Best Big Data Analytics Use Cases
Use Qubole Data Service (QDS) to quickly turn big data analytics into actionable insights across multiple big data use cases.
Sentiment analysis offers powerful business intelligence to enhance the customer experience, revitalize a brand, and gain competitive advantage. The key to successful sentiment analysis lies in the ability to mine multi-structured data pulled from a variety of sources into a single database. Learn how a big data platform can help you get more value out of sentiment
A 360-degree customer view offers a deeper understanding of customer behavior and motivations. Obtaining a 360-degree customer review requires analysis of data from sources like social media, data collecting sensors, and mobile devices. From there, more effective micro-segmentation and real-time marketing often result. Learn why big data analytics offers a more complete picture of the consumer.
Ad-hoc analysis only looks at the data requested or needed, providing another layer of analysis for data sets that are becoming larger and more varied. Big data ad-hoc analytics can help in the effort to gain greater insight into customers by analyzing the relevant data from unstructured sources, both external and internal. Learn how about a how big data cloud service makes ad-hoc analysis easier in Hadoop.
Systems that offer real-time analytics quickly decipher and analyze data sets, providing results even as data is being generated and collected. This high-velocity method of analytics can lead to instant reaction and changes, allowing for better sentiment analysis, split testing, and improved targeted marketing. Learn more about how your business can benefit from real-time analytics.
Multi-channel marketing creates a seamless experiences across different types of media like company websites, social media, and physical stores. Successful multi-channel marketing requires an integrated big data approach during all stages of the buying process. Learn more about how a big data platform can streamline your multi-channel marketing.
Customer micro-segmentation provides more tailored and targeted messaging for smaller groups. This personalized approach requires analysis of large sets of data collected through customers’ online interactions, social media, and other sources. Learn more about how companies are using big data to better segment and target customers.
Ad fraud detection requires data analysis of current fraud strategies by recognizing patterns and behaviors. Data that shows abnormalities of group behavior make it so ad fraud is detected early and stopped before it is spread. Learn why businesses are turning to big data platforms to combat ad fraud.
Clickstream analysis helps to improve the user experience by analyzing customer behavior, optimizing company websites, and offering better insight into customer segments. With big data, click stream analysis helps to personalize the buying experience, getting an improved return on customer visits. Learn more about the impact of big data on clickstream analysis.