How Companies Are Using Big Data and Analytics to Succeed

How Companies Are Using Big Data and Analytics to Succeed

Companies have great amounts of data at their disposal. And the volume of this data worldwide grows year by year. By 2025, we can expect it to reach 181 zettabytes:

But what can a company do with all this data? How are different businesses using big data, and what does it help them achieve?

Let’s answer these questions and take a look at some interesting case studies.

Benefits of Using Big Data Analytics

Currently, 97.2% of organizations are investing in big data and AI. Some of these organizations, such as American Express, Motorola, and other 60 Fortune 1000 companies, say they put in up to $50 million in big data analytics and research.

So, the question is, what value do they see in it?

We can single out the following benefits:

  • More precise customer insights. Through data sources, such as product purchases, social media activity, surveys, browser cookies, etc., companies can collect information helping them better understand their buyer’s customer experience and create a more precise ICP (ideal customer profile).
  • Improved understanding of the market. Since big data provides insights into customer purchase behaviors, it can deepen your knowledge of the target market and its dynamics.
  • Increased supply chain productivity. Big data enables predictive analysis, helping you spot possible bottlenecks and adjust your product offer to the growing demand.
  • Better audience targeting. Collecting big data from your buyers and social media followers helps you understand their needs better, thus allowing you to offer more personalized offers. In this case, you can also use big data to adjust your digital marketing activity to the needs and expectations of your target audience.
  • Better risk management. Again, thanks to the predictive analysis enabled by big data, your company can study possible risks and prevent them, ensuring smooth operation.

With the benefits of big data analytics clear, let’s move on to the use cases to show you how different companies are using it to succeed.

1.  Netflix Offers Content Based on Viewer Preferences

Netflix is known to offer a lot of content for people to watch. This platform creates and hosts many movies, TV series, documentaries, etc., and this large assortment can be challenging to manage when it comes to making a personalized offer to each viewer.

So, that’s why Netflix made a focus on using user data to provide each subscriber with the content selection that fits their taste. The company collects this data from various resources:

  • Content ratings left by the platform members
  • Popularity metrics
  • By examining clusters of users based on their location or similarity metrics
  • Stream-related data (time of playing, device type, day of the week, etc.)
  • Content catalog metadata, which includes the content’s genre, director, actors involved, reviews from other platforms, and so on. 
  • Social data
  • Performance, box office information, reviews from critics
  • Demographic data

Aside from using these sources to collect the data, Netflix also bets on awareness and does not hide that the platform needs user feedback to optimize and personalize content offers. As a result, you get movie and TV series suggestions based on your interests and taste. This is an inspiring case study for companies that have a wide selection of products and are looking into making product offers more customized.

2.  Preply Improves Customer Experience with Its Platform

Preply is an online language learning company that hosts over 140,000 tutors worldwide who teach over 20 languages. Apart from language learning, you can also get a math tutor on this platform.

Since the demand for its product varies from user to user, Preply’s main focus is to improve customer experience as much as possible and help its subscribers understand their language learning needs to pick the course that would help them the most.

So, in search of a solution that would help its customers understand how much time they need to master a language to a certain level, Preply analyzed large amounts of social data from its social media subscribers as well as ran some surveys to get more information. The result of these activities is the language learning calculator.

It tells you how long it takes to learn a language based on your current level, the level you want to reach, how many hours per week you are ready to dedicate, and when you expect to achieve your goal.

By the way, Preply also uses this calculator to collect more big data and use it to personalize language learning offers.

3.  Walmart Optimizes Supply Chain and Product Assortment

The final example today is Walmart – a well-known retailer from Northern America. The company uses big data for many reasons, including those similar to the companies described above. However, the ones that definitely stand out are supply chain and product assortment optimization.

So, how does Walmart do it?

When it comes to improving the supply chain, the company uses stimulations to track the number of steps taken from the dock to the store. This way, Walmart optimizes supply chain routes and counts the times the product gets touched until it reaches the customer.

The same data also helps the company analyze transportation lanes. As a result, this analysis allows Walmart to decrease transportation costs and product prices.

Regarding product assortment, Walmart analyzes customers’ product preferences and shopping patterns. This data influences how the company stocks product shelves and displays merchandise. Thus, the products are displayed in a way to help the customer find the ones they need the most.

Over to You

So, as you can see, investing in big data analytics is more than important for your company. With it, you are able to personalize product offers, improve customer experience, optimize supply chains, have a better understanding of the market, and pretty much make sure all your business operations are successful.

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