Uber and Lyft drivers say they're earning less through the apps. That's led some to start their own ride businesses to make more money.

Uber, Lyft Drivers Are Starting Their Own Ride Businesses As Pay Drops - Business Insider

Lyft Inc. recently agreed to pay more than $2.1 million to settle a lawsuit filed by the federal government.

Examining Lyft’s $2M FTC Fine - Lessons for Finance Pros

A rideshare co-op is launching services in Minneapolis in the coming weeks.

As Uber and Lyft threaten Minneapolis exit, driver-owned rideshare cooperative set to enter - CBS Minnesota

The multibillion-dollar rideshare companies have mobilized go-to allies to make their case to policymakers, but their lobbying spending is relatively modest.

Here's how Uber and Lyft are trying to get their way in Minnesota

Thousands of Uber, <strong>Lyft</strong> and Doordash drivers are striking Wednesday, in what organizers say is the largest nationwide work stoppages against gig economy giants.

Uber, Lyft drivers to strike on Valentine’s day for higher pay - The Washington Post

Payfare has been leveraging i2c's payment platform to facilitate instant, zero-cost access to earnings for gig workers through its leading neobanking products including Dasher Direct and Lyft Direct.

i2c Expands Partnership with Payfare to Provide Faster Payouts for Gig Workers in Canada and the UK

RAFAH, Gaza Strip (AP) — Palestinians reported another widespread outage of internet and phone service in Gaza early Wednesday, hours after Israeli airstrikes leveled apartment buildings near Gaza City and as ground troops battled Hamas militants inside ...

Gaza's phone and internet connections are cut off again, as Israeli troops battle Hamas militants

With Uber (NYSE:UBER) and Lyft (NASDAQ:LYFT) reporting quarterly earnings on Nov. 7 and Nov. 8, UBS is bullish on the results from both ride-share companies. However, the firm also said it sees lower multiples for both  companies "given broader macro pressure across the internet sector.

Uber, Lyft seen having strong Q3 results, though with lower multiples - UBS

Uber and Lyft are projected to report strong Q3 results in early November, though with lower multiples, according to UBS. Read more here.

Once the Lyft driver reached the destination, Pandey got out of the vehicle and started walking toward the passenger side to get Tux. "Before I could open the door, the driver started driving ...

Cat reunited with owner after Lyft driver rushes off before feline could be retrieved

Lyft

lyft

Photo by on Paul Hanaoka Unsplash For the first time in five years of driving for Uber and Lyft in Minneapolis, Tom LaDuke said he’s been thinking twice before heading out. Ride-hail drivers have in a surge of carjackings throughout the city over the past couple of months. increasingly become targets Officer Garrett Parten of the Minneapolis Police Department told The Markup that the department had tracked nearly 50 carjackings of Uber and Lyft drivers since mid-August. Twelve such incidents happened in the third week of October alone. LaDuke said these numbers are unsettling. “Over the last three weeks I have not driven as much as I did before,” he said. “I’m concerned about my safety.” The incidents tend to follow a pattern, Parten said. Typically, an individual uses a stolen phone to hail a ride through Uber’s or Lyft’s app. When the driver arrives, someone meets the car and asks the driver to wait a few minutes. Shortly after, a group of “armed individuals” appears, surrounds the vehicle, and demands the driver get out. Along with the car, the group also usually takes the driver’s wallet and phone and gets the phone’s security code to . access the driver’s various accounts to transfer out money “Some drivers have been assaulted, including being pistol whipped,” read a issued by the Minneapolis Police Department on Oct. 22. “Ride-Share operators need to take extra precautions to stay safe.” citywide alert Both Uber and Lyft said they’re working to improve safety on their apps. “These reports are concerning,” said Uber spokesperson Carly DeBeikes. “Safety is a top priority for us and we are constantly working to use technology to enhance safety on the app for riders and drivers.” Lyft spokesperson Ashley Adams said, “While these horrific crimes are by no means unique to rideshare, the safety of our community is fundamental to Lyft…. We continuously invest in new features and policies to help protect drivers.” Adams and DeBeikes said the companies were working closely with the Minneapolis Police Department on the issue. But Parten said the police department had reached out to Uber and Lyft, after noting the carjacking trend, to get the companies to provide drivers with safety tips, and “they just weren’t very responsive.” The carjackings of Uber and Lyft drivers in Minneapolis fit into a . Police departments from to to have said they’ve seen an uptick of all types of carjackings during the pandemic. larger nationwide trend Chicago Cleveland Washington, D.C., In Minneapolis, carjackings and attempted carjackings increased by 339 percent over the last two years, going from 104 in 2019 to 457 so far in 2021, according to the police department. There were 401 reported carjackings in 2020. And Uber and Lyft drivers are frequent targets all over the country. In July, The Markup used police reports, local news articles, and driver interviews to . identify 124 cases of ride-hail drivers being attacked Eleven of those drivers were killed during the assaults, including Ahmed Badal, a 35-year-old Somali immigrant who was . Lyft’s Adams said the company has “attempted to contact Ahmed’s family to offer our support.” shot in April while driving for Lyft in Minneapolis Over the past few months, The Markup has identified dozens more cases of Uber and Lyft drivers being carjacked in cities including , , and . At least three more drivers have been killed. Atlanta Chicago Kansas City These numbers are far from comprehensive. Uber and Lyft have repeatedly declined to provide The Markup data on the number of carjackings involving their drivers, and most police departments don’t compile stats on carjackings that happen specifically to ride-hail drivers. Not all drivers report the incidents either. The Minneapolis Police Department’s compilation of roughly 50 carjackings since August is rare concrete evidence of the high rate at which ride-hail drivers have been targeted. Uber and Lyft treat their drivers as independent contractors rather than employees, so when carjackings occur, drivers say they’re usually . They don’t have benefits like workers’ compensation or sick leave, and losing their cars can also mean losing their livelihood. left to fend for themselves “As soon as a carjacking is reported to us, we immediately reach out to the driver to offer our support,” Lyft’s Adams said. Several drivers, however, told The Markup that after carjackings . they’ve been offered little to no help The majority of these reported carjackings happened through Uber’s and Lyft’s apps. Drivers don’t have much say in whom they pick up and where they go, and the apps make it fairly simple for passengers to to hail rides. create fake accounts with fake names In most cases, rider identification isn’t required, and things like email addresses and phone numbers can be easily spoofed. In April, Uber began requiring a valid ID in the instances when a passenger creates a new account using an untraceable payment method like a prepaid card or gift card. Lyft followed Uber’s lead in June, but only for a handful of cities, including Chicago and Atlanta. Lyft’s Adams said the company added the requirement for Minneapolis in August. Christopher Herrmann, an assistant professor at John Jay College of Criminal Justice and former crime analyst supervisor with the New York City Police Department, sees these carjackings of Uber and Lyft drivers as crimes of opportunity. “You can control the situation quite a bit,” Herrmann said, speaking from the perspective of would-be carjackers. “You’re guaranteed a nicer car. You can control the location where you get picked up. You can control what time of day it is. It’s like dial-a-carjack.” Drivers Say They’re on Their Own LaDuke first learned about Uber and Lyft drivers being carjacked in Minneapolis from a popular Twitter account called , created by volunteers who listen to police scanner audio feeds. Through those feeds, CrimeWatchMpls heard and tweeted about more than a dozen police dispatch reports of ride-hail drivers being carjacked in October. CrimeWatchMpls “Robbery of person at gunpoint. Uber driver. Loss: Vehicle, wallet phone, red Camry,” read on Oct. 4. one Twitter post Another, two weeks later, , “6:15 a.m., Lyft driver carjacked at gunpoint…. This is officially a crisis for ride share drivers in #Mpls.” read That same day, the people who run CrimeWatchMpls, who declined to comment for this story, wrote a blog post about the . “In just the last seven days (since Oct. 14), we’ve reported ten such incidents that have been aired on police dispatch. And these are just the ones we heard or know about,” read the blog post. rash of carjackings of Uber and Lyft drivers Two days later, the Minneapolis Police Department issued its citywide alert to ride-hail drivers. At that point, LaDuke said, neither Uber nor Lyft had warned Minneapolis drivers about the carjackings. On Oct. 28, nearly a week after the police alert, Uber sent drivers there a message titled “Safety tips for handling carjackings.” “Minneapolis police have reported a rise in robberies, car thefts and carjackings over the past several months,” read the message, which LaDuke shared with The Markup. The alert advises drivers to give up their vehicle and call 911 in the event of such an incident. LaDuke said he has not received any notices about the Minneapolis carjackings from Lyft. “The fact that the Minneapolis police would release this statement specific to Uber and Lyft drivers before Uber and Lyft release information had me concerned,” LaDuke said. “As a driver, we have to periodically take a selfie to show we are who we say we are, we have to have a background check. This is to keep riders safe, which is fine. But what are they doing for the drivers?” Lyft’s Adams said the company “recently sent drivers in Minneapolis emails and in-app messages highlighting safety features available to drivers and tips about staying safe on the road.” She didn’t respond to questions about whether Lyft had warned Minneapolis drivers of the carjackings specifically. Unofficial Facebook and Reddit groups where drivers in Minneapolis go to share information are filled with safety concerns. One driver in a private Facebook group said he has invested in safety gear: “1. Bullet Vest 2. Two phones 3. Dummy wallet 4. Two dash cams,” he wrote. Others say they’ve started carrying weapons, like guns and bear mace, which is prohibited by Uber and Lyft. Another driver on wrote, “I won’t pickup in North Mpls, where the majority have occurred.” Reddit According to the Minneapolis Police Department, most of the carjackings of Uber and Lyft drivers have happened in North Minneapolis, with some also occurring in Northeast and South Minneapolis. LaDuke said when he gets pinged for rides in these neighborhoods, he’s hyper-aware of his surroundings and makes sure not to stop in isolated areas or places where he could get boxed in. “It’s like I’m afraid to pick people up in that area, which is sad for me,” LaDuke said. “I grew up in North Minneapolis.” Herrmann, the assistant professor at John Jay, said most people think of their cars as a secure place and an extension of their home, so when carjackings happen, it can feel especially violating. “If someone is pointing a gun at your face and yelling at you to get out of your car, that’s a really traumatic event,” Herrmann said. “The mental scar that that leaves—every time I get in my car I think, ‘Am I going to get dragged out of my car again?’ ” Herrmann said that after carjackings a lot of people tend to change their habits, like driving less and avoiding certain areas and times of day. “The problem with gig workers is, that’s the way they make their living,” he said. “They don’t have the option or the luxury to say, ‘I’m not going to work anymore.’ ” Written by: Dara Kerr Originally published here

Many Minneapolis Uber and Lyft Drivers Were Victims of Reported Carjackings Last Fall 

Challenge: Lyft realized its was hindering its ability to deliver a consistently positive experience to its users. mobile crash reporting tool Unreliable crash reports and inefficient debugging workflows caused bugs to slip through, despite their team’s best efforts to thoroughly test and debug prior to releasing new versions of their apps. In the end, they decided to move away from Crashlytics. Results: Lyft has now standardized on Bugsnag for crash reporting across all mobile applications — iOS and Android. Bugsnag is used throughout development, testing, and post-release, allowing Lyft engineers to reliably find and resolve crashes before they can impact their users. They have even decreased the time spent troubleshooting errors by 30%. Crash Reports In a highly competitive industry, Lyft needed a trustworthy crash reporting tool to ensure the quality of application releases. Lyft is a consumer application company that facilitates peer-to-peer ridesharing, with a focus on fostering community for the drivers and passengers on their platform. For a consumer mobile application in a highly competitive industry, reliability and precision in engineering can make or break the customer experience. For this reason, the main priority for Lyft’s engineering team is reliability with the goal of “making sure new versions of our apps are fully stable and bug free so our users have a positive experience,” says Billy Pham, Technical Program Manager for Passenger Applications. However, their team lacked trust in their previous tool because, despite their well built-out processes for testing Alpha and Beta versions of their applications prior to release, bugs would still surface in major releases. They realized that missing data and error reports were causing issues in production. “We would not catch every single event, and so crashes and bugs were slipping through, and of course, this caused a lot of pain for our users. It wasn’t reliable,” says Billy. Lyft’s engineers also had difficulty assessing the impact of individual errors because there was a lack of visibility into how frequently certain errors were occurring. This was especially problematic given their fast release cycles of one week. They needed a way to understand error impact to help with decision making around release blockers. These issues, coupled with a plan to triple the size of their engineering team, led them to search for a more reliable tool that would support their hyper-growth. Observability and Prioritization for High-Quality Releases Lyft has now implemented Bugsnag across all stages of the mobile development cycle which helps them detect and fix bugs before they impact their users. Since they can deliver on their main priority of reliability, they have even made gains in productivity, shipping new releases faster than before. “Bugsnag is a great way of logging crashes reliably, efficiently assigning those issues to your teams, and resolving them in a timely manner in order to focus on new features and provide a better experience for your users,” says Billy. “Bugsnag is a great way of logging crashes reliably, efficiently assigning those issues to your teams, and resolving them in a timely manner in order to focus on new features and provide a better experience for your users.” — Billy Pham, Technical Program Manager Since implementing Bugsnag, most crashes are now caught during Alpha and Beta releases, and the time to resolution for bugs has gone down by 30%. When monitoring a release, Lyft uses filters to find errors by version, a powerful functionality that gives them visibility into each release without much effort. Dave King, Engineering Manager for Mobile Infrastructure, explains that their team relies on the quantitative data Bugsnag provides on bugs in a particular version. This visibility allows them to triage and fix all high impact bugs in a particular release without hassle. “Bugsnag has great functionality to sort and group all crashes, so we’re able to know which are the most impactful issues based on the number of events and number of users that experienced a crash,” says Billy. An Efficient Debugging Process Saves Time for Innovating Lyft’s Platform Lyft has improved their debugging process by integrating Bugsnag with JIRA and Slack, and have decreased the time spent managing bugs in their tools by 30%. Lyft uses JIRA to manage bugs and has streamlined the process by integrating with Bugsnag. The two-way sync integration allows them to avoid duplicate work because creating tickets and closing bugs across both tools happens automatically with one click. This has made prioritizing bug fixes more efficient for their team, and is one of the features they feel they could not live without. Billy explains that “this saves us a lot of time since we don’t have to copy paste and manually write descriptions. A one click button creates a ticket, and we can just pass it along to the developer. It has made a big impact on our speed.” “The Bugsnag integration saves us a lot of time since we don't have to copy paste and manually write descriptions. A one click button creates a ticket, and we can just pass it along to the developer. It has made a big impact on our speed." — Billy Pham, Technical Program Manager To improve visibility on errors throughout their team, they use Bugsnag’s API to send the top 3 errors in each platform to Slack. Receiving alerts in Slack helps the team stay informed on the status of bugs, and allows them to collaborate on fixes. It also encourages more ownership over their work since everyone is able to see incoming bugs. Another improvement to their debugging process has been adding engineers to on-call duty as they are the best placed to quickly understand and fix a bug in Bugsnag, with the added bonus of increasing ownership of their work. All these gains in efficiency taken together have in turn made it possible for their team to release product enhancements and new features 20-40% faster. Overall, Lyft “considers Bugsnag to be a better tool in terms of functionality and reliability.” Also published here.

Lyft Case Study: How to Ship New Product Features 40% Quicker 

Uber and Lyft Drivers are Increasingly Victims of Carjacking

Eva Co-op Is Continuing The Cooperative Tradition This article is based on interviews I conducted while researching for the article " Inside the Cryptocurrency Casino " in the October issue of Current Affairs. Much of the material from that article was omitted from the final article, but after several reader enquiries, the unpublished material is presented here.  I have no relationship with the Eva Coop, financial or otherwise. Joe Biden wasn’t the only one popping champagne after last year's election. While all eyes were on the presidential race, Big Tech scored a major victory with the passage of Proposition 22, which exempts drivers in the gig economy from the protection of California’s employment laws. By all accounts, it was the most expensive ballot measure in California history. Companies like Lyft, Uber, DoorDash, Postmates and Instacart spent a combined , about ten times as much as the labor groups which opposed them. $205 million Californians were “ ,” not only on television and radio but also through their smartphones. Anyone in California who opened the Uber app in the month before the election had to sit through a pro-22 ad, warning them of higher fees and longer wait times if the proposal failed. deluged with ads mandatory Afterwards, they were prompted to click “Yes on 22” in order to use the app. (There was no option to vote no, but users could click “Okay.”) As expensive as those ads were, they were a bargain compared to what the companies saved in salaries, sick leave, training, insurance and other worker protections. For most companies in the gig economy, the road to profitability comes from cutting labor costs while passing expenses like maintenance and insurance to their drivers. Uber, for example, burned $8.5 billion last year in an effort to undercut its competitors—and once they're out of business, Uber can safely raise prices, having established an effective monopoly on local markets. The system squeezes value from both drivers and riders; fewer than of Uber drivers last a full year. four percent This business model reflects the worst aspects of venture capitalism, says Dardan Isufi, co-founder and Chief Operating Officer of the Montreal-based . Eva is a ride sharing platform, similar to Uber, but with two key distinctions. Eva Co-op First, . It is owned by the riders and drivers, who vote on key decisions, elect the company’s board, and decide how to allocate revenues. Eva is structured as a cooperative Also, unlike centralized competitors, Eva’s app is based on blockchain technology, which is used to protect the data of its users and avoid centralization around a single corporate entity. Power To The Riders "We had the idea in late 2017, when Uber was bullying the government of Quebec,” Isufi explained over Zoom last Thanksgiving weekend. The government had announced legislation requiring Uber drivers to take 35 hours of online training, the same amount of training required for taxi drivers. In response, the company threatened to leave the province. Uber ultimately did not leave Quebec, but the episode provided the idea for a new kind of ridesharing platform. Along with software engineer Raphael Gaudreault, Isufi alighted on the idea of a mobility app that serves the interests of the users. “We decided to ally the cooperative principle of management to decentralization,” Isufi said. Using blockchain-based smart contracts, the app allows Eva to “give back the means of production to workers, and also to give more power to riders.” Although it only hit the road in 2019, Eva is already going places. In Montreal and Quebec City, Eva has 850 driver-members, as well as 28,000 riders, making it the second-largest mobility app in Quebec after Uber. A second Eva Coop is expected to launch in Alberta, with a third on the way in New York. These are the first outposts of what the founders hope will become a “global empire” of member-owned cooperatives. Eva Global provides the technology and initial funding for new franchises, which act as nodes on the Eva blockchain. User data is either controlled by the cooperatives, or encrypted securely in the users’ devices. The result is an app which is designed to serve the users, not investors. While Uber charges upwards of 25 percent of each fare (and UberEats takes even more) the Eva Co-op takes only fifteen percent, a figure which can be changed by a general vote of the membership. In addition, the cooperative provides free insurance to all drivers while they are operating, through a partnership with Desjardins General Insurance. And, while Eva Global acts as a parent company, it does not have the same control as Uber’s headquarters in San Francisco. Eva Global takes a maximum of five percent of total revenues, a ceiling which is encoded by smart contract and cannot be changed without agreement from the block producers. User data is decentralized: even if Eva Global were hacked, as Uber was in 2017, it would be impossible to leak any sensitive rider information. While the app is based on forked software, you don’t need to understand the blockchain works in order to hitch a ride. There are no private keys or twelve-word mnemonics: all you need is a phone and a credit card. EOS The most complicated aspects—like wallet creation, tokens and trading—are handled entirely on the back-end. “Every month I take my car and drive as a driver, just so I can chat with the customers,” Isufi says. Most riders, he says, don’t even know what a blockchain is. New Tech, Old Ideas The emphasis on cooperation and worker ownership may seem a bit out of place, considering the predominance of libertarian and capitalistic ideas in the blockchain space. Isufi, a former officer in the Canadian Army, is hard to pin down as either a wishy liberal or a doctrinaire Marxist. “I do like the capitalist system, in the sense that it allows us to innovate,” he says. “But I don’t like giving my money to Mark Zuckerberg or Jeff Bezos. They have enough money.” “Our goal is to make profit," he added. "But profit for who? For members. Not profit for some shareholders. " Canada has a long tradition of cooperative enterprise, dating back to the 1800s. Four in ten Canadians are a member of at least one cooperative organization, and the figure rises to 70 percent in Quebec. “Most Quebec citizens, their bank is a credit union,” Isufi says. “Most of our farmers are in agricultural co-ops. Some of the biggest stores in Canada are co-ops. For us, it was the ideological thing to do.” That tradition also helped Eva arrive at its initial seed funding. Instead of going to traditional venture capitalists, Eva raised its first $810,000 CAD from government bodies and cooperatives like Mouvement Desjardins, one of the largest credit unions in Canada. These investments allowed Eva’s founders to set up their offices and start hiring drivers, without giving control to financiers. But there are disadvantages to this approach. One of them is money: most investors are not motivated by social good, and driver-owned cooperatives are not exactly a high-yield investment. “Funding is our biggest weak point,” Isufi says candidly. “It’s almost a miracle that we were able to get this funding.” The founders are now targeting more traditional investors, in exchange for equity in Eva Global. There are also tradeoffs to using blockchain technology, despite efforts to keep it user-friendly. Unlike centralized platforms, Eva Co-op cannot reset your account if you get locked out. “People forget their passwords,” Isufi admitted. “And if they forget their security answer, they’re fucked.” That’s not the end of the world, since Eva Global can still create new user accounts and recover lost funds. But the process is still a turnoff for infrequent users.  “We’re losing a lot of drivers because of that,” Isufi says. “These are some of the barriers that blockchain imposes." Delivering Through The Pandemic The COVID-19 pandemic threw another wrench in the works, shuttering businesses and halting tourism.  When I interviewed Dardan Isufi in October, Quebec was reporting a thousand cases per day, and every restaurant and bar in Montreal was closed. Even houseguests were forbidden. But the health crisis also presented an opportunity to explore a new avenue of business: instant deliveries. “What we did is create a software that can integrate any online website,” Isufi explained, which can estimate delivery times and fees. “Even Amazon Prime cannot do better than one business day, but we can do like thirty-four minutes.” There are no credit card fees or account creation: after placing an order, customers can track deliveries in real time, call the driver, and even change the destination. Moreover, unlike the 30 percent commission by UberEats, Eva Co-op only charges merchants a flat monthly fee. “Since we’ve launched that, forty percent of our market share is instant deliveries,” Isufi says, a figure which he estimates at about 2,000 deliveries per month, for 300 merchant clients.  “I think the money-maker of Eva will be instant delivery.” Can Co-ops Beat Uber? Despite the pandemic, the Eva Coop in Quebec is continuing to onboard new riders. Ironically, Isufi says, Uber has turned into one of their best marketing tools: many drivers work on both apps, and introduce their other riders to the local ride sharing co-op. The developers are also working on new functionality for the application’s users. While cryptocurrency payments are a distant goal, the priority right now is to introduce features like tipping and shared invoices, which may appeal to a mass market. While the technology is unique, Eva’s founders see the project as more than just an interesting application of blockchain technology: it’s also a strong counterpoint to the Amazons and Ubers of the tech world, which derive value at the expense of their workers. If Eva is successful, it will be a powerful demonstration that an enterprise can be both profit-oriented and socially responsible. “Every company should be community oriented,” Isufi says. “Why should we empower some already-rich people?”

Uber, Lyft, & Co. Spent $205M On Proposition 22: Here's What Men Can Do Against Such Reckless Hate

Sometime ago for a project, I was looking for a good rate-limiting service. For the scope of that project, the service would run along a front proxy and would rate-limit requests to third party applications. Nginx Plus and Kong certainly have rate-limiting features but are not OSS; while I am a bigger fan of OSS. Using Istio service mesh would have been a overkill. Therefore I decided to use . Envoy Proxy + Lyft Ratelimiting The aim for this blog is help you get started with the rate-limiting service and configure various combinations of rate-limiting scenarios. Let’s dive in… Understanding Lyft Ratelimiter Ratelimit configuration consists of : A domain is a container for a set of rate limits. All domains known to the Ratelimit service must be globally unique. They serve as a way for different teams/projects to have rate limit configurations that don’t conflict. Domain : A descriptor is a list of key/value pairs owned by a domain that the Ratelimit service uses to select the correct rate limit. Descriptors are case-sensitive. Examples of descriptors are: Descriptor (“database”, “users”) (“message_type”, “marketing”),(“to_number”,”2061234567") (“to_cluster”, “service_a”) (“to_cluster”, “service_a”),(“from_cluster”, “service_b”) Descriptors can also be nested to achieve more complex rate-limiting scenarios. We will be performing rate limiting based on various HTTP headers. Let’s have a look at the configuration file. domain: apis
descriptors:
  - key: generic_key
    value: global
    rate_limit:
      unit: second
      requests_per_unit: 60
  - key: generic_key
    value: rate_limit:
      unit: second
      requests_per_unit: 50
  - key: header_match
    value: rate_limit:
      unit: second
      requests_per_unit: 40
  - key: header_match
    value: rate_limit:
      unit: second
      requests_per_unit: 30
  - key: header_match
    value: post
    rate_limit:
      unit: second 
      requests_per_unit: 20
  - key: header_match
    value: get
    rate_limit:
      unit: second 
      requests_per_unit: 10
  - key: header_match
    value: path
    rate_limit:
      unit: second 
      requests_per_unit: 5 - key: custom_header
    descriptors:
    - key: plan
      value: BASIC
      rate_limit:
        requests_per_unit: 2
        unit: second
    - key: plan
      value: PLUS
      rate_limit:
        requests_per_unit: 3
        unit: second local "123" "456" #Using nested descriptors In the configuration above it can be clearly seen There are various different keys with different ratelimit values. We can use them globally for the entire vhost in envoy or even locally for a particular path. We can also have nested values of descriptors. Envoy Front proxy Let’s see how we can use them in the envoy config now. Have a look at the configuration below: static_resources:
  listeners:
  - name: listener_0
    address:
      socket_address:        
        address: 0.0.0.0
        port_value: 10000
    filter_chains:
    - filters:
      - name: envoy.http_connection_manager
        config:
          stat_prefix: ingress_http
          codec_type: AUTO
          route_config:
            name: local_route
            virtual_hosts:
            - name: nginx
              domains:
              - rate_limits:
                - stage: 0
                  actions:
                    - generic_key: 
                        descriptor_value: routes:
              - match:
                  prefix: route:
                  cluster: nginx_1
                  include_vh_rate_limits: rate_limits:
                    - actions:
                        - generic_key:
                            descriptor_value: - actions:
                        - header_value_match:
                            descriptor_value: headers: 
                              - name: prefix_match: - actions: - requestHeaders:
                          descriptor_key: header_name: - requestHeaders:
                          descriptor_key: header_name: - match:
                  prefix: route:
                  cluster: nginx_2
                  include_vh_rate_limits: rate_limits:  
                    - actions:
                        - generic_key:
                            descriptor_value: - actions:
                        - header_value_match:
                            descriptor_value: headers: 
                              - name: prefix_match: - actions:
                        - header_value_match:
                            descriptor_value: headers: 
                              - name: prefix_match: - actions:
                        - header_value_match:
                            descriptor_value: headers: 
                              - name: prefix_match: - actions:
                        - header_value_match:
                            descriptor_value: headers: 
                              - name: prefix_match: http_filters:
          - name: envoy.rate_limit
            config:
              domain: apis
              failure_mode_deny: rate_limit_service:
                grpc_service:
                  envoy_grpc:
                    cluster_name: rate_limit_cluster
                  timeout: 0.25s

          - name: envoy.router

  clusters:
  - name: nginx_1
    connect_timeout: 1s : strict_dns
    lb_policy: round_robin
    hosts:
    - socket_address:
        address: nginx1
        port_value: 80

  - name: nginx_2
    connect_timeout: 1s : strict_dns
    lb_policy: round_robin
    hosts:
    - socket_address:
        address: nginx2
        port_value: 80

  - name: rate_limit_cluster : strict_dns
    connect_timeout: 0.25s
    lb_policy: round_robin
    http2_protocol_options: {}
    hosts:
    - socket_address:
        address: ratelimit
        port_value: 8081

admin:
  access_log_path: address:
    socket_address:
      address: 0.0.0.0
      port_value: 9901 "*" "global" "/nginx_1" true "local" "get" ":method" "GET" #This will be triggered if `X-CustomHeader` is present AND the X-CustomPlan header has a value of either BASIC or PLUS "custom_header" "X-CustomHeader" "plan" "X-CustomPlan" "/nginx_2" true "local" "123" "X-MyHeader" "123" "456" "X-MyHeader" "456" "post" ":method" "POST" "path" ":path" "/nginx" false type type type "/dev/null" Here is how it works: We have defined a named which matches all domains. single vhost nginx There is global rate-limit defined for this vhost. The . descriptor value is global Next, we have under this vhost. Namely, . Routes for cluster and similarly for nginx2. 2 clusters nginx1 and nginx2 path /nginx1 are routed to nginx1 For nginx1, there is generic rate limit defined by descriptor value local and then we have rate-limits for different values of . standard HTTP headers such as method, path etc., and some custom HTTP headers such as X-CustomHeader We have similar rate-limiting set for nginx2 cluster. These 2 nginx clusters defined here actually refer to 2 different nginx containers running as part of docker-compose stack. The overall architecture can be visualized as Simulation Architecture Since they are running on the same network as the ratelimiter and envoy proxy, they can be accessed easily using the container name. All configuration files for this set-up can be found here . In order to run the set-up just clone the repo and do docker-compose up Once the stack is up, you will have 2 nginx containers running on port 9090 and 9091 of localhost.Envoy proxy to intercept and relay requests to nginx servers. Envoy admin console can be reached at localhost:9901. Envoy will be listening as localhost:10000. Ratelimiting service container with configured rate limits which will be used to envoy. Redis container which is used by the Ratelimiting service. It is important to understand that all the applicable actions for a particular path in a cluster are aggregated by the ratelimiter for the result i.e., Logical OR of all the applicable limits Let’s Test our Setup Firstly we need to install , a load testing framework. It can be done by Vegeta brew update && brew install vegeta : request on at 100 requests per second : 10% requests successful. (Logical of , and ) : Test Scenario 1: Case GET /nginx_1/ Expected Result OR "descriptor_value": "global" "descriptor_value": "local" "descriptor_value": "get" Command echo "GET http://localhost:10000/nginx_1/" | vegeta attack -rate=100 -duration=0 | vegeta report Actual Result $ | vegeta attack -rate=100 -duration=0 | vegeta report
          Requests      [total, rate, throughput]  1008, 100.12, 10.92
          Duration      [total, attack, ]      10.071526192s, 10.06832056s, 3.205632ms
          Latencies     [mean, 50, 95, 99, max]    4.718253ms, 4.514212ms, 7.426103ms, 9.089064ms, 17.916071ms
          Bytes In      [total, mean]              68640, 68.10
          Bytes Out     [total, mean]              0, 0.00
          Success       [ratio]                    10.91%
          Status Codes  [code:count]               200:110  429:898  
          Error Set:                               429 Too Many Requests echo "GET http://localhost:10000/nginx_1/" wait — — — : : request on at 100 requests per second. : 50% requests successful. (Logical of and ) : : Test Scenario 2 Case POST /nginx_1/ ExpectedResult OR "descriptor_value": "global" "descriptor_value": "local" Command echo "POST http://localhost:10000/nginx_1/" | vegeta attack -rate=100 -duration=0 | vegeta report ActualResult $ “POST http://localhost:10000/nginx_1/ echo " | vegeta attack -rate=100 -duration=0 | vegeta report
 Requests [total, rate, throughput] 4344, 100.02, 50.56
 Duration [total, attack, wait] 43.434227783s, 43.429286664s, 4.941119ms
 Latencies [mean, 50, 95, 99, max] 5.190485ms, 5.224978ms, 7.862512ms, 10.340628ms, 20.573212ms
 Bytes In [total, mean] 1370304, 315.45
 Bytes Out [total, mean] 0, 0.00
 Success [ratio]  50.55%
 Status Codes [code:count] 200:2196 429:2148 
 Error Set:                429 Too Many Requests — — — Case: request on at 100 requests per second with : 5% requests successful (Logical of , , , and ) : : Test Scenario 3: GET /nginx_2/ X-MyHeader: 123 Expected Result OR "descriptor_value": "global" "descriptor_value": "local" "descriptor_value": "123" "descriptor_value": "path" Command echo "GET http://localhost:10000/nginx_2/" | vegeta attack -rate=100 -duration=0 -header "X-MyHeader: 123" | vegeta report Actual Result $ | vegeta attack -rate=100 -duration=0 -header | vegeta report
          Requests      [total, rate, throughput]  3861, 100.03, 5.18
          Duration      [total, attack, ]      38.604406747s, 38.597776398s, 6.630349ms
          Latencies     [mean, 50, 95, 99, max]    4.96685ms, 4.673049ms, 7.683458ms, 9.713522ms, 16.875025ms
          Bytes In      [total, mean]              124800, 32.32
          Bytes Out     [total, mean]              0, 0.00
          Success       [ratio]                    5.18%
          Status Codes  [code:count]               200:200  429:3661  
          Error Set:                               429 Too Many Requests echo "GET http://localhost:10000/nginx_2/" "X-MyHeader: 123" wait — — — : : request on at 100 requests per second with : 5% requests successful (Logical of , , , , and ) : : Test Scenario 4 Case POST /nginx_2/ X-MyHeader: 456 Expected Result OR "descriptor_value": "global" "descriptor_value": "local" "descriptor_value": "post" "descriptor_value": "456" "descriptor_value": "path" Command echo "POST http://localhost:10000/nginx_2/" | vegeta attack -rate=100 -duration=0 -header "X-MyHeader: 456" | vegeta report Actual Result $ “POST http://localhost:10000/nginx_2/ echo " | vegeta attack -rate=100 -duration=0 -header “X-MyHeader: 456” | vegeta report
 Requests [total, rate, throughput] 2435, 100.04, 5.13
 Duration [total, attack, wait] 24.346703709s, 24.339554311s, 7.149398ms
 Latencies [mean, 50, 95, 99, max] 5.513994ms, 5.255698ms, 8.239173ms, 10.390515ms, 20.287931ms
 Bytes In [total, mean] 78000, 32.03
 Bytes Out [total, mean] 0, 0.00
 Success [ratio] 5.13%
 Status Codes [code:count] 200:125 429:2310 
 Error Set: 429 Too Many Requests — — — : : request on at 100 requests per second with and : 3% requests successful (Logical of , , , , and ) : : Test Scenario 5 Case GET /nginx_1/ X-CustomHeader: XYZ X-CustomPlan: PLUS Expected Result OR "descriptor_value": "global" "descriptor_value": "local" "descriptor_value": "get" "descriptor_key": "custom_header" "descriptor_key": "plan" Command echo "GET http://localhost:10000/nginx_1/" | vegeta attack -rate=100 -duration=0 -header "X-CustomHeader: XYZ" -header "X-CustomPlan: PLUS" | vegeta report Actual Result $ “GET http://localhost:10000/nginx_1/ echo " | vegeta attack -rate=100 -duration=0 -header “X-CustomHeader: XYZ” -header “X-CustomPlan: PLUS” | vegeta report 
 Requests [total, rate, throughput] 2372, 100.05, 3.16
 Duration [total, attack, wait] 23.71156424s, 23.707710415s, 3.853825ms
 Latencies [mean, 50, 95, 99, max] 5.396743ms, 5.179951ms, 7.981171ms, 10.084753ms, 15.086778ms
 Bytes In [total, mean] 46800, 19.73
 Bytes Out [total, mean] 0, 0.00
 Success [ratio] 3.16%
 Status Codes [code:count] 200:75 429:2297 
 Error Set: 429 Too Many Requests — — — : : request on at 100 requests per second with and : 10% requests successful (Logical of , , ) : : Test Scenario 6 Case GET /nginx_1/ X-Header: XYZ X-CustomPlan: PLUS Expected Result OR "descriptor_value": "global" "descriptor_value": "local" "descriptor_value": "get" Command echo "GET http://localhost:10000/nginx_1/" | vegeta attack -rate=100 -duration=0 -header "X-Header: XYZ" -header "X-CustomPlan: PLUS" | vegeta report Actual Result $ “GET http://localhost:10000/nginx_1/ echo " | vegeta attack -rate=100 -duration=0 -header “X-Header: XYZ” -header “X-CustomPlan: PLUS” | vegeta report
 Requests [total, rate, throughput] 1578, 100.07, 10.78
 Duration [total, attack, wait] 15.773500478s, 15.769158977s, 4.341501ms
 Latencies [mean, 50, 95, 99, max] 4.748516ms, 4.514902ms, 7.076671ms, 8.518779ms, 16.077828ms
 Bytes In [total, mean] 106080, 67.22
 Bytes Out [total, mean] 0, 0.00
 Success [ratio] 10.77%
 Status Codes [code:count] 200:170 429:1408 
 Error Set: 429 Too Many Requests Scalability In a production scenario, you might want to run multiple instances of your proxy which can refer to the same ratelimit cluster. The front proxy is basically stateless. As far as the ratelimiting service is concerned, I would recommend scaling it horizontally and moving out Redis Cache to a cloud-based service like RedisLabs or AWS Elastic-cache. Also, using separate is highly recommended. All you need to do is: Redis for Per Second Limits Set the env var, : REDIS_PERSECOND "true" Set Redis endpoint, REDIS_PERSECOND_URL Conclusion We can clearly see that the actual results are pretty close to expected results. We can accomplish all kinds of complex rate limiting scenarios using this and perform request throttling. Feel free to reach out should you have any questions around this. This article was also published on . https://appfleet.com/blog/http-throttling-using-lyft-global-ratelimiting/

HTTP Throttling via Lyft Global Rate Limiting

Let us all admit it! We have always been thrilled by rivalries between two fitting competitors. We have seen it happen between Lionel Messi and Cristiano Ronaldo. We have seen it happen between Roger Federer and Rafael Nadal. The world of technology and business cannot be an exception to this pattern. When it comes to mobile devices and operating systems, it is Google versus Apple. When it comes to enterprise computing, Apple has always been at loggerheads with Microsoft. However, all these technological rivals belong to the old school if we take into consideration the revolution called on demand transportation. When it comes to on-demand transport, especially in the United States, the competition has always been between Uber and Lyft. Any regular person in the United States who has been used to availing on-demand taxi services surely have heard these names being mentioned parallel to each other. This is quite possible that they might even have both the mobile apps installed on their phone. However, they are different as much as they are similar. Both of them have their own share of pros and cons. There are also differences when it comes to their approach and business model. In this write up, let us look at the key differences between Uber and Lyft. Even though they provide the same kind of services they differ in various aspects. The crust of it might seem to be identical. The core of it is not. Where it all starts… Uber, as an idea, originated in France in 2008. However, it started as a company in San Francisco a year later. Lyft was a bit late to the party and it started only in 2012. However, the delay has not deterred Lyft from gaining a considerable share in the US market. The business model The differences between Uber and Lyft is quite a good example that can be given for how businesses that offer almost the same services can have a drastically different set of business models. Uber has always been towards the concept of professional driving services. Most of their audience are business-centric. Uber has also been looking to expand its offerings into multiple segments not just within transportation of people but even in tracking and delivery of food. Lyft, on the other hand, focuses on creating a friendly relationship between the driver and the customer. It is quite likely that the intensity of engagement is far ahead in Lyft compared to an Uber. In addition to that, Lyft has not scattered its focus anywhere beyond cab services. Explicitly, their management has stated that the aim of Lyft is to be the best when it comes to transportation services. In terms of revenue Even when it comes to revenue, there is quite a massive difference. Uber has been dominating the market and it is only expected that Uber has a higher revenue. However, when it comes to the take rate Lyft performs far ahead of Uber. While the take rate of Uber has been struggling to cross the 20% mark, Lyft has been contemplating on breaching the 30% mark. Their application Both these businesses have dedicated apps for Android, iOS and even Windows. There are a lot of similarities between these apps as they offer the same service. Apart from functionality, both these apps offer an amazing user experience. Uber is rich in features and some of these features make it a better option than Lyft. For example, in Uber, the fair estimates and the ETA details are provided even before the ride starts making it extremely transparent. When it comes to Lyft, these details are not provided. Since Lyft is light on features, it becomes extremely easy for making the user interface intuitive and easy to navigate, making it a clear winner in these aspects. The magnitude of engagement When we talk about magnitude, it is best that we measure it statistically! Based on US Android data provided by reliable sources, Uber has an open rate less than Lyft. Lyft has an open rate of 4.5% but Uber stagnated at 2.3%. The best or the worst part (depending on whether or not you are on the side of Uber) is that the numbers are expected to tilt Lyft. What makes Lyft win this game is its intense referral program. The referral program not only targets customers but also drivers. In fact, Lyft offers better referral incentives and sign up rewards, making it a lucrative option for drivers to refer and get referred. This has been one of the greatest contributing factors when it comes to the quality of services and the magnitude of engagement. The vehicle options Both Uber and Lyft give quite a wide range of vehicle options to choose from. Uber offers six different options whereas Lyft offers four. What makes Uber special in this regard is that they also have vehicles that offer a wheelchair accessibility, making it quite accommodative for the differently abled. Lyft counts only on the utility but not on the fancy names. The vehicle options of Uber might seem great on the surface but what Lyft has managed to master is in the art of decluttering. People, more often than not, do not want to experience different levels of luxury but Uber has created a few needless layers of luxury that could have been done away with. Funding and market cap By the time we started to talk about this, we are sure you would have guessed that there is no need for a comparison here. With the valuation of Uber crossing $60 billion and the valuation of Lyft struggling to beat $15 billion, Uber is quite the clear winner. However, when it comes to the funding and the revenue, Lyft has kept it a bit low profile. Uber has raised more than $24 billion in 22 rounds of funding whereas Lyft has raised about $5 billion in 19 rounds. What is to be noted here is that the growth rate of Lyft is far ahead of Uber. Lyft has managed to double its market cap within a year while steadily gaining on the market share. Market share We have now come to discuss the final aspect of our differences which is in terms of the market share. It is quite evident that Uber dominates the market in the United States. It goes without saying that, with the existing marketing effort and global recognition, Uber dominates the market. However, in the past few years, Lyft has been steadily catching up while Uber has been losing out. In 2017, Uber had a 74% market share but it had dropped to 69% the very next year. At the same time when  Lyft started with a market share of 22% but started to grow steadily, reaching a whopping 29%. Conclusion Uber and Lyft have been at constant yet healthy loggerheads with each other. It is only the presence of a competitor like Uber that makes Lyft change their game and give services in a better way. On the other hand, it is the presence of competitors like Lyft that demanded Uber to go for competitive rates and addition of better features. As an aspiring entrepreneur who would like to create a taxi business out of the app, you should ensure that you take the best of both worlds and incorporate them in your app, so your business finds immediate success! Uber clone

Uber Cool vs. Face Lyft

Last year, Uber announced that it will start showing Lime scooters and Jump bikes in Uber app. On surface, it looks like this move will cannibalise Uber’s cab business. Per trip revenue made by Uber is much higher in case of a cab than in case of an e-scooter or a bike. https://twitter.com/uber/status/1016348927312965633?lang=en Six months later, Uber went one step further and started showing public transit information on the app. Now users can get to their preferred destination via Uber using public transport, which is non-monetised by Uber. https://twitter.com/uber/status/1091022619028176896?lang=en A week ago, Lyft followed suit and announced a similar experience on its platform with an arguably better user experience (bottom bar). https://twitter.com/lyft/status/1176650044293824512?lang=en So, why are Uber and Lyft adding commute options on their app that are potential competitors and will cannibalize their core business — cabs? To answer this we will have to take a step back and look at what is the “job” these apps are trying to accomplish for its users. Theodore Levitt, the former Harvard Business School professor, famously said: “People don’t want to buy a quarter inch drill. They want to buy a quarter inch hole”. The premise of jobs to be done theory is that effective customer segmentation doesn’t happen by looking at customer attributes or product features, but by getting a deeper understanding of what job user is trying to accomplish. People have “jobs” that arise in their day to day life, that need to be taken care of. When people come across a job that needs to be done, they look for a product they can “hire” to get the job done. Users don’t buy a product to get product features. They hire a product to get a specific job done, in a specific circumstance. technique, made popular by Toyota, is very helpful while analysing jobs to done. The gist of 5 whys technique is that you'll find the root cause of a problem by asking multiple times. The 5 Whys why? Imagine a situation in which a person want to buy a quarter inch drill. The 5 why approach would be to ask the person why does he want to buy a drill. He might get back with a reason like: to make a hole in the wall. — Why do you want to make a hole in the wall? — To hang a picture of my family on the wall. Q A — Why do you want to hang a picture of your family on the wall? — To give a personal/emotional feel to the house. In this circumstance, the job that a quarter inch drill is trying to do is to make the home personal. Q A The competitor of quarter inch drill in this circumstance is a sticky hinge that stays on a wall with a glue. Another potential competitor for a drill in this circumstance can be a digital picture frame, which probably does the job of making a home personal much better than what a quarter inch drill can do. In words of Clay Christensen in his book : Innovator’s solution “The critical unit of analysis, while analysing a job, should be circumstance and not the customer”. Let’s look at Uber from a jobs to done lens. What is the job users are trying to accomplish by hiring Uber? Users are not hiring Uber to get them a cab. The job to done here is to transport users from point A to point B. Essentially cabs, e-scooters, trains, trams are trying to accomplish the same job for the user through different means — human driven car v/s an electric charged scooter. These different means have underlying different business model but the user doesn’t care about that. The user will pick whatever options that suits him/her best in that particular “circumstance”. Uber wants to be the product users hire to get them to any destination in all possible circumstances. Uber is Trying to Build Transportation as a Service Layer Irrespective of the circumstance you are in, Uber can give you best option to transport from point A to point B. In short term, this will lead to reduction in revenue for Uber. Essentially, all transportation options are competing against each other in this model on one platform. The long-term gain for Uber is a superior mobility experience for the end user that leads to sticky users and higher retention. The ambition for Uber should be to reach a point in future that the user experience of getting to any destination is so superior on Uber that majority of mobility transaction in a city starts happening on its platform. That way Uber can leverage its huge sticky user-base, the demand side, to start charging on a per transaction to the supply side — public transportation. The long term advantage here out-weights short term loss. Another aspect of doing this, from Uber’s standpoint, is building habits in users. Amazon has been trying to build habits in users for years. Whenever you think about buying something online you think of Amazon. Uber is trying to build same habits in users for transportation. It wants to be in a position that users don’t open Google maps to check how to get to a specific place but always open Uber for that. A caveat to be considered is that changing habits is even more difficult than building a new one. Uber has long way to go to instil a new transportation habit in users but it is on right track to do that.

Why Uber and Lyft Feature Competitors Who Could Cannibalize Them

More than a comparison between and , this is a set of thoughts and observations that might help understand the dynamics of taking over established industries. Uber Lyft innovative business models Indeed, when building up a company, a conventional is to design a business model and roll it up in the business world. Or to assembling it piece by piece by gathering feedback from the market and iterating on it. strategy In some occasions though, a business model to be successful requires the transformation of the overall society. When that happens, need to become the builder of ecosystems rather than just companies. For instance, going back to and , their are trying to change an essential piece of our daily lives: transportation. business models Uber Lyft business models Mass car ownership Both and started more like movements than companies. Indeed, they both had an alternative vision compared to traditional transportation systems. Uber Lyft For instance, is about “making it easier to live without owning a personal car. Achieving that goal ultimately means improving urban life by reducing congestion, pollution and the need for parking spaces.” Uber ultimate goal At the same time, Lyft started as a movement back in 2012, and its mission is to “improve people’s lives with the world’s best transportation.” While they’re going in the same directions, those companies share different core values. Where Uber values: Expanding access Delivering reliability Providing choice Aligning needs Being upfront Lyft values things like: Culture and values Brand authenticity A multimodal transportation They might be using a different wordings to deliver the same message. However, from those core values different cultures form, and these cultures are a critical element to the ability of those companies to scale up. There is a phrase attributed to Peter Drucker, “culture eats for breakfast.” Whether or not he said it, we can’t know for sure. But I think this phrase raises an important point. strategy Strategy, in general, might sit on the same table of culture. In some occasions, when you’re rolling out an entirely new , that demands societal adjustments, then culture does eat and for breakfast. business model strategy business modeling In and case, their beginning as a sort of movement didn’t spring up from a vacuum, but it was a bottom-up movement that came from the drawbacks caused over the years by mass car ownership. Some of those significant drawbacks are: Uber Lyft Underutilization of vehicles which are parked for most of the time The inefficiency of owning cars, which led cities to build massive parking lots in urban dwellings The cost of ownership is enormous compared to the benefits of a vehicle, especially within cities Those movements started from large cities, and it’s not by chance that places like San Francisco, Los Angeles, and New York were the places where those services got first rolled out. That brings us to the next point. The formation of new macrotrends Globalization, digitalization, growing urban populations and the growing precarity of jobs all led to the creation of new trends that helped and enter the transportation market, to grow quickly. Some of those are: Uber Lyft A growing number of people prefer to avoid ownership of cars The Internat has made it possible to use certain services on-demand The need for additional income has created a parallel underutilized “cheap job market” People’s growing environmental concerns are making them more aware of their transportation choices The inefficiency of public transportation and the taxi industry has in some cities spurred the need for new alternatives Those macrotrends undoubtedly helped and to roll out their , nonetheless regulation, and protests from existing industries (like the taxi industry). People pushed it for those companies success at a bottom-up level. Uber Lyft business models That also brings us to another critical point. When business models become ecosystems-maker While and , both have two key partners (drivers and riders) their business model success depends on the ability to deal with local communities. Uber Lyft Thus, the overall implementation of this new transportation requires the understanding of complex dynamics that help build ecosystems. business model Indeed, while if you’re trying to build a company in an established industry, all you have to do is to create a sustainable business model able to develop a thriving community. When you’re rolling out a on a whole new industry, that business model needs to plant the seeds to build a new ecosystem. business model Thus, rather than looking internally, it needs to look outside, and while it starts from building a community, it then needs to be able to scale an entire ecosystem! With new industries, new opportunities emerge It is interesting to notice how the emergence of players like Uber and Lyft are also allowing the growth of complementary industries. For instance, Hyrecar is a peer-to-peer marketplace where owners of a car can rent their idle vehicles to drivers that want instead make an additional income by driving for or . Uber Lyft By providing a that makes it easy for car owners and drivers to connect, Hyrecar is building a new business from scratch. platform Why is this happening? aren’t an easy game, and Uber and Lyft have come up with clever strategies over the years, mainly to keep drivers going back to the platform. Platforms Indeed, of the two key players (drivers and riders), probably the most important for them, to generate are drivers. If as a rider I get on the app, but there is no rider around, or my wait time is too long I might well switch to another service or transportation mode altogether. network effects For that matter, both and Lyft use a dynamic pricing : Uber strategy Lyft dynamic pricing to incentivize drivers to drive in certain areas Uber surge pricing to keep drivers going back to the platform to earn extra income Where Uber and Lyft are successful in attracting a broader number of drivers, companies like Hyrecar benefit from those network effects. Platform business models domination Uber and Lyft aren’t just companies; those are platforms. A at its core is a place that connects people and groups and makes their interactions as smooth as possible. platform As a result of allowing those interactions platforms to collect fees, which dynamics is not different from that of paying taxes in the long-run. That’s also why and Lyft are not in the business of owning cars. They allow connections that spur marketplaces. From those marketplaces, they extract economic value in forms of fees. Uber In the last two decades, the web has allowed many to be formed, Uber and Lyft are no exception. platforms Transportation as a service Another radical change that is going through is a broader transition from ownership to possession or use. In short, in many industries, including software (with the rise of ) companies are transitioning from offering products or one-time services to ongoing services that can be benefited via a subscription plan. SaaS business models For instance, Lyft introduced subscription fees to the access of the network of shared bikes and scooters on the or to get access to special promotions. platform As pointed out on Lyft blog with a subscription fee you can get an interesting transportation package: : Lyft Blog Source Many companies are trying to make the switch from product-centered to customer-centered, which implies a transition from single sales touch points to a continuous stream of services and recommendation for paying subscribers. Would this trend pick up, then it might become easier also for other businesses like Lyft to implement and improve their subscription offerings. Key takeaway New industries can be built on top of older and already existing ones. In the case of mass car ownership, more and more people have opted for a model where they don’t own a car anymore. This transition is part of a more profound movement, started almost a decade ago, and that is still ongoing. When new entrants come in and roll out their , it is critical those business models also take into account the fact that a whole ecosystem needs to be built if the business model itself needs to be successful. business models For that matter, companies like Uber and Lyft transition from platforms to ecosystem builders. Only when that process is completed, the will turn out to be successful, and the bottom line might be finally looking good! business model Originally published at fourweekmba.com on March 2, 2019.

Uber And Lyft’s Business Models in Laymen’s Terms

Like almost everyone else in tech, we were excited to see drop last Friday. The rideshare industry has fundamentally changed how millions of people (including us!) get around on a daily basis, which is an incredible accomplishment given that Lyft and Uber have been around for less than a decade. It’s rare to see a product reach near ubiquity over a relatively short period of time, and this feels like a unique opportunity to watch a transformational consumer tech company debut on the public market. Lyft’s S-1 We’ll save you a page-by-page analysis of the S-1 — instead, we wanted to highlight five of the most surprising things we read and what they suggest about Lyft’s business. Given that there aren’t public comps in the rideshare business, we’ll likely learn more about Lyft’s true financial health once Uber’s S-1 is public. But, we’ve compared Lyft’s progress to other public marketplaces when appropriate to give context for some of the company’s key metrics. P.S. — if you’re working on an early stage startup in the on-demand or marketplace space, please reach out at twins@crv.com — we’d love to talk to you! For the core ridesharing business, Lyft’s bookings are the total amount of money collected from riders. However, Lyft doesn’t keep all of the bookings as revenue — the company pays out a wage to the driver, so Lyft’s net revenue is (fare quoted to rider)-(wage paid to driver). For pure marketplaces, we often see 5–15% of bookings (or GMV) as net revenue — in of eight public marketplaces, we saw a median take of 14%. In 2018, Lyft saw a ~27% revenue take rate, up from 18% in 2016. 1. Lyft’s take rate (net revenue/bookings) of 25%+ is surprisingly high. our analysis Graphic created from data in Lyft’s S-1. Lyft’s take rate is increasing for two reasons — the company is: (1) giving riders fewer discounts, and taking a higher cut from drivers; and (2) paying less in driver incentives (bonuses to encourage them to drive). Lyft sees these trends as industry-wide — as ridesharing has matured, all platforms have lowered deals to get riders and drivers to engage. Lyft forecasts this take rate will increase, as they see more room for improvement in incentives. They also expect their bike/scooter business to grow (this revenue was non-material in 2018), which is 100% net revenue — Lyft owns and operates the vehicles. While Uber’s financials are not yet public, the company’s take rate last year was (or upwards of) 25% — suggesting high take rates may be systemic to rideshare. In a on marketplace KPIs, Accel’s Andrei Brasoveanu referenced the ability to charge a higher take if your marketplace is a “key distribution channel.” This is certainly the case for rideshare, as it would be almost prohibitively difficult for a driver to spin up their own network, particularly in contrast to a marketplace like Etsy where the seller could list items on their own website, at physical marketplaces, or in stores. reportedly close to great piece In conjunction with Upwork’s IPO last year, we analyzed other public marketplace take rates — which vary, but tend to be higher when a marketplace has a core distribution advantage. In some ways, it therefore seems fair Lyft and Uber charge a higher take — as well as acquiring customers for their drivers, they handle regulation, customer service, and insurance (we’ll get to this later!) — seller services that are a lot more intensive than those you’d find in a typical online marketplace. Lyft’s leadership has always been vocal about the importance of being a good corporate citizen, but we were surprised by how strongly this was emphasized as a competitive advantage. Frequent words/phrases in the S-1 included (10x), (5x), (4x), and (4x). 2. A “mission-driven brand” is portrayed as the way to beat Uber. social responsibility authenticity empathy mission-driven Lyft’s mission statement, pictured above in the S-1, is similar in concept but differs in tone from Uber’s mission statement (on the company’s website): “We ignite opportunity by setting the world in motion.” Lyft attributed its recent U.S. market share gains (39% in December 2018, up from 22% in December 2016) to this branding resonating with consumers. Lyft has certainly benefited from Uber’s stumbles in at least a few markets (remember ?), and the S-1 outlines Lyft’s conviction that “users are increasingly choosing a ridesharing platform based on brand affinity and value alignment.” #deleteuber Second Measure’s graphic above shows how Uber’s U.S. market share fluctuated during the company’s rocky year of 2017. Second Measure estimates market share using a sample of credit card data — while their 18% estimate for Lyft’s market share at the beginning of 2016 is somewhat close to the 22% stated in the S-1, they estimated that Lyft only had in January 2019 — compared to 39% stated in the S-1. 29% market share The S-1 also details some of Lyft’s specific actions and campaigns around corporate responsibility (present and future): 100% of tips go to drivers, with $750M distributed so far $10M donated to charities via Lyft’s Round Up and Donate program (from 700k riders) Free rides during natural disasters, emergency situations, and on voting days for underserved communities Rides are carbon neutral— Lyft is one of the largest voluntary purchasers of carbon offsets Partnership with cities to offer bike/scooter rides to low-income residents Plan to invest the greater of 1% of profits or $50M to social impact efforts annually Lyft’s gross margin is 42% (up from 19% in 2016), but the company’s EBITDA margin is -45% (up from -201.7%). These numbers are relatively low based on public marketplace comps, which typically see EBITDA margins of 5–10% — reflecting the fact that Lyft is much more complex than a pure software marketplace. 3. Huge insurance and incentive costs hit margins. By far the largest hits on profitability are cost of goods sold (58% of revenue in 2018) and sales and marketing (37% of revenue). Within COGS, insurance costs increased by $319M in 2018, compared to only $110M for payment processing, and $75M for platform hosting. The Lyft team details insurance needs at length in the S-1 — the company provides $1M in commercial automobile liability for each driver, which is managed through their own insurance subsidiary and third-party providers. Graphic created from data in Lyft’s S-1. As of EoY 2018, Lyft was holding nearly $865M in an insurance claims payment account (more than doubled from $360M in 2017). The S-1 attributes a significant increase in insurance costs in Q1 2018 due to “increased frequency and severity of claims,” though the company still appears to be somewhat cautious in estimating reserves. From 2016–2018, only 20–35% of the year’s reserves were paid out in losses. The second largest drag on margins is sales and marketing — which is where driver referral and rider incentive (discount) costs are allocated, which represented 36% of all sales and marketing spend in 2018. Lyft paid out $296.6 million in targeted rider and driver incentives in 2018, almost double the 2017 amount of $155.6 million (which was up only slightly from $141 million in 2016). Uber’s work in autonomous driving has been heavily publicized, especially during the company’s drawn-out lawsuit with Waymo over the acquisition of Otto. But Lyft is also betting big on autonomous vehicles, which were referenced more than 100 times in the S-1. Lyft, through a partnership with Aptiv (formerly nuTonomy) has completed nearly 35,000 autonomous rides (accompanied by a human safety driver) in Las Vegas. 4. Lyft has invested heavily in AV — and is closer behind Uber than they may seem. Lyft has completed 35k monitored autonomous rides through its partnership with Aptiv — image from Lyft’s . blog Of Lyft’s 4,700 employees, “several hundred” work in the company’s AV lab in Palo Alto, the Level 5 Engineering Center. Lyft plans to build its own AV system and partner with third-party developers who can build on Lyft’s Open Platform. Last year, Lyft disclosed its acquisition of computer vision startup Blue Vision Labs, as well as its with an auto manufacturer to build AV tech — both of which are referenced in the S-1. $72M five-year agreement As a result of these efforts, Lyft hopes to serve a “portion” of rides via AV in the next five years, and the majority of rides via AV in the next ten years. Uber, by contrast, has pursued a more aggressive strategy in deploying autonomous tech — after acquiring Otto for a reported $700 million in 2016, the company started AV tests in four North American markets (at least one without a DMV permit). Uber on deploying a driverless car service by EoY 2018, but has scaled back with new CEO Dara Khosrowshahi. Similar to Lyft’s projections in the S-1, Khosrowshahi stated in early 2018 that Uber’s ability to serve of a city’s rides autonomously is still 10–15 years out. originally planned the majority You may remember Lyft’s $250M acquisition of Motivate, the largest bikeshare company in the U.S., in late 2018. According to the S-1, the acquisition gave Lyft 200 new employees, as well as a commitment to invest $100 million into the New York bikesharing ecosystem over the next five years. Based on the frequency of mentions in the S-1 (“bikes and scooters” comes up 105 times), providing short-distance EV transport is crucial to Lyft’s mission of enabling multimodal “transportation as a service.” 5. Bike and scooter revenue is “not material” now — but likely will be soon. Image from Lyft’s S-1 — Lyft’s transportation as a service (TaaS) network spans local transit (info provided for free in the app), short-distance bikes and scooters, and a range of longer-distance vehicles. Lyft did not disclose bike and scooter revenue, beyond stating that it was “not material” for 2018 — which is unsurprising given the acquisition took place in November. However, Lyft’s disclosure in the S-1 that there is “no material difference” in active revenue per ride between rideshare and bikes/scooters gives us some more insight into this business line. Because Lyft has a 28% take rate on rideshare and a 100% take rate on bikes and scooters, this implies the average rideshare booking is ~3.5x the average scooter booking. Graphic created from data in Lyft’s S-1. Lyft does not outline revenue per ride in the S-1, but we calculated it at $2.80 given the rides taken and revenue earned figures for 2016–2018. Since Lyft’s bike/scooter share services have only been deployed since Q4 2018, this implies the average bike/scooter booking is ~$3. At a $1 cost to unlock and $0.15 per minute to ride, this implies an average ~13 minute ride. This is relatively in-line with data that has been by Bird, Lime, Jump, and other major e-vehicle operators, though we don’t know what Lyft’s vehicle utilization looks like compared to competitors. In the S-1’s, Lyft’s listed concerns about bike and scooter operations include: reliance on and potential quality issues from third-party manufacturers, uncertain weather-related seasonality, and “negative public perception” around e-vehicle launches in new markets. publicly disclosed Lyft did not disclose specific plans for expanding the bike and scooter service, beyond growing a pilot that connects riders to local transit or a Lyft shared ride. The company estimates the useful life of a scooter as 12 months for depreciation purposes (which seems somewhat optimistic, given a suggesting that the average lifespan is closer to one month), and as of EoY 2018 had $23M in value of scooters not yet deployed. In total, the company listed $68M of bike and scooter fleet acquisition costs for 2018. recent report Thanks for reading! We’d love to get your thoughts on anything you found to be particularly surprising or interesting in Lyft’s S-1 — feel free to email us at or tweet us . twins@crv.com @venturetwins Like this post? to help more people see our story, and share it with your friends! Clap for it Interested in reading more content from us? You can subscribe to our weekly newsletter, , for more insights on millennial and Gen Z trends: Accelerated https://accelerated.carrd.co/

Five Surprising Things in Lyft’s S-1

I was in New York City last month, in a Lyft, and I asked the driver how he was doing. He said it was a slow day. I’d heard that it was slow for the service industry in January. I asked him how much money he makes on an average day. Four hundred dollars. Four hundred dollars times 5 days a week times 52 weeks a year… is $104,000. He explained that he works for Lyft, Uber, and two other public apps. He also works for two other private app companies that allow him to pick up and drop off at the airport. He takes whichever job comes in first and off he goes. He’s working six different apps so he can maximize his time and his profits. He’s found a way for technology to work for him. Instead of waiting on one or two apps for a job to come through, especially in the slower months, he doesn’t wait. He goes from one app to the next so that he’s always on the go, always making money throughout his work day. He’s leveraging the technology to make the most amount of money in the least amount of time. That’s what the new internet is going to enable for everyone. The new internet will be layered with blockchain technology. The tokenization of all forms of value and the transfer of crypto will happen seamlessly, with minimal friction and miniscule fees. As blockchain technology allows for a more efficient means of value transfer, micro-payments will be made over the internet like credit card money transfers are now, only it will cost less and it will happen almost instantly, and in most cases there will be no big banks or interemediaries taking a cut. Soon, making $104,000 a year will become even more commonplace for anyone interested in leveraging their time through efficiencies inherent in the technology. The new internet will allow users to make money (by earning convertible tokens) — doing things we do now for free. Soon, and in some cases even now, we can make money by listening to podcasts, watching ads, writing blog posts, exercising, playing video games, and more things we do everyday anyway. If we’re going to do these things anyway, why not make money for doing them? Back to the Lyft driver I met. $104,000/year works out to $50/hour. That’s more than 3x the newly increased hourly rate for minimum wage and almost double the average American household income for 2018. There’s a new internet being built right now, and with it, new opportunities. It’s not just for the tech savvy. Making money over the new internet will one day be for everyone.

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