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Disruptive innovations typically begin by bringing a product or service to customers who are outside of an existing market because they lack the means or knowledge necessary to access and use incumbent solutions. They offer new value to customers; in spite of providing lower performance along the measures that have historically been most valued in relevant markets, they’re simpler, more convenient -- and cheaper.
UberX -- Uber’s budget-priced option for hiring a ride -- is a classic example of this. Uber’s marketplace -- less skilled, but good enough drivers -- have entered the driving-for-hire business. Essentially anyone with a licence, a car, requisite insurance, and the ability to pass a background check, can get on Uber’s platform and offer on-demand car services. UberX customers trade off using the “expert” value of taxi drivers’ experience, and knowledge of local roads, in favour of the lower price and greater convenience that UberX offers.
In disruptive innovation, it is the business model, not the technology in and of itself, that is disruptive. The same technologies can be deployed in ways that are disruptive, or in ways that are not, depending on the business models built around them. Looking at the use of GPS technology and mobile computing by Uber and by existing taxi companies illustrates this point: in addition to the technologies behind Uber’s matchmaking, pricing and rating platform, the near ubiquity of mobile computing and mobile-based GPS enables the disruptiveness of UberX.
In a world before reliable access to GPS technology, it would have been too risky for consumers to trust that any person with a car and a driver’s licence could get them where they needed to be -- the chances that the prospective driver didn’t know how to get to the desired destination would have been prohibitively large. In that world, a taxi driver’s intimate knowledge of local neighbourhoods was very valuable. Instead, in today’s world, this advantage is being completely commodified by GPS.
Yet the same technology, in the hands of taxi drivers, is used very differently. Every taxi driver has a mobile phone with GPS, and many have dedicated GPS-devices suction-cupped to their windscreens. But unless you ask to be taken somewhere that the driver is unfamiliar with, and that you can’t give directions to, they’ll go unused.
The coincident commoditisation of a core incumbent advantage is a fundamental factor in the speed of UberX’s success. Another example of where we’ve seen this phenomenon in the past is with Intuit’s TurboTax product. Taxpayers and accountants all had access to the same PC-based technologies. But by embedding the decision rules that had been part of tax accountants’ expertise into their software, Intuit commodified accountants’ core competence, so many taxpayers switched to the cheaper, more convenient option that TurboTax represents.
Much has been made of the way that some municipalities have reacted to the introduction of Uber into their markets. Heavily regulated industries tend to have much less innovation, so new innovations often occur in adjacent, unregulated spaces. Eventually, regulators acknowledge that customers have already “voted with their feet”, and they adjust accordingly. We see this regularly in healthcare, for example.
Consequently, innovators hoping to bring change to heavily regulated parts of the economy should find ways to target customers neglected by the incumbents operating inside the walled garden of a heavily regulated environment. By instead developing a model that targets the non-consumers of a product or service, innovators have the chance to develop a business model appropriate to benefitting their customers (rather than one that is optimised for a regulatory regime).
Regulators, in turn, should be mindful of these dynamics. Because new innovation will every so often take root in unregulated parts of the market, regulators should look to those niches to prepare themselves for what is coming, rather than trying to fight the market. Regulation sometimes must have a role, but that role has rarely been effective when it has come to picking winners and losers.
This article was originally published by WIRED UK
