DoorDash: Timing, pay, and tip transparency

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A long-time reader pointed me towards an article in the New York Times the other day which included a lot of interesting details about app-based delivery companies, including the answer to a question a number of people have asked me.

“Active time” versus “Dash time”

DoorDash records two different measures for how much time you are working on the app. Your “active time” refers to all the time you are picking up or delivering an order, while your “dash time” refers to all the time you are logged into the app and eligible to receive and accept orders.

I assume this function grew out of the battle in California over the treatment of app-based delivery workers, since the apps fought to ensure that only “active time” would count towards eligibility for certain benefits.

To DoorDash’s credit, this information isn’t buried in the app like some other functions, so it’s possible to easily calculate your hourly wage based on both metrics. Here are the results from the six full weeks I’ve been working for DoorDash:

As you can see, the wage you earn on DoorDash depends critically on which time metric you use. According to the Times article, “Uber says its drivers average $30 an hour. The delivery service DoorDash says its drivers make at least $25,” and that lines up with my experience, if you use the metric of active time. If you use dash time, my hourly rate fell to $9.16 per hour.

You can take this with a grain of salt if you like: sometimes I don’t end or pause my dash time when I’m running personal errands or on the phone, but broadly speaking that’s true of any worker juggling the job they’re paid to do and the demands of living, so let’s ignore the details and focus on the broader point: to maximize your earnings from DoorDash, you have to make active time match up with dash time to the maximum extent possible, since you’re only paid for active time, not for dash time.

Busy periods and bonus pay

As the Times article helpfully explains, “Drivers want fewer drivers on the road, giving them less competition for the best-paying orders and more bonuses. But the platforms want the opposite — slightly more drivers than orders, to keep the service reliable and consistent.”

Understanding that tension helps explain one of the most confusing features of working for DoorDash: ignore bonus pay.

As a reminder, bonus pay is one of the 3 components of DoorDash pay, along with base pay and customer tips. Importantly, it’s the only component of pay you know before you even receive an order, because it applies to all the orders received in a specific DoorDash zone during a specific period of time. As you’d expect, DoorDash offers bonus pay to encourage workers to start delivering.

You also might expect that periods of bonus pay are by definition the most lucrative: the same order that might pay $5.75 ($2.75 base pay plus $3 customer tip) during a normal period will pay $8.75 during a $3 bonus period. But as the quote above explains, this is wrong, because DoorDash is always trying to encourage too many people to work at any time.

Why does this matter? Remember that to maximize your earnings, you need to match your active time to your dash time as closely as possible. To the extent that bonus pay successfully attracts more people to start working, it has no effect on that match between active time and dash time, and therefore does not help maximize earnings.

And indeed, this lines up with my experience exactly. The very first time I logged in to work, I headed over to a zone with a $3 bonus pay, on the assumption that there must be an unlimited number of orders to deliver in that zone given the high bonus. But in fact, I waited 30 or 40 minutes for my first order, precisely because the $3 bonus pay had succeeded in attracting too many workers to the area.

To maximize earnings, focus on times you know it’s busy

To give another example of how this works, DoorDash frequently offers some of its highest bonus pay at confusing times of day or, especially, night. For example, there will be no bonus pay from 11:30 am to 1:30 pm, but $3 in bonus pay from 1:30 am to 3:30 am. This seemed bizarre to me at first: surely there are more people ordering delivery during lunch than there are people with the munchies at 2 in the morning?

And it’s true, there are. But there are also already more than enough workers available at lunch, while DoorDash offers bonus pay to secure that cushion of unutilized workers at midnight.

This means to maximize your earnings — to match your active time to your dash time as closely as possible — you want to identify periods when there are enough people working, but only just enough. If there are too few workers, then DoorDash will crank up the bonus pay to attract excess workers and your active time will correspondingly drop.

But remember, since DoorDash guarantees bonus pay for a set period of time, they also don’t use it any more aggressively than they have to, so in periods where there’s a good match between orders and workers, there will often be no bonus pay, even though those are the periods where your earnings are highest since you’ll have the closest match between active time and dash time.

The mystery of the magical tips

A final interesting detail the Times explains is why the final tip amount on an order is sometimes higher than the amount shown when you accept the order. I had assumed this was another one of DoorDash’s psychological tricks, providing intermittent positive reinforcement to encourage people to continue working as long as possible, generating the surplus labor that keeps the circus going.

It turns out, it’s even more nefarious than that. From the Times:

“DoorDash does not show full tip amounts for ‘orders that contain larger tips’ in advance ‘to ensure all Dashers have an equal chance at receiving high-value orders,’ said Rachel Bradford, a spokeswoman. When drivers take only orders with big tips, it ‘harms the experience’ for customers and merchants, she continued.”

Remember, customer tips in DoorDash work as a kind of crude auction mechanism, whereby customers can bid their way to the front of the delivery queue by offering higher tips, increasing the likelihood that their order will be accepted and they’ll get their food faster. What Rachel is saying is that while that mechanism is deliberate, complete tip transparency would allow it to work “too well:” orders with low or no tips would go unfulfilled or suffer long delays, leading both to customer dissatisfaction with cold and late food, and a build-up of orders waiting at merchants who start preparing food when the order is placed, not when a worker actually accepts it.

Tip opacity is therefore a way of constraining the market: customers are allowed to tip as much or as little as they like, but workers will only every see $3 or $4 of the tip, so they can’t hold out for orders with large tips. This means that while I think you should not tip at all, or tip in cash after you receive your order, if you are going to play the tipping auction in order to speed up your delivery, there is no reason to tip more than $3 or $4 in the app. Workers won’t see it and it won’t help you get your food any faster.

DoorDash: Tips (1)

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I recently received a question on Twitter about what percentage DoorDash customers typically add to their order as tips, which is the third component of each order’s pay (after base pay and peak pay), and is mostly (but not entirely) invisible to workers until after an order has been completed.

This is a question that’s literally impossible to answer on a retrospective basis, for two complicated reasons, so I designed a brief experiment to calculate it in real time, and am happy to share the results today.

Methodology

There are at least 4 different “Types” of DoorDash orders, which I’ll explain in more depth in a future post, but can briefly summarize here:

  1. Orders originating with DoorDash. These are “classic” DoorDash orders where you place your order in the DoorDash app, and DoorDash has completely visibility of both menu items and prices.

  2. Orders originating with the merchant. I believe these orders are created when you order delivery through a merchant like McDonalds or Sweetgreen and they foist the order off on DoorDash (or one of their other delivery partners). Here, DoorDash does not see or share the contents of the order or the price customers pay the merchant.

  3. Red Card orders. These are orders where the worker uses a special debit card to physically pay for an order in-store. The worker sees the items in the order, but uses the Red Card to pay whatever retail price the store charges, and does not see how much the customer is charged for them.

  4. Cash on delivery orders. I currently have this feature disabled, but my understanding is it allows workers to pay for orders with their own money at the merchant, then be reimbursed by the customer in cash when the order is delivered.

For the purposes of my study, I used exclusively the “classic” order Type 1, for the simple reason that this is the only order Type which allows you to see, in advance, the contents of an order and the price paid by the customer. Over the course of a few days, I set out to track 10 Type 1 orders, the retail price of the contents, and the final tip left by the customer. I ultimately forgot to sign off and accepted an 11th order, so I decided to include it as well, although you can rerun the calculation excluding that tip ($3 on a $30.56 McDonalds order).

One reason this is frustrating, and why I limited the number of orders I’d study, is that the contents and price information are only visible for the order you are currently delivering, only while you are delivering it, and are buried several menus deep, making it impractical to do this at any scale (at least on a volunteer basis!).

During the study period (Friday, September 30 and Saturday, October 1, 2022) I accepted every order I was offered (which included several Type 2 orders excluded from the study) in order to prevent the sample being biased towards higher-paying, higher-tipped orders.

Data

With that out of the way, here are the results of those 11 Type 1 orders:

As you can see, a majority (6) of my Type 1 customers tipped between $2 and $3.50, with tips of $2, $3, and $5 being equally represented in the sample.

Of the 9 customers who tipped, the average tip was $3.56, and the average tip rate was 13.3%.

Interestingly, there is no correlation between the price of an order and the tip rate, which is strongly suggestive that most customers are not following a customary rule based on order value, but instead tip a fixed amount on every order regardless of the order’s contents. Customers’ tipping decisions may be based on other factors, like ability to pay (customers who can charge tips to their employer or receive monthly credit card statement credits for DoorDash orders may have a higher propensity to tip), perceptions of cost or inconvenience incurred by workers (customers in less convenient and more difficult to access homes and offices may feel an obligation to compensate workers at a higher rate), or a perception that tipping is either pro-social or anti-social behavior. Additionally, if orders placed without tips are regularly refused or delayed, there may be an additional feedback mechanism encouraging workers to gradually increase the amount of the tip they add until their food begins to be delivered more promptly or by more skilled or experienced workers.

Conclusion

I hope this was an interesting, albeit limited, snapshot of the tipping behavior of DoorDash customers in my area. I’m happy to run more experiments like this in the future, so as always feel free to leave any questions, comments, or suggestions for future studies in the comments.