Every organization likely has data or potential data sources that are not YET being collected. Often, they recognize the importance of data science, but don’t fully understand its value or how to make sense of it. Through data science, we can glean useful information and uncover insights that can be used to understand an organization’s business and make data-driven decisions to improve its overall health and success.

“A successful company is never content to rest on its laurels – it’s always learning, adapting and growing – fueled by data and science.” – Neil Patel

Organizations that successfully generate business value from their data will outperform their competitors. Today, more companies are placing big bets on data and analytics – 90% attribute data science with driving innovation. And according to GE’s Industrial Insights Report, 89% believe a lack of big data adoption will create a risk of losing market share, and 75% cite growth as the key value of analytics.

But understanding an organization’s data, especially one with a lot of data, is no small task. Many struggle to develop talent, business processes, and organizational structure to capture real value from data and analytics. In fact, only 9% of companies can quantify the ROI data science drives. Let’s take a look at how some of the world’s biggest brands are leveraging data to fuel business growth.

Related: Why Every Company Needs A Data Strategy

Netflix Reduces Customer Churn by $1 Billion with Data

Netflix researchers found that a typical Netflix member loses interest after perhaps 60 to 90 seconds of browsing if they can’t find the right show. With 80% of viewer choices coming from recommendations, it’s essential Netflix gets this right. While it took years to develop, Netflix credits the combined effect of personalization and its content recommendation engine with reducing customer churn to the tune of $1 billion annually.

Delta’s Tracking of Customer Sentiment Leads to Improved Customer Experiences

To get a finger on the pulse of their customers, Delta regularly monitors Twitter to understand how customers react to delays, upgrades, in-flight entertainment, lost luggage, and more. It became evident to the company that lost luggage was a big pain point for customers, and Delta knew they had to respond. So to solve this, the company previously developed a plan to send a customer support representative to a passenger’s destination with a free first-class upgrade along with baggage tracking information – a nice gesture, but hard to maintain at such a large scale. Because Delta sees more than 130 million checked bags annually, they decided to leverage data and technology to solve this huge pain point. Now an industry leader, Delta became the first major airline to use RFID-generated data to let customers track their own bags from a mobile device. The app has now been downloaded more than 11 million times.

UPS Orion Optimizes Logistics with Big Data

The supply chain and logistics industries are reaping the operational efficiencies derived from big data to optimize routes, delivery times, and delivery methods. With more than 4 billion items shipped annually on 100,000 vehicles, UPS has vast amounts of data it can leverage to manage fleet optimization. UPS launched Orion to track data from on-truck telematics to understand route optimization, engine idle times, predict when fleet maintenance is required, etc. The company has saved more than 39 million gallons of fuel and has trimmed 364 million miles from drivers’ routes since adopting Orion, saving more than $300 million annually.

Airbnb Improves Conversion through A/B Testing

With a philosophy of continuous improvement, Airbnb regularly tests variants on its user experience – so much that it has built its own A/B testing framework rather than use off-the-shelf testing solutions. In 2014, Airbnb noticed that when users from certain Asian countries visited the homepage, they bounced quickly. The company’s data science team dug into its data to understand these users’ behavior, noting that users often got lost browsing photos of beautiful neighborhoods and never completed a booking. The data science department worked closely with engineering to create a redesigned version for testing and showcased top travel destinations in China, Japan, Korea, and Singapore. As a result, users in these countries completed 10% more bookings.

Orbitz Leverages Data to Deploy Dynamic Pricing

Dynamic pricing (a.k.a charging some customers more for the same services other customers pay less for) has been around for a long time, but with the advent of data science, computers are making it even easier to determine a customer’s threshold for paying a premium price for products and services. The Orbitz team of data scientists uncovered a unique insight – that hotel searches made from a Mac computer resulted in a 30% higher than average booking. The company used this information to target anyone coming to their site via a Mac with a higher price point than PC users receive, resulting in an overall lift in revenue.

These are successful examples of how when leveraged well, data science can uncover new questions and test new ideas that drive better business outcomes. But what if your organization isn’t quite there yet? That’s ok! That’s where stable|kernel comes in. Our data scientists will work with you to assess your organization’s existing data sources and architecture. We’ll research additional data sources for incorporation and find opportunities to improve your existing architecture as well. Once a centralized system is in place, we’ll build tools on top of that data to improve internal processes and customer interactions, and ultimately optimize and automate your systems to meet your company’s business objectives. Interested in learning more about creating a successful data strategy? Get in touch! We’d love to help.

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