Case Study – Decision Based Science Analytics
A large global casino entertainment company saw a critical need to implement decision based science analytics to more effectively track, analyze and provide supreme services to on-site casino customers during stays. Legacy business analytic technologies were inadequate at dealing with the type and volumes of data that were collected.
Centizen proposed building a big data analytics engine using an open-source Apache Hadoop distribution system that could handle real time action in response to point of sale on-premises customer events streaming in that could be used to dynamically customize each customer’s experience.
This client’s new data-driven strategy is based around an internal customer rewards program which has more than 45 million members. Program members are tracked throughout their entire casino travel journey. From the moment they book, until the moment they leave the casino, every customer purchase is tracked and analyzed. The data is then used to provide customized services to members in real time.
Centizen consultants built the data analytics engine using components of the Cloudera Distribution Hadoop to handle real time action in response to Casino point-of-sale events that streamed in. The goal was to be able to apply a set of rules on the events and trigger alerts on incoming data. The technologies and tools used by Centizen included HDFS, HIVE, PIG, Python, Impala, Spark Streaming, Flume, HBASE, Solr, JSON Parser and Java.
Outcome / Business Value
With implementation of the new decision based science analytics, this client has benefited greatly from the ability to integrate structured and unstructured data to gain a more complete understanding of each customer during casino stays. This understanding allows the casino to provide a significantly better personal customer experience than was possible before. With the new data analytics, the casino is gaining unique insights into their customers’ purchasing behavior that are then leveraged to make offers targeted to each customer’s unique interests.
In summary, this analytics engine performed as intended to support new ground breaking marketing campaigns than were possible before and targeted at customers with interests beyond traditional gaming that include entertainment, dining and online social gaming.