Flood of data -what is useful
There is a flood of data that is now available which has the potential to help in informing businesses more about the customers. Now, those businesses which are successful in utilizing these new resources and amounts of data will be in a position to provide a better customer experience. However, to predict the behavior of customers continues to be an uphill task.
The very first question that comes up in a data scientist’s mind is, “What type of data does the card to the link have that they can use to enhance their new customers by leaps and bounds, and thus maximize the profits they can make from this campaign?”
Invest in the MapR podium
The very initial parts of the solution as a workflow is in getting the data onto the MapR platform, that is very easy via the NFS (Network File System) protocol. Very unlike the distributions of Hadoop that only permit the imports of data clusters or an import as an operation that is batched, MapR allows you to be able to mount the clusters themselves via the NFS. The file system of the MapR allows you to directly modify the file and allows for multiple reads that are concurrent using semantics of POSIX. An NFS mounted cluster makes it for easy ingestion from the other machine leveraged commands in Linux, applications, scripts and utilities. The whole of the completed customer purchases and history data from the past campaigns is then exported from the classic data warehouse and placed inside of the MapR platform in the form of Inlay tables. The browsing history from the internet is put into the MapR platform in the form of text documents. Once the purchase history, the browsing patterns and the campaign details are put on to the MapR platforms, the Apache Drill is put to use for interactively exploring the pre-processing of the information with an SQL query engine which is free of schema.
Classification and Machine learning
Classification refers to the family of the supervised algorithms employed for machine learning which identify as to which category does an item belong to (for example, whether a customer has a liking towards pastries or not), based on the data that is labeled (such as the history of purchases). Classification takes as input a data set which has labels and also features and then tries to learn as you how to go about labeling the new records in the light of that information. In this particular example, the history of purchases is used in putting labels on the customers who ended up buying pastries. The history of browsing takes into account millions of the test keywords used, most of which do not seemingly have anything to do with pastries. It is put to use as the features in discovering what is similar while categorizing segments of customers.
So, this is how data science is able to use behavioral data from customers to find groups of customers who all share the same behavioral likings so that better advertisements can be served to them.
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