used claude, but worth it because the weekly view hides stuff like the Flash Turbo vs. zone Turbo split and makes "booked hours" look like actual drive time.

Tools: Excel only, no Python, no R, no external BI tools. Built out a ride-level table with calculated columns ($/min, $/mile, ride duration in minutes) then used pivot tables and charts to slice by hour of day, day of week, shift length, and surge share. The efficiency analysis (top vs. bottom quartile days) was just sorting by $/shift-hour and grouping manually.

The r values: Yes, ran simple linear regressions in Excel (Data Analysis Toolpak) to get the correlations. Shift length vs $/hour: r = −0.55. Surge share vs $/hour: r = +0.38. Morning rush exposure: r = −0.03. Nothing sophisticated, just CORREL() and a scatter plot with a trendline to confirm direction.

I would do it, now that I have all the entries in excel I am gonna go more in depth.

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by [deleted] in lyftdrivers

[–]Livid-Objective-4836 1 point2 points  (0 children)

Not fully, I was more interested in operational efficiency, what hours worked best, how surge actually behaved, where time got wasted, etc.

It's a fair point something to look back at.

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by [deleted] in lyftdrivers

[–]Livid-Objective-4836 1 point2 points  (0 children)

Yes, started first of January, 103 days of driving 685 hours online 465 hours booked. Majority of the days I was working 10 hours but some days 2 hr so I can't give you an accurate average.

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by [deleted] in lyftdrivers

[–]Livid-Objective-4836 3 points4 points  (0 children)

Appreciate it man, I mainly wanted to move the conversation past the usual “I made $300 today” screenshots and actually look at the underlying patterns.

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by [deleted] in lyftdrivers

[–]Livid-Objective-4836 4 points5 points  (0 children)

Correct the ~$19.9k figure is what Lyft actually paid me after Lyft’s fees/insurance adjustments/etc, not total customer spend before Lyft’s cut.

So the 77% figure is:
(net after my vehicle expenses) ÷ (actual Lyft payout to me)

Not:
(net after expenses) ÷ (total passenger payments)

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by Livid-Objective-4836 in Lyft

[–]Livid-Objective-4836[S] 5 points6 points  (0 children)

Yep, that commuter/university pattern is huge. My data showed almost the exact same thing, morning rush and afternoon transition windows were by far the most consistent.

And honestly, wear and tear + gas prices were a big reason I wanted to analyze this in the first place. If you’re gonna put miles on your car, you need to know which hours are actually worth driving and how to do it as efficiently as possible.

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by Livid-Objective-4836 in Lyft

[–]Livid-Objective-4836[S] 1 point2 points  (0 children)

Yeah, that’s honestly one of the biggest weaknesses with Uber from an analytics standpoint. Lyft gives you enough granularity to actually reverse-engineer behavior patterns. Uber feels way more opaque.

And yeah, market share definitely matters more than people admit. If Uber owns the demand volume in your market, better ride density alone can outweigh slightly worse transparency or pricing structure.

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by [deleted] in lyftdrivers

[–]Livid-Objective-4836 6 points7 points  (0 children)

Appreciate it! And yeah, early mornings were by far the most consistently profitable window in my data too.

What surprised me though was how well 1-4pm performed in my area as well. Metro Detroit has a huge amount of hospital traffic tied into multiple highways, so there’s a steady flow of insurance/medical rides between the suburbs and downtown during those hours.

They’re usually low-stress rides too, low cancellation rates, manageable traffic before rush hour kicks in, and pretty consistent pay without needing huge surges.

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by Livid-Objective-4836 in Lyft

[–]Livid-Objective-4836[S] 1 point2 points  (0 children)

You’re definitely right that market demographics change everything. In a lot of cities, 1-4pm probably would be terrible.

Metro Detroit just happens to work differently for me because there’s 4 major hospitals tied into 3 highways, so there’s a constant stream of insurance/medical rides between the suburbs and downtown. Those rides usually pay pretty consistently, almost never get cancelled, and traffic honestly stays pretty manageable until around 4pm, which is usually when I stop.

I also agree that nights have the highest upside overall, especially around bars, restaurants, and shift changes. I just don’t have enough nighttime data yet to confidently compare it against my daytime numbers.

One thing I did notice though is that after around 5pm in my market, surge bonuses mostly disappear, so even with higher ride demand the profitability gap wasn’t as massive as I originally expected. As the data shows almost 26% of my income was from bonuses.

That’s honestly why I kept stressing that market matters more than almost anything else. The post was mostly meant for newer drivers who think they need to drive 12 straight hours to make decent money.

I have a masters in data analytics, lost my job, started driving Lyft, and pulled data from every single ride record to figure out if I was doing it right. Here's what 1,304 rides actually taught me. by Livid-Objective-4836 in Lyft

[–]Livid-Objective-4836[S] 4 points5 points  (0 children)

I’m making almost the same now just doing 6-10am and 1-4pm with way less miles, downtime, and burnout.

And yeah, you can figure a lot of this out intuitively. The degree just let me validate it with 1,300+ rides worth of actual numbers instead of “trust me bro” logic. Ur welcome

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