When it comes to consumer services, convenience is king. If you want customers to buy what you’re selling, make it easy for them. Figure out what their pain points are, and nix them from your business equation. Identify their proclivities, and align your brand with them. Anticipate their needs, and deliver them. Sounds difficult, right? Well, It doesn’t have to be.

These days, the best business models aren’t necessarily about who works the hardest, but rather, who works the smartest. That’s really what Big Data and analytics is all about – and it’s why the following service models have proven to be so wildly successful:

Uber: Location, location, location

Thanks to GPS and street data, Uber always knows where its drivers are. The second customers request a ride, it knows where they are, too. An algorithm will then match drivers and customers up based on fastest estimated time of arrival, being sure to factor in extraneous conditions such as traffic and weather. A driver with one mile of gridlock between him and the customer will arrive later than a driver with nothing but two miles of empty road ahead. Uber Pool functions the same way, but with the added variable of extra customers.

The most ingenious part of the model, and what ultimately helps the company be so lucrative according to Data Science Central contributor Bernard Marr, is that the fare is based on the time of the journey rather than the distance. This is what allows Uber to implement surged pricing. Whereas traditional cabs would typically charge a flat rate, Uber’s algorithms shift at a moment’s notice according to traffic conditions – not unlike hotels and airlines during peak travel times.

Finally, Uber’s algorithms help ensure quality service by analyzing review data that customers input at the end of a ride. It also calculates the percentage of accepted and declined ride requests. The company encourages an 80 percent acceptance rate, which is really just a way of ensuring that drivers make themselves more available to customers.

GrubHub knows how much like you like eating chicken wings in bed.GrubHub knows how much you like eating chicken wings in bed.

GrubHub: Decisions, decisions

Restaurant discovery services have been around for a little while, but not in the way that GrubHub has. Once a modest Chicago startup, the company now partners with restaurants all over the world to help customers identify choice meals that they might not have known about. Big Data is aggregated, analyzed and sorted, allowing users to identify restaurants in their area according to price, type of cuisine, whether or not they deliver, etc.

It sounds a lot like Yelp, and there’s certainly overlap between the two. However, GrubHub’s CEO has emphasized that “data” rather than “content” is really what differentiates them from the competition. GrubHub does use a qualitative, open-ended review system, but it also relies on data gathered from its robust online ordering and delivery system to glean specific insight into its customers’ preferences and behaviors.

As a result, the company can tell you stuff like this with near certainty: Women are 63 percent more likely to order a dish with beets, while men are 54 percent more likely to throw in for a 2-liter soda bottle … at 2 a.m. … on a Sunday.

Amazon Prime: ‘But I want it now!’

Amazon Prime Now is Jeff Bezos’ gift to the Veruca Salts of the world. The same company that patented free two-day shipping and its very own version of Black Friday, called Prime Day, has now added free two-hour shipping to its online retailing repertoire – or 1 hour shipping if you’re willing to fork over an extra $8 dollars. It works like this:

“Within 58 minutes, a man arrived with the requested chocolate orange in tow.”

Existing Prime members visit the Prime Now webpage or smartphone app, enter their ZIP code, receive a list of the different types of items that they can have shipped to their door within two hours, place their orders, and voila: shopping complete. WIRED’s London office put this to the test in 2015, shortly after it became available in the Old Smoke. Within the first 58 minutes of a 2-hour delivery window, a man known only as “Anderson” arrived with the staff’s chocolate orange in tow.

On the operational side of things, Big Data plays a big role in making it possible to deliver goods to hundreds of thousands of customers within two hours. The magic happens in the company’s warehouses.

“We have high-tech algorithms that we have taken from our normal fulfillment centers, and we use them in this smaller building,” Stephenie Landry, Amazon’s worldwide director of Prime Now, told TIME. “It takes the picker on the fastest path possible to grab all of the items.”

Progressive: What’s a mile worth to you?

“The selling point here is personalized, behavior-based insurance.”

Auto-insurance companies have been trying to answer this question for years for the sake of establishing precise policy premiums. They’ve performed extensive statistical analysis in an attempt to figure out which of their customers pose the biggest risk behind the wheel. As any male between the age of 16 and 25 is aware, this has more or less resulted in profiling.

In a bid to assess risk less superficially, Progressive has begun offering usage-based insurance, also known as pay as you drive, or pay how you drive. Called Snapshot, the plan uses a small device that collects telemetric data. Comparative analysis of this data can then predict the likelihood that a customer will get into an accident based on certain road behaviors.

Shrewd customers might think it’s another excuse to raise your rates. But usage-based insurance is optional, and seems marketed to drivers who want measured behavior on the road to be rewarded with lower rates.

Obviously, some cars cost more than others, and that’ll still be a factor in auto insurance. Likewise, proximity to certain locations increases the likelihood of a stolen vehicle. That said, the selling point here is personalized, behavior-based insurance. An owner of a sports car who drives like the Queen of England’s chauffeur might get a bit of a break on her rates. The lady driving her grandmother’s 1997 Nissan Sentra like Vin Diesel in “The Fast and the Furious” franchise, will not. And it’s all thanks to predictive analytics.

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