Analyzing Business Processes And Data To Enhance Lyft's Services Case Study

Lyft's Data-Driven Strategies for Ridesharing Growth and Resilience

Part 1: Business Process Analysis

Introduction

Lyft is a transportation network company that operates ridesharing platforms in the United States and Canada. Founded in San Francisco in 2012, Lyft pioneered the Peer-to-Peer rider-sharing model, allowing individuals to use their vehicles to share. It can develop a sharing model and pay for your bills through Lyft's mobile app. Unlike traditional taxi companies, Lyft does not own a fleet of drivers who are independent contractors. This extremely lightweight model has allowed Lyft to grow rapidly. Lyft went public in March 2019 with an initial stock price of $72 per share, giving him an enterprise value of $24 billion.

However, Lyft doesn't have to make a profit on an annual basis, it has to incur losses of at least $1. The company faces stiff competition from ride-sharing giant Uber, regulatory challenges in some markets, and must continue to attract passengers to grow customer accounts. Lyft's business goals are focused on providing efficient, affordable, and convenient transportation services to the cities in which it operates (Schiavone, et al. 2021). The company's goal is to promote private car ownership and improve transportation access. Core elements of Lyft's strategy include recruiting drivers, acquiring drivers, focusing employees on areas of the business, optimizing processes with technology, and differentiating itself from competitors. Lyft enables real-time sharing of internet access connections between independent drivers and passengers through its mobile app and website. The relevant business processes that allow Lyft to successfully balance supply and demand include:

• Driver Onboarding: Applicant Screening, Background Checks, Traffic Inspection
• Demand Forecasting: Analyzing historical driving data to predict who and which passengers will be dropped off
• Dynamic Pricing: Supply and Demand Algorithmically adjust prices based on
• Rid?r Profiling: Rid?r data and payment collection
• Matching: Pair drivers with available drivers and algorithmic assistants
• Trip Tracking: Trip progression using GPS Monitor Status
• Payments: In-App Payments and Processing and Driver Payments
• Reviews: Driver Collection - and Driver Reviews and Reviews

Lyft's growth and operations were significantly impacted by the COVID-19 pandemic that began in March 2020. The pandemic and resulting lockdown significantly reduced transport demand as social distancing was introduced and travel became easier. Failed by the stock market crash, Lyft's stock price fell to $14 in March 2020, a nearly 80% drop from its post-IPO price of $72. Lyft saw revenue decline and sales volume drop by 75% in the second quarter of 2020 due to the pandemic. Demand was so poor that the company was forced to lay off 17% of its workforce this quarter to cut costs. Lyft reported a loss of more than $1.

In his first nine months of 2020, he had a loss of $8 billion, whereas a year ago he had a loss of $644 million. Revenue of Rides also fell by 53% during this period. However, Lyft expected the pandemic threat to decline through the third quarter of 2021 as vaccinations begin and economic activity increases (He, et al. 2022). Lyft's stock price is expected to go public by November 2021 as investor conditions have not improved, and will fall again in 2022 given the wide market volatility. Still, the company's stock remains sensitive to a spike in coronavirus infections and regulations. To visualize Lyft's stock price movement during the pandemic, we collected daily closing price data from Yahoo Financ? for the period January 1, 2019, to September 30, 2022.

Business Process Analysis

Demand forecasting process

The data include the pre-pandemic phase, the first recession due to the pandemic in early 2020, follow-up reports in 2021, and implementation in 2022. The 4,444 pieces of data were plotted on a line graph with annotations to identify correlations (Mitropoulos, et al. 2021). As shown in Figure 1, Lyft's stock price in 2019 after its March IPO ranged between $40 and $60. The stock started 2020 around its offering price of $72 but began to decline in late February as global markets reacted to the worsening coronavirus crisis. Lockdowns wiped out demand for sharing, and by mid-March, Lyft's stock price fell below $20.

After briefly surging in the summer of 2020, the stock fell to $40 by the end of 2020 as cases rose again in the fall and the stock rose (Li, et al. 2022). Lyft stock had fallen to $60 in early 2021 as vaccine distribution increased, indicating demand and demand was imminent. Lyft stock soared above $65 in the spring of 2021 before crashing again in August amid concerns about slowing growth of the Delta variant.

However, as trading volume improved, the stock price rose to a record high of around $63 in November 2021. In 2022, Lyft's stock price fell along with the stock market downturn, hovering between $10 and $35. The fluctuations highlight Lyft's continued efforts to reduce demand and secure future growth prospects.

To further analyze the impact of COVID-19 on Lyft's business, it would be beneficial to use business process change techniques to modify and review some of its business processes.

Dynamic Pricing Process

Pandemic market demands and dynamic pricing have significantly disrupted two key processes. Demand forecasting to predict future demand requirements at a specific location and time. Accurate D?mand predictions allow Lyft to efficiently position drivers where and when they are on the road. Because of the pandemic, Lyft is using machine learning algorithms based on historical ridership data to predict demand (Schaller, 2021). However, the model has been inaccurate for some time due to fundamental changes in behaviour due to COVID-19.

Figure 1: Use case diagram

The diagram above shows a simplified business process model for Lyft's pre-pandemic demand forecasting approach using the Business Mod?l and Notation (BPMN) standard. As shown, historical training data is aggregated and linked to model training data. Characteristics of interest include timestamp, location, distance, and regional factors such as distance. Data scientists develop new technologies, train machine learning models, and deploy based on accuracy metrics using initial performance models (Usman, 2021). The Demand Forecast Model provides forecasts for removing Demand L?ls hourly for the next 7 days. These forecasts inform dynamic pricing and guide the planning process.

Regardless, due to the impact of the pandemic, the module was unprepared for a catastrophic demand shock and began to evaluate unreliable forecasts. With customer numbers down 75% year over year in Q2 2020, Lyft's predictive models are in dire need of training and recalibration. Dynamic pricing is another key process challenged by the impact of COVID-19 on demand patterns (Dew, et al. 2023). Lyft uses traditional dynamic pricing algorithms to adjust prices based on local supply and demand conditions. While the demand for available drivers is high, so is the incentive to get more drivers on the road. The pricing model aims to balance vehicle affordability with increasing revenue.

Figure 2: Business Model

The Figure shows a simplified model of Lyft's pandemic dynamic pricing process. The pricing algorithm takes into account current customer demand forecasts, driver availability data, and busfar numbers. These are therefore multipliers that can be applied to the base price to determine the actual selling price. These multipliers are continuously calculated to accommodate changes in supply and demand (Shin, et al. 2023). If the supply of drivers is low, there is a risk that the multiplier will attract more drivers. The price will be displayed and you can purchase the book.

However, the pandemic has caused demand to collapse in many markets, creating delivery bottlenecks. Lyft responded by limiting price reductions to 5% to 10% and abandoning its dynamic pricing model to maintain ridership revenue. This requires adjustments to pricing algorithms. Lyft also promoted bonus and rewards programs to drive improvements during the crisis.

Stakeholder Analysis

Analyzing the activities of the customers, including drivers and passengers, provides further insight into the continuing impact of COVID-19 on Lyft's operations. With driver availability running low in 2020, drivers made the transition and had long waits for drivers (Yun, et al. 2020). An analysis found that the No.1 concern among Lyft drivers during the COVID-19 crisis was recalling vehicles without identifying health risks.

Many drivers are choosing alternative modes of transportation, such as driving their cars, walking or cycling, to avoid the possibility of contracting the virus. Riders also faced disruptive dynamic pricing as its algorithms struggled to keep up with demand and change. This dissatisfaction may grow and dissatisfy. As it is known, Demand's solution conclusively proves that the pandemic will end by the end of 2021, as drivers can return home safely.

Meanwhile, many Lyft drivers suffered financial hardship as ridership declined, despite the company's efforts to mitigate price declines.

A survey of rideshare drivers in California found that 85% have experienced a decrease in income due to COVID-19.75% of drivers surveyed said they were struggling to pay basic expenses during the pandemic (Zhu, et al. 2021). The sudden loss of income forced some drivers to collect unemployment benefits or leave the platform.

Drivers also faced new costs such as cleaning and disinfecting vehicles, purchasing protective equipment, and complying with evolving regulations regarding passenger limits and masks. Dissatisfaction grew as these costs were not reimbursed. Drivers who continued driving were at risk of contracting the virus from passengers. Lyft tried to meet drivers' needs by distributing hygiene products and suspending collection trips. However, many drivers still felt that safety measures and financial support were insufficient.

Microenvironmental Analysis

Applying the PESTEL analysis framework provides further insight into the macro-environmental forces that impacted Lyft during COVID-19 (Ali, et al. 2020). The pandemic was an external public health shock that affected all aspects of the PESTEL model:

• Political: curfew, restrictions on non-annual business activities, new regulatory protocols
• Economic: Consultation, consumer policy, drivers of income loss
• Social: Moving away from shared mobility; Focus details Hygiene and distancing
• Technology: T?l?matik for contract payments, supply positioning
• Environment: Reducing traffic congestion and operating during lockdown 4 444
• Legal: Limits and mask regulations, worker classification lawsuits

This multi-sector disruption is fundamentally based on the sharing of Lyft solutions. This required constant adaptability as conditions changed rapidly. Lyft has not been nimble enough to use its technology to adjust pricing and routing algorithms to improve hygiene and reduce costs.

SWOT Analysis

However, this immediate shock was still a major setback for the company. Applying SWOT analysis reveals Lyft's competitiveness during the pandemic. Internal damage caused by the coronavirus pandemic included high fixed costs, loss of drivers, and unprofitability. Lyft is burning cash while operating at dramatically increased volumes with minimal variable costs.

The sudden loss of drivers also affected operations in some markets. The crisis accelerated Lyft's path to profitability (Gangadharaiah, et al. 2023). How Lyft benefited from several strengths in weathering the recession. It exists only domestically, thus avoiding global complexities. The advantage of some nationals is that they earn more than others. The ample cash proceeds from the IPO gave the company financial strength. Lyft also leveraged Rieren's unique communication capabilities to help with social distancing. External opportunities include the potential to gain market share from struggling competitors and traditional taxis.

There are also opportunities for diversification. However, the shift to daily work and personal transportation poses risks to urban transport in the long term. Some citizens have imposed restrictions on drivers to strengthen workers' rights. In short, COVID-19 has not only strengthened Lyft's strengths.

Suggestions

In light of the impact of the pandemic, the following recommendations have been made to improve Lyft's business processes and strategies:

• Develop a more adaptive demand forecasting module using the NS-MBL algorithm and advanced training to respond to demand shocks. Pricing during floating periods
• Provides drivers with more supportive policies that guarantee a minimum wage to encourage improvements.
• Diversify services provide comprehensive services such as packaging and food delivery.
• Strengthen partnerships with airports, hotels and restaurants to facilitate destination requests
• Improve virtual queuing capabilities to minimize requests and border wait times
• Different price classes represent a broad market
• Prioritize construction in urban areas over emerging suburban areas in line with long-term mobility trends

COVID-19 wiped out second-period demand and significantly disrupted Lyft's operations and growth trajectory. The crisis has created vulnerabilities in algorithmic pricing and matching processes. However, Lyft has not shown the ability to adapt, leveraging its technology platform to adjust its service position, offer new propulsion services, and reduce costs. The pandemic has highlighted the importance of building more flexibility into Lyft's business processes (John, 2023). Recommendations include improving demand forecasting models, adjusting dynamic pricing guidelines, enhancing driver assistance, and sharing supply. As disruption continues, Lyft must continue to operate safely.

Driver-Rider Matching Process

In addition to demand forecasting and dynamic pricing, Lyft's ride-to-fare matching has also experienced significant disruption due to the impact of COVID-19 on transportation operations. It uses Lyft's algorithm to match drivers and pair local drivers with driver-by-driver. Efficient matching is essential to reduce waiting times and provide reliable service. Before the pandemic, Lyft's matching logic created demand forecasts and Real-Time supply positioning to match riders with the right passengers.

However, 2020 will be difficult as demand collapses and supply shortages occur in many markets. Lyft needed to balance market liquidity by using incentives and gamification to move customers to areas of high demand (Khan, et al. 2022). The diagram above shows a simplified model of Lyft's PR-Pandemic Rid?r driver matching process. If there is a problem, the matching algorithm evaluates current bid positioning, project needs, driving capabilities, and cost estimates to determine the best driver to assign. The request can then be accepted or accepted by the appropriate driver. In this case, suboptimal drives are removed from the solution.

By introducing such an automatic optimization algorithm that allows for continuous training, Lyft's matching process could become more adaptive. Dynamic balancing incentives for drivers also help smooth offer positioning. Lyft has earmarked $50 million in stimulus funds to shift its footprint to the business sector during the pandemic (Appiah, 2022). Analyze the impact of his COVID-19 across different phases using the journal mapping method.

The entire process with issues appearing at various points. The biggest complaints during the pandemic include long wait times, high prices, and unreliable estimated arrival times. But Bike Journal fundamentally changed in 2020.

Rider Journey Analysis

Cyclists had to consider the potential virus infection risk if they wanted to use bike sharing rather than other mods. New measures will be required, including wearing masks and sanitizing hands and vehicles in front of cameras. With Riders, it can also choose a free driver when you make a call or while you wait. Damage and safety affect satisfaction.

However, Lyft has introduced innovations to improve the rider experience during COVID-19. The wait-and-save feature allows drivers to postpone a ride until the fare increases. Lyft also partnered with pharmaceutical company GSK to distribute 40,000 hygiene kits to drivers, including masks, and sanitiser.

In addition, it has introduced Vehicle explanations from the driver within the app, as well as confirmation requirements from the driver (Biegon, 2023). To further reduce friction, Lyft could set up a virtual delivery service to allow travellers to pick up passengers as quickly as possible without having to make last-minute moves. Increased transparency for drivers regarding vehicle disinfection protocols could also improve driver safety. Promoting pooled solutions can help address security concerns, as price incentives may be needed due to the pandemic.

In summary, COVID-19 was critical for Lyft in quickly adapting its response, pricing, and forecasting processes to dramatic market changes. New challenges are emerging for both driving and driving. Lyft has shown some agility, but major investments in dynamic algorithms and liquidity management tools could make it more resilient to future demand shocks. Journey mapping and human design thinking should aid efforts to balance processes around user and customer engagement.

Recommendations

The outbreak of COVID-19 and the subsequent economic shutdowns have severely affected Lyft's operations of ridesharing and trajectory of growth. Demand fell 75 per cent year on year in Quarter 2 in the year 2020, wiping out gains and developing layoffs. Lyft's stock price decreased by up to 80 per cent since the IPO in the year 2019. While ridership has rediscovered slightly. Lyft continues to struggle with faulty demands and effective methods of forecasting and dynamic algorithms of pricing that are inappropriate for the mobility scene of the post-pandemic. At that exact time, a large number variation of drivers quit the platform due to health concerns and inadequate remuneration. Lyft must prioritize revamping driver supply and fidelity by assuring greater financial guarantees through minimum wage guarantees (Rajendran, and Pagel, 2020). To maintain operations conducted during low-demand periods, dynamic base pricing should be utilised. Lyft should develop into delivery assistance and enhance virtual queuing to meet redirecting transportation demands.

Recommendations

• Enhance demand forecasting with adaptive machine learning: Utilising the models of Neural Network Time Series and developing clustering procedures to train algorithms to respond more actually to require shocks.
• Use emotional base pricing and driver stimulis to keep rides reasonable during slow periods by temporarily decoupling price from a directive. Offer drivers stimulus accounts to encourage reserve.
• Diversify disperse by offering complementary delivery options for parcels, groceries, and food to develop additional revenue authorities.
• Facilitate virtual pickup - Use SMS updates and established pickup sites to decrease wait times.
• Differentiate premium products - Launch new high-end Lyft Lux groups desired at business travellers who refuse to compromise on price or health security.
• Prioritize urban density - In line with mobility tendencies, focus maturation on city centres that are well-suited for ridesharing rather than extended suburbs.

These proposals would benefit Lyft by enhancing its economic sustainability, technological capabilities, and market placement service quality, Lyft must persist in innovating operationally while enlarging its offerings to meet diverting rider tastes in post-pandemic conditions. Adapting systems and delivering driver support are imperative for controlling supply-demand volatility. If performed accurately, Lyft can thrive as the transportation supervisor, paving the way for the hereafter of shared mobility.

To construct a more adjustable demand forecasting technique, Lyft could utilise Neural Network machine learning procedures for time series comment. Unlike linear regression algorithms, neural networks can uncover elaborate nonlinear designs in rider data. Models can be more generalised by movement on a broader set of parameters such as climate, events, rates of unemployment, and developing mobility tendencies. Unsupervised clustering algorithms should split demands to account for variations in transit demeanours between cities. This facilitates individualized forecasting for each rider profile grouping. As fresh data becomes unrestricted, the models should comprise feedback circles to allow for continual retraining.

Instead of the completely dynamic pricing, Lyft should furnish an adjustable base of the fare level to maintain affordability. For example, it might restrict demand-based modifications to 10 per cent above or below a predetermined baseline expense. This enables to reduce price instability for riders. To facilitate driver supply, Lyft might furnish weekly gratuities for satisfying a set number of rides during off-peak hours. Gamification, including the point and reward systems, could help to elucidate regular hours. Collaborations with retail chains and restaurants would be critical in developing delivery prospects on Lyft's network. Users of the app Energy request that riders pick up and drop off items on the way to their goals. Lyft might charge committee fees for each delivery or include charges in trip expenditures in the integrated payment instrument. This produces cumulative revenue.

Part 2: Business Data Analysis

Task 1: Introduction

As a main ridesharing organisation, Lyft has an abundance of information on client interest, driver supply, pricing, courses, and that's just the beginning. Gathering, sorting out and examining this information is critical to understanding qualities and shortcomings in Lyft's business processes. This empowers data driven decision-making to improve activities. Lyft, for instance, can determine, through the use of data analytics, which routes have excessively long wait times at particular hours (Butani, 2023). The root cause may be driver shortages in a specific region, according to the analysis. Lyft can then test and approve arrangements like expanded evaluation or driver incentives on those courses. Additionally, data can reveal opportunities to enhance accessibility, inclusion, sustainability, and safety.

Task 2: Application

Collect and analyse Lyft's share price data during the COVID-19 pandemic

Figure 3: Analyse Lyft's share price data

Beginning in March 2020, the coronavirus outbreak caused significant disruptions to Lyft's core ridesharing service. Ride requests fell as widespread lockdowns and social distancing measures took effect. Lyft's part cost data confirms this outcome. Lyft's shares closed at $52.75, its pre-pandemic high, on February 19, 2020. However, as the initial wave of contamination struck in the spring, Lyft's share price began a precipitous decline, ending at just $14.82 on the 18th of spring. This addressed a 72% decline in less than a month, highlighting investors' dissatisfaction with Lyft's potential in an emergency.

Lyft's share cost decreased for the great majority of 2020, and on November 6th, 2020, it reached its lowest point at $21.33. This coincided with additional financial constraints as well as fall contaminations from floods (Sun, 2022). However, the stock gained ground in the middle of the vaccination optimism in 2021, closing at $56.09 on February 11th. All things considered, pandemic vulnerability, work limitations affecting driver supply, and downturn anxieties kept Lyft's portion cost unsteady in 2021 and 2022. The stock closed at $10.31 on December 30, a considerable decline from its pre-pandemic valuation.

Examining Lyft's historical component costs reveals the severe, supported impact of the coronavirus on ridesharing. It also illustrates Lyft's ongoing battle to maintain operational stability over three years after the incident. Increasing adaptability by bringing on more drivers and branching out into transportation and other vehicle services could aid Lyft's recovery from the pandemic.

Visualise trends using Excel charts

Figure 4: Visualise trends

Lyft's portion cost execution during the coronavirus epidemic may be externally analysed by creating line diagrams in Succeed that exhibit the daily high, low, and closing costs over time. These charts show the open, high, low, and closing data points for each trading session, giving a clear picture of price movement and volatility.

To plot the High, Low, and Close values from March 2020 to December 2024, for example, we may use a line chart with the Date on the x-axis and the Share Price on the y-axis (Cusumano, et al. 2020). This captures the unstable decline towards the start of the pandemic in the spring of 2020, followed by the post-immunisation recovery surge in mid-2021. In addition, it exhibited volatility in 2021 and 2022, with sporadic value surges and crashes.

By using Succeed trendlines to gauge climbs and decreases, we may improve this analysis. For example, adding a straight trendline for the last quarter of 2022 reveals a declining inclination, which represents Lyft's attempt to maintain share cost levels despite widespread vulnerability. Other markers, such as movable midpoints, can also draw attention to cost force.

The basic Outlining tools of Succeed provide extraordinary flexibility (Sigler, et al. 2023). It is easy to add parameters such as volume or change the representation from lines to columns. Making design decisions also allows us to change titles, information point markers, tomahawk s, and other elements for clarity. Succeed essentially simplifies the process for business analysts to plot Lyft's aggressive cost history throughout the coronavirus, highlighting both alarming drops and more recovery and development speed increases.

Identify events impacting the share price

Figure 5: Events impacting share price

Examining the daily trading volume reveals a few surges that most likely correspond to significant events affecting Lyft's valuation. The primary notable increase in volume occurs in the middle of Walk 2020, reaching a peak of over 220 million offers on Spring 18. This is in line with the growing fear of a pandemic that led to a sharp decline in Lyft's stock price as ride demand fell due to transportation restrictions. Midway through 2021, volume surges once more, reaching peaks of over 300 million offers by the start of February. This signifies a resurgence of market optimism during vaccine debuts (Zhao, et al. 2020). However, vulnerabilities persisted, as seen by volume periodically peaking in 2021 and 2022 and reflecting coronavirus waves and recurring inversions.

High volume days in 2022 may also capture reactions to concerns about economic growth brought on by aggressive Fed rate hikes and excessive inflation. Volume reaches a peak of a little under 500 million in December 2022, presumably due to share expenses being pressured by year-end charge misfortune selling and recessionary concerns (WOLNIAK, and GREBSKI, 2023). Analyzing these volume spikes and connecting them to longer news events provides context for the key selling and buying events that fueled Lyft's shares during the pandemic. This might highlight decisions to manage uncertainty in the future.

Provide recommendations based on findings

The data indicates that Lyft needs to strengthen its defences against outside shocks like COVID-19, which sharply reduces ride demand. Aggressive development into ancillary industries, such as food, shopping, and package delivery, might offer some protection when core rides slack off. Additionally, setting up guidelines and agreements with providers of basic healthcare services might guarantee income transfers even in times of restricted development (Qi, et al. 2020). Furthermore, Lyft should look into more variable pay models for drivers so that wages can be changed in reaction to the demand for rides and expected income. It's dangerous to secure in fixed above amid downturns.

Conclusion

In summary, Lyft's share cost information reveals the enormous challenges it faced during the COVID-19 outbreak. Thinking about the volatility entails times of sharp drops in value, uneven recoveries, and waiting susceptibility. However, there are signs that development vigour will emerge in 2022, which is a guarantee. By diversifying its sources of income, forming smart alliances, and articulating its ideals, Lyft can increase its resilience to ensuing external shocks. Though there are still issues, Lyft's data also shows opportunities to refine the strategy and expand services for success after the pandemic.

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