Why Location-Based Offers?
Mobile technology is widely used in this 21st century and is becoming an effective medium to serve customers. Smart phones are capable of identifying the user’s geolocation that can then be used to find the nearest ATM, gas station, or hospital. Banks can use this same mobile technology to provide personalized offers to mobile customers tailored to their current location.
Banks collect customer data through credit card transactions, bill pay, account transactions, and other interactions. Applying Big Data solutions to this variety of customer data enables a wider view of the customer. This broader view of the customer, when combined with mobile devices, can then be used to select merchant offers for credit card customers that best fit their purchasing preferences and locations. These offers are used to incentivize increased credit card transactions and drive more traffic to partner merchants.
Business Intelligence tools are used to analyze customers’ big data and derive a model of each customer’s propensity for accepting offers. Recommendation engines with adaptive learning capabilities can consider customer preferences based on the propensity model when selecting offers.
Oracle is providing a complete platform for this Location Based Mobile Offers service with its proven technologies such as Oracle Mobile Application Framework, Oracle Big Data Appliance, Oracle Real-Time Decision Server, and Oracle Advanced Analytics. These products work together to capture and store customer Big Data, analyze the data, make offer decisions based on that analysis, and interact with the consumer.
Mobile Technology to provide mobile device applications
Oracle Mobile Suite provides a common technology framework for building, integrating, securing, deploying, and managing mobile apps on any device. A mobile application developed with Oracle Mobile Application Framework runs on both Android and iOS devices. The mobile application allows authenticated users to request offers from the bank via web services. It also manages the received offers for the user and helps them locate participating merchants.
Big Data to aggregate customer data
Banks have been capturing big data for years, but it’s often in a variety of nonstandard formats and located in isolated silos around the enterprise. Only recently has the technology become generally available to create an infrastructure that can process and analyze the variety, volume, and velocity of the data. Oracle brings big data to the enterprise with the Oracle Big Data Appliance- a pre-integrated engineered system that includes the Cloudera distribution of Hadoop (CDH) and all Cloudera Enterprise Technology software, plus Oracle NoSQL Database, and the Oracle R Distribution.
The Big Data Appliance is the place where all the customer data comes together. Extraction, transform, and loading (ETL) tools are used to move the data from the bank system to the Big Data Appliance where it is then available to advanced analytics.
Advanced Analytics to predict customer propensities
Once the large datasets (big data) have been ingested and acquired into the Oracle Big Data Appliance, analytical techniques are used to develop predictive models of customers that reflect the propensity of a customer to like and use offers from a specific types of merchants.
Oracle Advanced Analytics, an option of Oracle Database Enterprise Edition, offers a combination of powerful in-database algorithms and open source R algorithms to predict customer behavior. High performance in-database data-mining algorithms and statistical functions are accessible from SQL and R.
Event Processing to send offers based on the credit card events
Oracle Event Processing is a java container implemented with a light weight, modular architecture based on the Open Service Gateway initiative (OSGi). It provides an environment for the development of event processing applications to handle hundreds of thousands of events per second such as credit card transactions, stock trades, travel bookings etc. Banks use this software to handle large volumes of credit card transactions and extract customer and merchant data that feeds the offer decisioning process and the customer model.
Decision Management to deliver offers as per customer preference
A critical component for providing the right customers with the right product, services or advertisement is the ability to provide near real-time decisions. Oracle Real Time Decision Server (RTD) supports all four elements of building and managing decision logic, embedding predictive analytics, optimizing results, and monitoring and improving decision making. RTD supports analytic models that learn, identify, and use predictors based on customer behavior. This helps banks scale their use of analytics across their channels and products while reducing the cycle time to learn about market changes and new opportunities.
For location based offers, RTD is the decision engine that uses the results of analytics and event processing to select the best offer for the consumer based on their location, propensities, and previous history.
Banks are uniquely positioned to provide a complete, end-to-end offer presentment solution that is both more personalized to the needs of the consumer and tied to the existing merchant and payment ecosystems banks dominate today.