Big Data and Data are two of the words most widely used nowadays in the innovation and entrepreneurship ecosystem. However, do we know about specific cases in which they have been used? This article will present some of these practical examples, in areas as diverse as sports, politics or the economy. From BBVA to Obama, from baseball to the Gay Pride Week in Madrid, the use of data and its analysis to predict trends and behavior is here to stay.
In recent years we find some striking examples of the use and analysis of Data and Big Data that somehow serve to create new products, to predict behavior and trends, to optimize marketing actions, etc. The following are worth mentioning:
Macy’s and its prices in real time
Macy’s is one of the most important retailers in the United States, and stands out for its e-commerce. Using the SAS Institutute’s technology, it has managed to improve its revenue and the user’s experience. Thanks to the speed of analysis and the reports obtained with this new technology, they have reduced the annual cost of analytics by 500,000 dollars. Today, Macy’s is perfectly aware of the impact of its newsletters and notifications, and has a better idea of its most satisfied customers, of what they like and what they don’t… Today, the use of data enables it to segment its deliveries to the maximum possible extent and send fewer e-mails, but with much greater impact, and they have managed to reduce by up to 20% the number of customers unsubscribing. Thanks to the use of an algorithm and demand and inventory control, they can launch cross-offers, adjust prices and offer discounts practically in real time for their 73 million items on sale.
Ball games and millions of data
Nearly everybody has heard of the film Moneyball: Breaking the Rules (2011), if not for Brad Pitt’s role, at least as an example of the use of Data. It happened in the 2002 pre-season at the Oakland Athletics team of the U.S. Major League Baseball. The sports manager Billy Beane revolutionized the history of the club and possibly of sports in general after recruiting a young economist, Peter Brand, who brought new ideas. Together, they signed up undervalued, but economically profitable players, using a very different selection criterion. The intuition and knowledge of the talent scouts are replaced by the conclusions of the analyses of statistics and figures accumulated when it comes to establishing the team’s needs and the players that best meet those needs.
Today we have many more cases where Big Data is used in sports. NBA team have implemented the use of data for planning strategies before each game. And the NFL itself has a platform whose applications help the 32 teams to make the best decisions based on data analytics: from the condition of the grass to weather conditions, or information on each player’s time at university… everything is recorded and everything can be used to draw different conclusions, for example, to prevent player injuries. Moreover, it analyzes the preferences of fans thanks to its NFL Now application, that offers them the possibility to create their own channel with varied NFL content: fun videos, preferred cheerleaders, information by team or by player, etc. They also use NetApp to store all this data. In this way they can establish the demands of fans and it also makes it easier to define marketing actions, expand the market, find the most appropriate partners, etc.
Obama’s segmented campaign
After his first term of office, the U.S. president Barack Obama decided to use Big Data for his re-election in 2012. A hundred people worked in the campaign’s analytics department. 50 worked full-time at the central offices, another 30 mobilized across the different headquarters in the country, and 20 were focused exclusively on interpreting the data received. After a first analysis, the campaign’s efforts focused on three aspects: registration (collecting data from committed voters), persuasion (effectively addressing hesitant voters) and electorate’s vote (making sure that supporters would vote). And for the first time, the three most important teams of the election campaign (field, digital and communication) worked with a strategy unified with the respective data of each one. The driving force behind all this, the smart platform used, was HP Vertica. The most effective actions offered by this platform included collecting data on the field and providing very fast feedback via e-mails by the online team (with improved time and efficiency), or detecting the niches where TV advertising would work best, cross-referencing voter data with demographic data and information on audiences, advertising prices, and programs (thus improving the impact and segmentation). This their analytics, Obama’s team optimized communication and improved the response of Democratic voters, and avoided wasting resources, time and money on voters who did not support their party.
BBVA: Mobile World Congress y Turismo Madrid
BBVA has also conducted several Big Data tests where attention was also paid to the display of data to enable the information to be more understandable by inexperienced viewers. The economic impact of the Mobile World Congress was measured in Barcelona in 2012. To this end, data for the transactions made using credit cards was extracted, both for the previous week and for the week when the event took place. The results revealed the places, days and times with more activity, something, for example, that can help retail establishments reinforce their marketing and sales actions for similar events. It can also help cities do the same with their tourist promotions.
Another example is the study conducted by BBVA on the analysis of credit card usage in Spain during Easter week in 2011 in four sectors: markets and food, bars and restaurants, fashion and gas stations.
One last example is the work carried out together with the Madrid City Council. Under the title Tourism Dynamics in the City of Madrid, it analyzes the shopping behavior of tourists in 2012. Among the many results, the study quantified the economic impact of Gay Pride in various areas of the city. Shopping spending increased by 24% on the same week of the previous month. It also provides other interesting data, for example, the tourists who spend the most, what they spend their money on, where they go, etc.
Data and Big Data are changing many things, not only when it comes to making decisions related to shopping, sports, politics, etc., but also when creating new products, innovating, storing data, developing, viewing things… It is a widespread trend that is here to stay.
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