Three Industries Being Disrupted By Data Products

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Sundeep_RPMIn this special guest feature, Sundeep Sanghavi, Co-founder and CEO of DataRPM, identifies and discusses three industries and they’re being disrupted by three popular data products. Sundeep is an award winning, industry pioneer in Cognitive Data Science for Recommendation and Prediction data products.

In the coming years, the key factor that will determine whether a company fails or prevails will be based on how it operationalizes data into business workflows. This is already being seen in the accommodation, delivery and movie streaming industry, as the successful companies, such as Netflix and Airbnb, are the ones who incorporate data into their business models.

This is opposed to analyzing  data manually for exclusively one project, which requires a large team of data scientists and lots of time. In the coming years, companies who use this model will face a predicament. McKinsey and Company predicts that by 2018, there won’t be enough data scientists in the U.S. to go around.  Demand will outstrip supply, and the rising cost of talent will price smaller companies out of the data market. Companies won’t be able to hire talent to achieve their data goals, and will not be able to sustain themselves in current fashion.

To stay afloat, companies need to use data science concepts to create data-product-driven business models. This means leveraging data through cognitive computing, which uses machines to select and tune algorithms to quickly deliver recommendations and predictions. Companies who use cognitive data science power to operationalize data into product will be the new generation of data driven business, and even now are out-smarting their competitors. In this article, we will explore three industries disrupted by cognitive data products, while arguing that a technology company’s use machine learning to predict and recommend is the key to its success.

The Accommodation Industry: AirBnB vs. Hotels

Airbnb is only eight-years-old, but is proving to be a huge disruption for the hotel industry. In fact, once travelers have lodged in peer-to-peer accommodation, they are less likely to stay at a hotel the next time they travel, according to a new Goldman Sachs survey.

Airbnb is completely data product driven. It doesn’t just use data, but has incorporated it into its business model. Riley Newman, head of data science at Airbnb, told VentureBeat he thinks that data is the “voice” of a customer. It’s a good way of thinking, and is clearly working for the now $25 billion company. Airbnb uses data to be proactive and enhance customer experience. The data helps to identify problem areas on the platform, such as certain links that distract customers from booking, and also aids hosts in choosing the right price ranges for their accommodation.

Riley explains to VentureBeat that Airbnb is always trying to make the site more intuitive and useful. He recounts that in 2014, it became clear that users in some Asian countries would become distracted on the site. Many would click on the “Neighbourhoods” link, search through photos, and never end up make a booking. Using this data, Airbnb redesigned the homepage for people in these areas by removing the link. It saw a 10 per cent higher conversion rate from their customers in Asian countries as a result.

In June 2015 Airbnb launched Aerosolve, which it called machine learning for humans. It allows hosts to better price their listings based on user data. Aerosolve lets hosts interact with a simplified version of data, and gives them explanations on why demands may be higher or lower than expected.

“We’re trying to equip, empower our hosts so they are able to price the listings, get bookings, and do it seamlessly, effectively, so we have more hosts and more stays on Airbnb,” Airbnb data scientist Bar Ifrach said at a company OpenAir conference in June.

Airbnb leverages data and machine learning to create a more comprehensive booking experience for hosts and customers alike, and it’s a model the traditional hotel industry is frightened of. A customer survey slipped under a hotel door does not get to the root of a customer’s behavior, and simply cannot compete with data collected through machine learning.

The Delivery Industry: Rapidus vs. Traditional Couriers

Rapidus is a crowd-sourced on-demand delivery service. The service is like Uber, but for packages. The company has incorporated machine learning into its business model and collects data about a driver’s journey. This predicts and recommends the most efficient routes drivers should take in real time, even if the package delivery changes.

Rapidus is quite new to the scene, but the service and its use of machine learning is likely to seriously disrupt the traditional delivery industry. Although the big players use data, they don’t use it intelligently, and will have a hard time collecting as much useful data as Rapidus through machine learning. It’s something that means the company is on route to turning traditional delivery on their heads.

The Movie Rental Industry: Netflix vs. Other Streaming Services

Netflix has an attractive business model. It’s one that gives subscribers the ability to watch unlimited movies and shows, on-demand and from home for a small monthly fee. But this isn’t Netflix’s unique selling point; the company certainly has its share of competitors. Rather, it’s Netflix’s use of big data and machine learning that keeps customers coming back for more. Netflix recommends and predicts the best matches using data products, instead of engaging in individual, lengthy, and talent intensive data projects.

Netflix wants to know when each of its 75 million streaming customers pauses, rewinds, and fast forward. It wants to know which day of the week a user watches. It wants to know what zip code a user is watching from, and browsing and scrolling history. Netflix even analyzes what goes on in its content, to consider what colors and scenery a viewer likes, for example.

Netflix estimates 75 per cent of what people watch is driven by recommendation. But machine learning also helps Netflix in creating its own content. The decision to create original series, like House of Cards for example, was driven by algorithms.

“We have a high degree of confidence in House of Cards based on the director, the producer and the stars,” said Steve Swasey, Netflix’s VP of Corporate Communications to Gigaom in 2011, just after House of Cards was released. “Through our algorithms we can determine who might be interested in Kevin Spacey or political drama and say to them, ‘You might want to watch this.”

The enormous popularity of Netflix is a strong indicator of how having a powerful data-driven model can continue to prevail as the largest movie streaming service on the market.

It’s a company’s use of machine learning that leads them to rise above and prevail in  data-driven industries. The companies are able to collect data faster and easier than their competitors, and are able to predict and recommend to offer a better product or service. It’s something all companies need to incorporate into their business models, or else, they’ll sink fast.


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