Real-time data: The key to transaction satisfaction for the contemporary bank accountholder
The following is an excerpt from the white paper, "Enhancing the End-to-end Customer Experience with Real-time Data," the companion paper to a June 7 webinar of the same title. The complimentary white paper and free webinar replay are available at ATM Marketplace.
When it comes to data collection and analysis, many banks and FIs are wondering how "quick" is good enough.
There are cases where batch data and near real-time data collection will suffice, but minimizing data latency becomes more important if a delayed reaction means hurting customer loyalty, channel profitability or the reputation of the bank.
Meeting today's customer expectations generally requires real-time data. As we continue to adopt more digitally assisted service models, self-service touch points and digital banking applications, you have less time to make decisions while dealing with a higher velocity of transaction data.
The longer it takes to collect this data, the more chance of:
- Data depreciation, shrinking decision windows and delayed actions and decisions.
- Customer inconvenience due to fraud and transaction availability issues being missed.
- Customer dissatisfaction due to lack of instant gratification.
The challenge is to figure out how best to apply real-time data collection tactics in a consistent, cost effective manner.
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Collecting, integrating and sharing real-time transaction data has become less costly and easier to do. It is now possible to automatically collect, decode and correlate every end-to-end transaction — without the need to add software agents, extra traffic load or code changes.
By establishing a centralized data hub, rich transaction intelligence is made more accessible to multiple teams and applications — anytime, anywhere.
Organization-wide wins associated with open access to real-time transaction data include:
- Improved collaboration between teams and more effective, fact-based decisions.
- Faster response times to issues impacting the end-to-end customer experience.
- Reduced transaction failures and a more consistent service delivery across all applications and banking channels.
- Reduced cost-to-serve through faster troubleshooting, research and consolidated tools.
- Deepened omnichannel customer engagement with ongoing analysis of customer usage patterns, and the application of predictive modeling and machine learning techniques (i.e., estimate line queues, cash utilization and customer interaction sequences).
- Mitigated fraud loss with instant notification of transaction slowdowns, failures, lack of activity and anomalous card use.
Here are some examples of how different departments can utilize a centralized source of real-time transaction data to discover new revenue opportunities, reduce operating support costs and deliver an amazing end-to-end customer experience ...
Download the complete white paper
Listen to the webinar replay
Companies: INETCO Systems Limited
Suzanne Cluckey Suzanne’s editorial career has spanned three decades and encompassed all B2B and B2C communications formats. Her award-winning work has appeared in trade and consumer media in the United States and internationally. She is now the editor of ATMmarketplace.com and BlockChainTechNews.com www