Big data has increased the requirement for using timely data in analytics. As part of leveraging a wide variety data for granular customer analysis, we started tapping into dynamically changing data. Thus the new sophisticated analyses we leverage for customer experience requires more frequent data capture from source systems to keep the analytical data stores up to date. Imagine the old days when businesses ran basic customer segmentation using mainly demographic information and major products owned. In that case, the data used in the analysis changes pretty infrequently. Capturing data weekly or daily would be adequate to keep the data current. However, when you employ more sophisticated analysis, by including clickstream analysis on the web, responses to other marketing offers, sentiment analysis results and more, you are leveraging frequently changing data points. In our digital age, customer behavior and their sentiment change more frequently than their demographic information. Thus the analytical system needs to receive continuous updates from source systems to be most effective in providing relevant and actionable insights. This is where real-time data integration comes in.
Real-Time Data in Action for Improved Customer Experience
Without an organized and complete view into all customer data, the analysis will be incomplete and the effort to “wow” the customer can be futile. For example, if a customer has recently bought a second service from a company but the marketing team does not have data from the order management system, they can continue to contact this customer for the service this customer already owns.
The Role of Data Latency in Building Deeper Customer Insight
Data timeliness is crucial in driving value by engaging with customers based on their unique circumstances. It is not just about getting the data from core systems, but making sure the data stays up-to-date to reflect that customer’s truth. Only with relevant information can the business’ action continue to create value for the customer.
Many industry leaders, such as BT and DIRECTV, choose real-time data integration and create customer analytics solutions that leverage low-latency data. Their call center agents are able to make personalized offers to customers when they are on the phone, using the latest information including the reason for contact. These organizations combine historical customer information with real-time data and use real-time analytics. They incorporate the results into call center business processes and guide call center agents to provide completely personalized offers to customers. This capability increases the conversion rate for promotions, and also reduces churn because they are able to quickly determine the best way to handle a particular customer’s issue.
MegaFon, Russia's top provider of mobile internet solutions, deployed real-time data integration to capture billions of monthly transactions from eight regional billing systems. The data is centralized onto an operational data store and distributed to business-critical subsystems. The unified and up-to-date view into customers enables sophisticated analysis of mobile usage information, and facilitates more targeted customer marketing. As a result of the personalized campaigns with timely data, the company increased revenue generated from the current customer base. Woori Bank in Korea adopted real-time data integration for its marketing operations as well. Now, users can immediately access and analyze transactions for specific trends and adjust campaigns and strategies accordingly. Woori Bank uses real-time customer data to send tailored offers to customers.
These customers illustrate how advanced data integration, data quality, MDM and big data analytics technologies provide the businesses the right foundation to wow their customers, one customer at a time.
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