Big data is one of those concepts that you hear about all the time, but it is difficult to “land” with concrete examples. If you’ve never seen big data in action, it may be difficult to imagine the practical applications and benefits it can bring to your business.
To help and inspire you, we have compiled 7 examples of brands that are already using big data to achieve incredible results. Don’t miss them!
7 examples of big data in brands
1) Netflix
Netflix is estimated to save 1 billion dollars a year thanks to its big data algorithms.
Its story begins in 2006, when it launched the “Netflix Prize” of one million dollars to whoever could create the best algorithm to determine subscribers’ opinions on a series or movie based on previous scores. Today, 80% of the content played on Netflix comes from the recommendation system.
Netflix employs several traditional business intelligence tools (such as Teradata and MicroStrategy) and combines them with modern big data technologies such as Hadoop, Hive, etc. The result is an algorithm that predetermines the content that users are most likely to see.
In the end, the key to Netflix’s success is personalization, and big data is what makes it possible. Only in this way can they provide a unique experience for each user.
2) Apple
Apple uses the big data applied to behavioral economics, in order to draw conclusions about your user base and use them to your advantage. These are the 6 principles of behavioral economics that have helped you build your brand:
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Tribalism: Tribes are social groups with similar interests and beliefs, which share the same identity. In that sense, users of Apple products are a tribe that shares the same aesthetics and lifestyle.
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Endowment effect: We tend to value the objects we already own more highly, and big data shows that we are willing to pay more for them. Apple implements this principle by allowing you to try products in its stores.
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Social proof: This principle is based on taking advantage of user testimonials and recommendations from family and friends.
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Heuristics: People use “mental shortcuts” to make quick judgments. Apple makes the most of this principle in its packaging, since it is considered that if a packaging is well designed, the product will also be well designed.
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halo effect: This cognitive bias judges the quality of a product based on impressions of previous products. Thus, Apple has been creating a long history of successful launches that make its brand almost blindly bought.
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Price: Apple’s big data analysis reveals that its pricing strategy works, despite being unintuitive: its products are always priced high and they never go on sale.
3) Barcelona Metro
Barcelona Metro has implemented the RESPIRA system, which uses artificial intelligence to improve ventilation and help control coronavirus infections on the Barcelona metro network.
This control system analyzes different variables, such as thermal sensation, temperature, humidity, indoor air quality of the stations and electrical consumption of ventilation. All these variables are centrally correlated to establish the optimal ventilation strategy thanks to a dynamic algorithm based on machine learning techniques.
4) Amazon
The large retail giant is capable of analyzing a brutal amount of customer data. Its algorithms allow you collect, analyze and use a massive amount of data from search and purchase history. Therefore, they are able to offer recommendations with a high probability of generating a purchase, optimize prices and the supply chain, and detect fraud.
The secret to its success lies in its advanced big data analysis tools, such as advertising algorithms and the “Amazon Elastic MapReduce platform for machine learning.”
5) Zara
Since 2008, when it surpassed Gap, Zara is the largest clothing retailer in the world. The secret of his success lies in his ability to detect new trends as soon as they arise and ship garments to stores faster to meet their customers’ needs.
Zara’s supply chain is supported by the use of data and analysis to make predictions and make sound decisions. The data comes from both daily inventory and store orders and customer reviews.
In order to analyze all this raw data and make the right decisions, Zara incorporates multiple artificial intelligence, automation and big data tools into its business strategy.
6) UOB Bank (Singapore)
Singapore’s UOB Bank is a great example of big data for risk management. As a financial institution, there is a great potential for losses if risks are not managed properly. For this reason, recently, in 2018, it tested a risk management system based on big data. This allows them to reduce the calculation times of the variables at risk, going from 18 hours to a few minutes. Thanks to this initiative, UOB Bank hopes to be able to perform risk analysis in real time, which will produce large savings in avoided losses.
7) PepsiCo
The big data and cloud analytics platform used by PepsiCo, Pep Worx, helps the company advise stores on what products to buy, where to place them and what promotions to launch.
In preparation for the launch of Quaker Overnight Oats, PepsiCo was able to identify 24 million households to target its product. They then identified the shopping locations those households were most likely to use and created specific promotions for these audiences. Thanks to this use of data to focus on a very specific market, they achieved 80% sales growth for the product in the first 12 months after launch.