Three ways for sharing economy companies to support operational cost control
Shifting gears: getting from market share to margins
The sharing economy industry is diverse: There are ride-sharing companies – Lyft and Uber, of course, are front and center here – car-sharing, bike-sharing, as well as on-demand scooters and mopeds. There’s also on-demand carpooling, or food and package delivery by ride-share drivers. While a leading goal of these companies is profitability, scaling and securing market share is an important first step. In this world, margins inevitably suffer.
Let’s focus on Uber and Lyft and how they loosened their margins in service of growth.
To scale, Lyft and Uber first invested in early wins in terms of securing market penetration, often sacrificing meaningful profit margins with, of course, buy-in from their investors on this approach. However, having each gone public, the companies will face new and different pressure from shareholders around quarterly losses. Ultimately, investors will want to see how their strategy builds to long-term wins that secure their business’s future.
For example, both Uber and Lyft have made progress in shoring up their core business by narrowing their focus to areas that generate revenue and backing out of markets that don’t. Case and point, by exiting out of major international markets that weren’t working for them – China, Russia, and Southeast Asia – the company posted a positive net income in 2018 by gaining $3.2 billion from divestitures. This helps cut costs and narrow the profit gap.
But it’s a complex problem: each company has its own set of challenges and opportunities to succeed long-term, but in reviewing Uber and Lyft’s recent IPO statements, we have identified three areas to focus on for the ideal profit-loss balance in the short term:
- Insurance costs
- Drivers’ fees
- Sales and marketing costs
Let’s look at each area.
Three ways sharing economy companies can cut costs
- Optimize insurance
Insurance is one of the biggest expenses for sharing economy companies – insurance and payment processing make up 14% of all expenses per ride, which means there’s a significant opportunity for improvement in this category.
According to The Wall Street Journal, “Insurers typically rely on decades of industry performance records to measure the likelihood of certain outcomes and adequately price insurance policies. With too little data, some insurers won’t issue a policy. For sharing economy upstarts, especially those like scooters that are essentially creating a category, this is an issue.
Compounding this problem, and the cost is the sheer volume of rides given – increasing exposure with higher mileage – combined with the lack of historical ride-sharing data, which hinders an insurer’s ability to assess what is an appropriate policy for these rides. Insurers tend to hedge their bets because they don’t have the insight to actual risk.
The diversity in sharing economy business models also means that there’s no standard policy. For example, from an insurer’s perspective, ride-sharing is very different than car-sharing. Questions that demonstrate their differences include, who owns the car? What are they driving and for whom, and how far are they driving? Are drivers using vehicles for personal or commercial use?
Mobility data enables efficient optimization of insurance policies for sharing economy companies to implement operational cost control in the long term. It allows these companies to…
- Implement predictive scoring based on historical data in similar industries to anticipate and reduce risk
- Use predictive scores to set a threshold to accept/reject drivers
- Attract, optimize, and retain the right drivers with mobility data-driven programs
- Identify and quantify driving behaviors via telematics and predictive variables
- Continually measure driving risk with the right analytics
Ultimately, mobility data can help insurers understand the risk profile of their drivers and what the company can do to reduce those risks. The alternative – increasing costs to cover the unknown – is simply not a viable option.
Sharing economy companies can also use the same mobility data that they share with insurance carriers to identify, quantify, and control driving risk based on what we expect them to cost over time as an active driver on the platform. We can also use this same mobility data to optimize driver’s fees.
- Pay Drivers According to Risk
Knowing how an individual drive and the risks (and accidents) correlated with those specific behaviors allows sharing economy companies to adjust their take rates – or apply tiered pricing.
In other words, risky drivers could be paid less until they demonstrate safer driving behaviors. Not only does this method free up dollars to appropriately insure riskier drivers, but it also incentivizes these people to participate in safe driving programs.
- Reduce Sales and Marketing Costs to Recruit the Best (and Safest), Drivers
By hyper-targeting recruiting efforts to focus on the best drivers for their platform, sharing economy companies waste less time on sales and marketing to the wrong people. It also may translate to less attrition down the road.
To accomplish this, sharing economy companies could partner with a company that evaluates multiple variables, including MVR or credit data, demographic data, and more about drivers in order to assess how their expected risk affects their potential lifetime value.
Why sharing economy companies need mobility data now
Rideshare competition is only increasing. And while market share is critical to survival, mobility data shows investors and stakeholders you can institute operational cost control and increase profitability. Ultimately, as sharing economy companies face pressure to increase ridership and customer loyalty, mobility data will only support these goals.
In the next section of this series, we’ll cover How Real-World Data and Analysis Will Help Sharing Economy Companies Diversify.
For more information
To leverage telematics and driving behavior insights to start cutting costs, contact Arity today.
Missed part 1 of the series? Read it here.
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