Uber vs. Lyft: A Tech Strategy Should Mirror the Business and Never Stop Evolving

Follow us on LinkedIn for our latest data and tips!

Uber vs. Lyft: A Tech Strategy Should Mirror the Business and Never Stop Evolving

As far as the general marketplace is concerned, Uber and Lyft are competitors who do the exact same thing. But for technologists, the two companies approach development, infrastructure and technology in distinctly different ways. Their strategies provide a series of lessons for organizations on why technology deserves a personalized touch when it comes to driving business growth.

Consider regulatory filings for Uber and Lyft. Lyft is all about the transportation services business. Uber is more ambitious, with plans to build a marketplace and technology platform that moves beyond ride sharing transportation with additional offerings like Uber Eats and Uber Freight.

The Right Partner is Everything

Both Uber and Lyft are heavily invested in the cloud, but Uber employs a hybrid approach with a highly automated infrastructure, co-located facilities and partnerships with multiple cloud vendors. The company uses third-party cloud computing services and global data centers to quickly and efficiently scale handle business needs without compromising its focus on building better products or taking on additional costs.

Lyft, on the other hand is betting big on Amazon Web Services for its cloud architecture, with spend estimated at some $300 million over the next few years to ensure it can withstand business surges.

The Power of Machine Learning and AI

Uber mentions artificial intelligence, machine learning and the word algorithm numerous times in its IPO filing. Why? Data science will factor heavily in its business plans. Consider, to leverage the company’s research, patented technologies, open source software and data from countless customer trips, it will need machine learning and artificial intelligence to automate and verify millions of data points each year, and ultimately help make better market decisions.

Lyft isn’t as forthcoming about which tech it plans to use in its IPO filings, but the company does mention that rides “inform our machine learning algorithms and data science engines.” Data insights likely facilitate a better, more efficient ride experience for its customers. They probably help the company match local market demands, optimize routes, etc. as well.

Technology as Business Enabler

Uber has built its own proprietary payment, routing and marketing technologies using real-time algorithm-based decision-making for Uber Eats, Uber Freight and its other offerings. The tech has influenced quite a few of its marketplace capabilities. From its ability to use trend data to predict supply and demand, to algorithms that evaluate variables like weather, time, distance, traffic, and meal prep times for Uber Eats, the company has made technology a key driver in not only how it operates, but its plans to grow and continuously improve business.

Lyft also uses historical data and machine learning capabilities in many of the same ways to predict driver availability as well as allow it to match rider supply and demand. Algorithms and data science will help to inform service levels, increase efficiency, facilitate product development, even increase driver earnings.

Never Stop Evolving

Business and technology have one important thing in common: They should never stop evolving. Both Uber and Lyft know that continuous improvement is key for their success, and technology is a key enabler.

Uber uses DevOps and agile software development to keep both tech and business fluid. The company embraces experimentation, testing, prototyping, and constantly refines its products to ensure its customers get the best possible service. City districts or neighborhoods become ideal focus groups with which to gather feedback, try out new features, increase performance, gauge how they’re doing, and of course, make any corrections necessary.

Lyft’s business model organizes product and development teams that integrate data science, analytics, design and engineering. The company also uses a lot of open source software, which is often quite innovative, as it exists outside the constraints of normal tech and software development cycles and is continuously developing and improving.

Both companies are very future-forward. For instance, Uber and Lyft are both investing in autonomous vehicles, which may impact their transportation networks substantially in the not too distant future. The companies approaches differ, however.

Uber’s Advanced Technologies Group is focused on autonomous next gen transportation, and the company is partnering with well-known manufacturers like Toyota and Volvo. Lyft’s open autonomous vehicle platform will connect its network with partners.

For example, Lyft partnered with Aptiv to deploy a fleet of autonomous vehicles in Las Vegas. The program has logged more than 50,000 trips since it began last year. In addition to partnerships, Lyft is also building its own autonomous vehicle system in hopes that a combination of partnerships and intellectual property will be a competitive advantage in the marketplace.

The cloud, AI, and machine learning will factor heavily in both Uber and Lyft’s future business growth and operation plans. They will need talent with advanced level skills in all of these technologies, and the marketplace likely won’t be able to provide that talent at the volume these companies – and others – will need. That means they’ll have to leverage another kind of development partnership to build the skills they need: a learning and development partnership.

Business strategies and technology don’t mean much without skilled talent to execute and deploy them. Active partnerships with experienced, proven training organizations like DevelopIntelligence will be invaluable not just for disruptive, industry-bending companies like Uber and Lyft, but for any organization that plans to move in step with the times and use technology to grow in the global marketplace. Training is like business and like technology – to be effective, it must never stop evolving.