Why is data analytics important?
If you're wondering what data analytics looks like in action, let's start with a scenario you may be familiar with: before making a large purchase, it’s normal to research and collect insights into what choice is best for you and your price range. Now think about making a similar decision, but this time, millions of dollars are involved. You’d want to take the time to collect as much information as you possibly could to ensure you make the right choice, right?
Companies of all sizes make daily decisions that have big impact on everyone, from employees, to customers, to shareholders. So, how do they face the pressure of making these decisions? None of them would dare do so without the help of data analytics!
What is data analytics?
Data analytics involves collecting, organizing, and drawing conclusions from sets of data. Data sets can include anything from customer demographic info, to purchase history, to social media engagement, and more. These findings are typically used to help a company improve their products, processes, and marketing strategies.
From online retailers like Amazon to sports organizations like the NBA, the importance of data analytics is bigger than ever in helping organizations move forward. Here are some cool examples of companies using data that show off its impact across all industries!
Netflix
If you find yourself addicted to bingeing a Netflix show, your viewing habits may be a direct result of the good work done by data analysts.
Netflix captures user data such as time you spend watching a show, if you watched consecutive episodes, and what kind of movie genres you lean towards watching. This data is then used to recommend new shows to not only you, but to producers and executives. For example, if data analysts find that 90% of people who checked out a specific Netflix show ended up marathoning the latest season, it gives executives all the more reason to renew the show for another season and keep viewers hooked.
Amazon
As a trillion dollar company and a world leader in online retail, Amazon makes daily decisions that have a ripple effect on how the internet marketplace operates and interacts with customers.
Amazon uses big data across all of its properties, from Amazon Echo to Amazon Pay, to improve the customer experience. The company has become a known industry leader in tracking and capturing behaviour analytics, including customer purchasing patterns, most searched items, and products in your cart. This data helps the company market related and similar products to people browsing. Impulse purchases based on these suggestions account for 35% of the company's annual revenue.
Uber
Since its launch in 2009, Uber has revolutionized transportation and how we get around. With competitors like Lyft popping up, Uber’s existence and prosperity heavily relies on good data.
Data is behind every decision Uber makes, from surge pricing to estimating fares and driver ratings. Uber has data on every single GPS point and information about every trip you’ve taken using the app. With this, Uber can do things like match you with the most suitable driver based on your past trips to ensure another enjoyable user experience and keep you using the app in the future.
NBA
Data analytics have become an integral part of the sports landscape, and the NBA is no exception. The league collects and analyzes data in various ways, ranging from tracking the details on player performance to anticipating injury risk.
One area in which data has become particularly useful in the NBA is in scouting players. Executives now have the ability to look up specific player statistics and data (e.g. percentage of blocked shots with a players right hand) to see areas in which a player could improve or is particularly strong in.
This data helps coaches and scouts make the right decisions when it comes to drafting and developing players. Drafting a player who is struggling in areas in which you need them to thrive can be a costly mistake, which can set a team back years or cost them important games.
Starbucks
Ever go to order your favourite drink at Starbucks, only to see that it’s been removed from the menu? That’s because you’re in a small minority of customers who order the drink regularly. And Starbucks knows this because of the data it collects and analyzes on its product and customer behaviour.
Starbucks uses its mobile app to collect data on your most frequent orders and suggest recommended items for purchase based on your likes. The company also uses this data to create marketing campaigns, promotions, and decide on which menu offerings bring in the most revenue. If this data wasn’t being tracked and analyzed, Starbucks could potentially be losing millions per year on unpopular menu items or missed marketing opportunities.
North Face
More than just a warm jacket, North Face is at the forefront of fashion and data analytics. Using their customer loyalty program, North Face is able to track and analyze information about repeated customer behaviour. How often do they shop at the store? Which items do customers buy together? And is positive brand interaction more likely to turn customers into repeat customers? North Face was able to answer all these questions and more via their customer loyalty program in order to do a better job at marketing their product and increase sales.
Looking to learn data analytics?
Our beginner-friendly Data Analytics course will teach you the basics, fast. You'll learn to make data-driven decisions at work, optimize your workflows, and better understand your customers with in-demand tools and languages like Google Sheets, SQL, and Tableau. See what you'll learn and build below: