The Role of Artificial Intelligence in Business, and Why Uber, Airbnb, and Etsy Can’t Survive Without It [Infographic]

45% of the fastest-growing companies in the world will “employ” more smart machines and virtual assistants than people by 2018.

Indeed, nowadays we see more and more businesses using artificial intelligence in a variety of areas. And among the early adopters are industry leaders such as Uber, Airbnb, and Etsy - which are also the subjects of this article.

The importance of artificial intelligence in businesses like Uber, Airbnb, and Etsy cannot be underestimated. The famous sharing economy giants use AI in their core processes, like seller/buyer matching, pricing, and many more.

As these companies grew, they found it more and more difficult to operate efficiently and provide the desired level of service to their users. AI has already become the secret weapon that helps these three leaders stay competitive and there is huge potential to be explored in the future.

Let’s take a look at the key reasons why Uber, Airbnb, and Etsy won’t survive without artificial intelligence in business.

1. Consumers expect personalization Personalization has quickly become the new standard for modern eCommerce:

• 56% of consumers expect all of their interactions with brands to be personalized
• 74% of customers feel frustrated when website content is not personalized
• 78% of consumers will only engage offers if they have been personalized to their previous engagements with a brand

No one wants to sort through millions of listings at Airbnb to find the perfect place. We want to see exactly what we want when we want it.

The emergence of big data technologies was the first step towards mass personalization. So, this is how it started at Airbnb. They were using big data and a bunch of hard-coded rules to provide personalized search results and offers to their users. However, as their user base and geography grew, they quickly ran into limitations with this method.

Right now, both Airbnb and Etsy use AI to predict what listings users will like based on buyer profiles, past behavior, similar user likes, and much more. Airbnb takes 100+ factors into account, while the ML system decides what weights to assign to each factor for each user.

As for Uber, the app also provides a personalized experience by predicting address searches and offering shortcuts for the most frequent destinations. Uber also recently launched calendar and phonebook integration. Calendar integration is used to suggest meeting addresses and phonebook integration helps to locate friends that you want to meet (‘people are the new places’ feature).

The philosophy behind these beneficial additions is that Uber wants you to spend as little time in their app as possible (quite the opposite of most eCommerce apps). They want Uber to become great at predicting where you want to go and take you there as smoothly as possible.

2. Buyers want to pay less, while sellers want to earn more

A good bargain is among the top reasons why consumers choose a sharing economy platform.

63% participate in a sharing economy because it offers better prices.

But, on the other hand, there are also sellers that are looking to earn money. So how do you provide low prices to buyers, while letting sellers get their desired paycheck?

It is very common for sellers to find it difficult to price their offerings appropriately. As Dan Hill, product lead at Airbnb, says, their hosts often think their listings are worth more than they actually are.

But, they’ve managed to solve this dilemma with AI.

With the help of the AI-based feature, Price Tips, hosts get suggestions for optimal prices that will get their places booked with a nice profit margin.

Uber, on the other hand, is also attempting to solve its problem of low-profit margins with the help of AI. The company has recently launched Route-Based Pricing, a system that predicts how much riders are willing to pay based on their location, destination, where they are going and time of the day. In such a way, a rider going to a wealthy neighborhood may be charged more than somebody going to a poor neighborhood (provided the same mileage and time of day).

3. Online fraud is growing, while marketplace safety is expected by default

Participation in the sharing economy implies that users will be entering into transactions with strangers. No wonder trust and safety play such a huge role for users (learn more about this in our article ‘3 Trust Strategies for P2P Marketplaces’).

67% of consumers' fears about the sharing economy are related to trust.

Both buyers and sellers expect platforms to provide a safe environment for them to transact. According to research, trust in the platform is more important for buyers than trust in individual sellers.

However, online fraud is on the rise and it has become more and more difficult to fight it:

• eCommerce fraud increased to 33% in 2016, compared to 2015
• 68% of online businesses were anticipating more fraud in the coming year
• 62% of users experienced phishing & social engineering attacks

This is why both Uber and Airbnb started using AI to prevent fraud on their platforms.

First of all, Airbnb has developed its own Fraud Prevention Service that spots anomalies, recognizes patterns, and acts on them. Secondly, they’ve acquired Trooly, a background checking service that detects ‘risky’ users based on their digital footprint (public online data, including darknet).

Uber uses hard-coded rules for most of its fraud prevention tasks, but there are still things that they cannot do without machine learning models. Namely, they’ve developed a Locally Sensitive Hashing system to identify fraudulent drivers.

4. Efficient route planning is key to Uber’s survival

When the Uber app says that your taxi will arrive in five minutes, you expect it in five minutes. If it doesn’t arrive in time, it may mean that you’ll be late for your appointment.

Getting an accurate estimated time of arrival may look simple, from a user perspective, but it involves a lot of complicated background work. To enable this, Uber’s engineers have built Gurafu, an all-in-one routing engine that runs on AI. Gurafu is used for efficient route planning and estimating the time of arrival.

In addition to this, Uber also uses machine learning to determine the best pick-up places. This feature has resulted in serious time and fuel savings.

5. Autonomous fleets are the only hope for Uber to become profitable

Autonomous fleets can become 3.4x more profitable than just ride-hailing.

This makes sense because, with autonomous cars, you don’t need to pay drivers (duh!) and, in the case of Uber, drivers take the biggest share of the profits. This can be the ultimate salvation for the company that still hasn’t broken even.

But, out of the many companies that have joined the ‘driverless car race,’ no one has managed to come up with a finished product yet. Experts say it is only a matter of time.

The development of driverless cars relies on AI and deep learning and, in particular, the world’s most brilliant minds in the field of AI work who continue to build technology for autonomous cars.

Key Takeaways

These are just a few examples of the use of artificial intelligence in business.

As you can see, AI has already made its way to the core business processes of Uber, Airbnb, and Etsy.

They have incorporated machine learning, matching sellers and buyers, pricing, and fraud prevention. Moreover, Uber is among the top contestants in the driverless vehicle race. The company develops deep learning-based hardware and software to equip Volvo cars to go solo.

We are convinced that, very soon, AI will stop being something that only the big guys do. Especially with the emergence of solutions from Google and Amazon, which democratize technology. Thanks to them, we’ll see smaller companies adopting AI to transform their businesses.

If you are interested in further exploring how AI can be used in business, consider reading our article ‘3 Ways Machine Learning Can be Used in P2P Marketplaces’.

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Author: Kateryna Reshetilo is a Head of Marketing at Greenice web development agency. She is responsible for uncovering new market opportunities in the eCommerce, Healthcare and eLearning spheres. Kateryna also crafts and oversees the execution of marketing strategy at Greenice. When off-duty, she is a loving wife and mother of a cute 8-year-old boy. She is also passionate about climbing and windsurfing!