Uber’s local knowledge gives it global drive

Uber’s assault on the developed world’s transport networks has drawn hostility and investors alike. It has also sent columnists and chief executives scrambling for the formula that has powered the company’s explosive growth.

Uber’s assault on the developed world’s transport networks has drawn hostility and investors alike. It has attracted US$1 billion in investment capital against an implied valuation of $18bn. It has also sent columnists and chief executives scrambling for the formula that has powered the company’s explosive growth.

Talk to a customer and they’ll tell you that Uber is easier and, in many cities, cheaper than its rivals.

But that’s exactly half of the story. It’s on the supply side where things get interesting.

Firstly, Uber treats a city’s transportation system as a big maths problem that can be solved. Existing systems treat getting a taxi as an imprecise art, and a matter of luck and judgment.

Imagine you are an omniscient city planner, able to allocate cars at will, and that you have a lot of information at your fingertips. At a glance, you can see the number of cars on the road, their locations, the locations of customers, average journey time, average prices per distance travelled, peak usage hours and destinations, a host of standard deviations, and so on.

If you had the processing power, you’d be able to make lightning-fast allocation decisions about where to send cars.

You’d be able to predict with great confidence when cars are busiest, and you’d be able to make your cars sing with efficiency by boosting the utilisation of your fleet.

Now compare this to the status quo: individual taxis stumble across customers both by habit, luck, and occasional missives from the central office, where a customer booking has wound its way through a call centre.

I’d invest my money in the omniscient city planner.

But this isn’t the clever bit. Uber doesn’t actually have any cars or drivers, in the traditional sense. Instead, it simply incentivises local freelancers and existing car companies to use the app.

That means that no Uber workforce is the same in any country: the company’s fleet reflects local market conditions wherever it goes.

This is the key to the success of globalised companies. The Catholic Church, one of the first multinational corporations, staffs its dioceses with local priests, all the better to deploy the localised, homely imagery needed to keep pews filled. Subway says it changes its menu based on the “cultural tastes, customs and religious preferences” of its customers.

Sociologists call this trend glocalisation: a hideous portmanteau used to excuse the existence of McSushi.

But a key difference between Uber and traditional multinationals is that, in the latter, product and organisational differences are the result of specific management decisions.

Localisation is built automatically into Uber’s business model. Existing car operators want the larger volumes brought by Uber, and so sign up. Uber gets its localised fleet, with its local drivers. When Uber grows, the suppliers feeding it grow.

The data that fuels Uber can also boost car companies. If UberBlack, the premium app, is more popular than UberX, the discount offering, car companies know what kind of cars to add to their fleets. Driver ratings can help companies to reward their best performers.

Not all is sweetness and light at Uber HQ. One executive reportedly used the app’s data to track a reporter he disliked. The company faces a host of legal, regulatory, and public relations battles in New York, Toronto, London and a range of major European cities, as a broad coalition of opponents, from feminist activists to concerned taxi drivers, seeks to prune the company’s growth.

But Uber is hydra-headed. It takes varied forms across different jurisdictions. Its opponents will find it all but impossible to fight and win battles across the world. Uber has the luxury of fighting its opponents one market at a time, with US$1 billion of ammunition to spend on fancy lawyers.

Besides – as its users will tell you, it’s easier and often cheaper.

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