Five Common Hadoopable problems: part2

Big Industries' blog

Hadoop gives companies the power to store and analyze information quickly, efficiently and at a lower cost than ever before. It's power and flexibility make it the perfect solution to problems that involve large, complex data sets, and that demand new approaches to processing and analysis.


Hadoopable problem #2: Reducing Customer Churn


The Hadoopable Problem:

For wireless telecommunications providers, digital media companies, and other service organizations, customer acquisition costs are high and the impact of losing customers, dramatic. Customer loss has direct impact on profitability and performance.

Companies like these have taken extraordinary measures to understand their customers - both how to improve overall satisfaction and how to stem attrition. The discipline of Retention Processing (operations undertaken to reduce churn) has been on the leading edge of relational database technologies for years.

But both th processing necessary to provide impactful, real-time analysis, and the amount of data required, has caused researchers to scale back analysis, rather than expand it.

The Solution:

To analyze multiple data sources and determine why users might terminate contracts, a large mobile carrier turned to Hadoop. With Hadoop they could combine traditional transactional and event data with other data sources, including information from social networks.

The company combined traditional transaction data such as call logs to develop models for inter-customer communications (who called whom) and created a graph of their users social networks. An analysis of customer loss combined with social networking details, indicated that groups of customers left together - the loss of one member of a social network was highly predictive of the loss of other members.

By combining market data on new equipment release dates and adoption (in this case, mobile phones) with known patterns of customer churn, correlation was discovered between the arrival of new phones and customer departure. New phones and discounts were directly related to a customer's choice of a new service plan or provider.

The Bottom Line:

Data analysis is key to determining customer preference and building customer lifetime value. However, to successfully analyze complex problems like customer churn, data from many sources is required. By combining these data sources using Hadoop, it's possible to create models that tie together market forces, customer preferences, and company operations into a holistic view of customer retention that can positively impact profitability and company performance.



Hadoopable problem #3: Targeting advertising

Targeting Advertising.jpg

 The Hadoopable Problem:

Online advertisement targeting is big business. At its core, ad targetting is a specialized type of recommendation engine that identifies users, determines their preferences, and delivers the ads best suited to each user. In practice, ad targeting is incredibly complicated - involving paid placement by advertisers who are constantly working to increase ad views and drive viewer engagement. Ad networks auction ad space to advertisers who want their ad shown to the people most likely to buy their products. Ad placement can become a very complex optimization problem with conflicting priorities and complex models.

The Solution:

One advertising exchange uses Hadoop to collect the stream of user activity coming off of its servers. The system captures that data on the cluster and runs analyses continually to determine how successful the system has been at displaying ads that appealed to users. Business analysts at the exchange are able to generate reports on the performance of individual ads and to adjust the system to improve relevance and drive immediate increases in revenue.

Another ad exchange has focused their efforts on building sophisticated models of user behavior in order to choose the right ad for each viewer in real time. The model uses large amounts of historical and tracking data about each user to cluster ads and users, and to deduce preferences. By leveraging the power of Hadoop to analyze historical user data, the exchange delivers much better-targeted advertisements and can steadily refine its models and deliver increasingly better ads.

The Bottom Line:

Hadoop is a powerful platform for dealing with cybersecurity threats, fraud, and criminal activity. It is flexible enough to store all the data that matters - content, logs, relationships between people or systems, and patterns of activity. It is powerful enough to run sophisticated detection, analysis, and prevention algorithms, and to create complex models from historical data to monitor real-time activity. Hadoop is also flexible enough to change as threats evolve, and to both store and analyze new data sources as they become available. With Hadoop, companies can better analyze and prevent cybersecurity threats efficiently and effectively.

 More Hadoopable Problems


Source: Cloudera Whitepaper


Posted by Matthias Vallaey on Dec 19, 2016 2:43:24 PM

Matthias Vallaey

About the Author

Matthias is founder of Big Industries and a Big Data Evangelist. He has a strong track record in the IT-Services and Software Industry, working across many verticals. He is highly skilled at developing account relationships by bringing innovative solutions that exceeds customer expectations. In his role as Entrepreneur he is building partnerships with Big Data Vendors and introduces their technology where they bring most value.