Use  Cases

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Sentiment
Analysis

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.

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360-Degree
Customer View

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.

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Ad-hoc
Analysis

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.

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Real-time
Analytics

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.

Multi-Channel Marketing

Multi-Channel
Marketing

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.

Customer Micro-Segmentation

Customer
Micro-Segmentation

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.

Ad Fraud Detection

Ad Fraud
Detection

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.

Clickstream Analysis

Clickstream
Analysis

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.