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There are some advantages of combining big data with machine learning, writes Deji Atoyebi for Entrepreneur.
Customer segmentation is best done using machine learning algorithms because they don’t require human intervention to operate. An unsupervised clustering algorithm only needs data for exploration to create distinct customer groups with their own set of features.
For instance, in 2009 Orbitz resorted to machine learning to hasten customer segmentation, which allowed it to discover a pattern from the data three years later.
They discovered that Mac users were willing to spend 30 percent more nightly for hotel rooms than Windows users, which Orbitz exploited by displaying costlier hotels to Apple users.
Other businesses can follow Orbitz steps to attain segmentation by utilizing the combined strength of machine learning and big data. But it is a must that they find out if segmentation will give any potential edge to their organizations.
Machine learning and big data make targeting feasible and effective by allowing business to formulate ways to cater to various needs.
For instance, Google uses both to understand user preferences and to provide relevant results for online searches and queries.
Pixar also relies on machine learning to tease audiences with movie advertisements based on learned preferences. Netflix earns as much as US$ 1 billion a year in value from customer retention.
Predictive analysis is done easily through machine learning and big data, such as what American Express did when it found out that as much as 24 percent of its Australian accounts would close within four months. T-Mobile also depends on predictive analysis to study customer fluctuations.
Machine learning and big data are also the starting points for risk analysis and regulation.
American Express saves millions of dollars by using machine learning to detect fraud in large historical datasets.
Machine learning can also ensure compliance with anti-money laundering, detecting rogue trading, and other anomalies.