7 real-world examples of how brands are using Big Data analytics

In the last 2 years, 90% of the world’s data has been created and businesses are spending more than $180 billion a year on big data analysis.

Even though the numbers are high, there are still companies that don’t use this as an opportunity to start investing in Big Data. If you are one of them – take a look at how some of the big guys are doing it and how it’s giving them an advantage on the market.

But before we start – what exactly is Big Data?

(and how is it different from “small” data)

The term Big Data has been around for some time now, but it has taken a whole new meaning today when people share 500 terabytes of data per day just on Facebook. And on YouTube, there are over 300 hours of videos shared every minute!

Based on those facts, we can say that users are constantly offering their data to companies. But why do companies need so much data?

Data can help them with many things, but most importantly, it can help them identify new business opportunities which can generate more sales and create a better customer experience. It also makes decision-making faster and solving problems more efficiently.

As for the difference between Big Data and the rest of the data – it lies within the 5 Vs.

  1. Volume
  2. Velocity
  3. Variety
  4. Veracity
  5. Value

When it comes to volume, the word itself says it all, Big Data is exactly that – big. When we talk about velocity, we talk about how fast we can gather the data we need – it is incredibly important to have real-time data at any time to make better business decisions faster.

Once we’ve accumulated data, we need to understand the different types of data there are (variety).

When we talk about Big Data, we talk about its three forms:


Which is easy to store and analyze, and can be created by a machine or by humans (accounts for up to 20% of all of the collected data).
Examples: address details, numerical ratings by customers, demographic information…


It’s more difficult to analyze and search for and, even though it accounts for a much larger percentage, people usually disregarded it – until AI was able to make it easier to process.
Examples: photos, videos, audio, social media content, open-ended surveys…


This is a mix of structured and unstructured data. It includes parts that can be easily organized, but it also includes data that is hard for a machine to sort out.
Example: an email message — unstructured data is the text within the email, while structured data includes the name of the recipient, time sent, email address of the sender…

Once you have the data, it needs to pass a credibility and quality test (Veracity), and last but not least the data needs to be useful for your business – it needs to give you information that will provide some benefits to your business (Value).

Now that you know the traditional explanation of what Big Data really is, the best way to actually understand it is to see how it works in real life. Let’s take a look at how some of the world’s largest brands made Big Data a part of their core business.


Amazon is definitely the number 1 e-commerce shop at the moment and they have their database to thank for that. They are constantly using big data to improve their customer experience, so here are two examples that show just how well that works.


Everybody knows that airlines use this tactic when selling plane tickets – if you check out the same tickets over and over again, it probably means you really want them and are willing to pay more for them. That same logic is implemented on Amazon’s website. But what you probably didn’t know is that they change their prices up to 2,5 million times a day.

What affects these price changes are factors like shopping patterns, competitors’ prices, and whether the product is a common one or not.


It doesn’t matter if the person buys the products, puts it in the cart or even just takes a look at it – Amazon will use that data. That way they can learn what each customer wants and likes and can recommend that same product or similar ones to them when they return to the shop.

This is how the company earns 35% of their annual sales.

The Marriott hotels

The hospitality industry has been growing in the last few years and will definitely continue to grow. Marriott hotels, one of the biggest hotel chains in the world, are one of the leaders in the industry, so let’s take a look at how they use Big Data to generate more revenue and gain more loyal customers.

Just like Amazon, Starwood hotels (one of the brands under Marriott hotels) also use dynamic pricing. It is changing based on a variety of factors such as local and global economic situation, weather, availability and reservation behavior, cancellations and many others – this tactic resulted in a 5% revenue increase per room.

They go so deep with utilizing data that they even follow if famous musicians are playing at Madison Square Garden so they could adjust their rates at their nearby hotels.

To create a better customer experience, they also started testing out facial recognition check-ins which looks like a win-win scenario – their guests don’t need to wait at the reception desk anymore, and the hotel gathers even more valuable information. Another element they implemented to gather data is putting Amazon Echos into the rooms – this allows guests to make Alexa handle everything that was previously handled by the reception staff. Now guests can get all the information they want, while Marriott gets the knowledge of their customer’s preferences, needs and potential concerns.


Netflix is unarguably the biggest online platform for streaming movies and TV shows, and it owes its success to Big Data. Because they know their users, their retention rate is 93% which, compared to their main competitors, is a huge number. They are also growing rapidly because of their original movies and TV shows that just show that they actually listen to their consumers.

And because of that, this year they were able to compete with traditional giants and win two Golden Globes and two Oscars. And it looks like this is just the beginning!

So, how do they use their data to create such a good customer experience?
They collect data such as the time during which their subscribers watch the show, if they binge-watched it or it took them some time to finish, did they pause the show and if they resumed it after pausing…

And all of this is used to create personalized accounts for each consumer!

Their goal is the ultimate personalization and that is clearly shown through their plans for the future which include using AI to create trailers – because why should everybody get the same one? They plan on creating trailers that are fully personalized for their viewers – for example, if a specific user loves romance movies, they are more likely to watch a non-romance movie if the trailer is filled with romantic scenes. And that is just the tip of their plan.

Netflix will be able to achieve this through machine learning, and even though machines still can’t understand human emotions, it is something we can expect in the near future. And when it comes to the bottom line – this will greatly reduce the cost and time needed to create the trailer and put Netflix on the right path to achieve their goal of ultimate personalization.

Uber Eats

For a couple of years, Uber has been the leader in the taxi business and nobody was surprised when they announced they will be expanding their services – from driving people to delivering food.

They entered this saturated market of food delivery and, thanks to the data they collected from being a taxi giant, they are here to stay.

They wanted to be recognized as a delivery that always brings food while it is still warm, so they tried to model the physical world in a way that would allow them to be as accurate as possible when predicting the time of food delivery. To make this endeavor work, they also collected data on how much time it usually takes to prepare a certain meal, so they could pinpoint the exact time when the delivery person should come and pick it up.

This action allows the drivers to pick up more meals on their way (as they don’t have to wait for the food to be prepared) and Uber is encouraging them to carry more than one meal per trip with a bonus for each meal they collect.

This, however, is not something that has never been done before, but by using data, they are doing it better than anyone before. I mean, they are going deep with this that they even employed meteorologists to help them predict what the weather will be like and how it will affect the delivery.

What Uber Eats is doing is a textbook example of how Big Data and data analysis can help businesses expand their services and give them a clear advantage over their competitors.


The food industry and trends are always changing, and if you want to be able to stay at the top, you need to be able to change with them. And that is exactly what McDonald’s is doing. With the rise of the trend of healthy living and using online ordering, fast food restaurants were faced with a problem.

That is when McDonald’s started to turn to the data they collected over the years. They wanted to transition from mass marketing to mass customization – in order to do such a thing, they needed to unlock the data in a way that is useful to customers.

What they came up with was a drive-thru with digital menus that change based on a variety of factors – from the time of the day to weather, and to historical sales data. That way they can offer their clients a cold beverage on a hot day or maybe a coffee with their breakfast menu.


Starbucks is a global brand with a famous logo and the infamous activity of writing the wrong names of their customers on cups. As I previously mentioned, personalization is the key to growth nowadays, and that is exactly what Starbucks is doing – they are using Big Data to create a better customer experience!

The way they collect data is by providing their customers with Starbucks rewards programs and mobile apps which help them learn more about the buying habits of each of their customers.

Starbucks is then using that data to recommend products to their loyal customers, create better marketing campaigns and new menus, as well as decide where they’ll open their next store. This system is so organized that it will offer their customers products based on the season, weather and location they are at.

They also send out personalized emails with offers to customers who haven’t visited the store in a while, so they can re-engage them or send them discounts.


All the companies mentioned above use their data internally, but can the big data you have be turned into a service? Well, Accuweather did just that.

In the past, their partners were exclusively global brands, but they realized that many other businesses could benefit from their weather data as well. That is why they created an online platform for developers where they could purchase API keys and implement them in their own projects/businesses.

What can you pick up from this article?

Sure, not every trend is worth jumping on, but big data is no longer just a trend. This is a proven thing that works if you truly want to be able to understand your clients and transition your business to the next level.

Today, in order to generate more sales, you need to be able to really listen to your consumers and treasure all the data they provide you with. And with the advancements in machine learning, even smaller companies can use Big Data to improve their businesses.

However, with all the previously mentioned elements getting easier and more available, the influx of data sources and ways to analyze and present data has made the process of defining the right business strategy around big data more difficult – but that is the topic for the whole new article.