Hadoop-Driven Big Data Digital Marketing Use Cases

Hadoop-Driven Big Data Digital Marketing Use Cases

Marketers in the modern period have been experiencing an influx of large amounts of data in their operations. Companies nowadays use big data in their activities in marketing, risk management, customer behavior analysis, feedback analysis, production of new products, and customer segmentation, among other operations. This paper discusses the use of big data in marketing. It discusses the case of Nike, a global company that deals with sports equipment and gear. Notably, big data in marketing has enabled many companies like Nike to create innovative and creative marketing campaigns.

The case of Nike

Nike has been a global leader in the manufacture, sale, and marketing of sports products for many years. The company recently bought a big data analytics company to help manage its analysis and management of the vast amounts of data it gets each day. The company uses big data analytics to learn about customer habits and craft marketing campaigns that target specific client segments. By using this strategy, Nike has managed to acquire and retain customers by customizing its marketing messages. The firm can identify the customers to target and when to target them. Therefore, its marketing campaigns have been useful.

Big data use in Nike entails some elements of big data-V characteristics. Though big data applications in this company involve most of the big data V-characteristics, this paper discusses volume and variety (Sun, Strang & Li, 2018). Nike experiences enormous volumes of data daily. It includes text, videos, and photos, among other kinds of data. The company uses big data analytics to sort out this information and makes sense of the data. It then picks the most essential and uses it to design marketing campaigns and messages. The other notable characteristic is variety. The company receives data from various sources. They include in-house information, comments from clients and other stakeholders, and information from social media sites like Facebook, Twitter, and Instagram. Thus, information flows in from multiple channels. The company applies big data analytics to link all this data to create meaningful marketing campaigns.

Hadoop stack components

Business organizations can use Hadoop stack components to manage and process big data to generate essential business analytics. They include HDFS and Map Reduce.

HDFS

HDFS can be a useful tool in managing and processing big data for purposes of marketing. In essence, the program works by breaking down data into separate blocks and distributing them in clusters (Achari, 2015; Ghazi & Gangodkar, 2015). By doing this, it allows for parallel processing of the data. Thus, it is easier to sort out similar information and put it together in meaningful forms. When a company uses this program, it analyzes data quickly because all similar information can be put in one platform. Therefore, it is easier to access the required information from these large caches of data and use it as necessary. Campaign messages can thus be customized accordingly.

The other program used in the analysis and management of big data is MapReduce. The application converts data into different sets and breaks it down into value pairs (Ghazi & Gangodkar, 2015). It then combines the value pairs into smaller clusters, enabling analysis and management of this data. The application can be applied in big data analysis to generate essential business analytics. Notably, a company can acquire a lot of data regarding many issues. It can then break it down into smaller sets that are alike and then analyze it in this form. By doing this, it is easier to get the data to use for specific marketing campaigns.

To summarize this paper, big data use has enabled companies to develop innovative marketing campaigns. The example of Nike puts this issue into context. One can see big data-V characteristics in Nike’s use of big data. Apart from that, companies can also apply MapReduce and HDFS in their big data management and analysis.


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