'A 1000-mile journey begins with a single step' – ancient Chinese adage In today's intensely competitive environment, banks are faced with the challenge of maximizing revenue, reducing attrition, and maintaining customer relationships. This means implementing a holistic approach for creating strong customer-centricity, which is the key objective of great customer lifecycle management (CLM). Whether it is transaction banking or wholesale banking or cash management services, customers continue to generate significant fee-based revenue for banks. So CLM is vital for serving corporate, institutional or individual customers. And given the fierce competition to win customer loyalty, especially with increasing mergers and acquisitions, banks are now investing much more than before in improving their CLM processes.
In our last article, Lifecycle mapping: uncovering rich, predictive data sources, we discussed the importance of mapping out your customer lifecycle to better understand where your most predictive customer data is hiding. Now, we'll pose some questions to help identify your predictive customer attributes and lifecycle events, pinpoint where that data is located, and recognize patterns to predict outcomes for future prospects, leads, and customers. Data discovery is the second stage in the customer lifecycle optimization (CLO) process. The primary task of this stage is to expand on your lifecycle map to identify authoritative data sources that establish progress. At each stage, prospects, leads, and customers will complete certain events that will individually or collectively trigger a transition in or out of that stage.
They are going to be briefing majorly into key topics like data creation, management, and value creation lifecycle. They will also deliver unconventional intelligence and analysis of the key data issues challenging companies as 5G begins to roll-out and the Internet of Things continues to rise. It will bring together pioneers in all of these areas and will furnish best-in-class wisdom to those striving to understand the multiple legal and business issues that go into fabricating a world-class data management and exploitation strategy.
To characterize 2017 technology trends, we might use the phrase intelligent systems. They have had a major effect on users' digital experience. Technology trends like artificial intelligence (AI), chat bots, virtual reality, the internet of things (IoT) and data analytics are increasingly bringing new insights and data to traditional systems and processes. These intelligent systems are supposed to help decision-makers use data to take action: AI can help sales reps, marketers and customer service agents learn more about customers and prospects; chat bots can help service agents provide more efficient service; and IoT is collecting real-time data generated from a variety of connected things to not only provide proactive service on cars and fridges, but also offer cities valuable data about traffic congestion. Intelligent systems are also helping alleviate some of the manual, time-consuming work of yesteryear with automated processes.
With so many digital initiatives competing for resources, breaking through legacy analytic slowness and modernizing the analytics lifecycle is vital. The key is turning data assets into insight driven actions as quickly and collaboratively as possible― and without reinventing the wheel. Levon Johnson, Manager of Employee Data and Analytics at Alaska Airlines, will further illustrate how he single-handedly elevated employee analytics from next-to-nothing (gut-driven decisions) to company-standard (data-driven/intensive). Join us for this latest DSC Webinar and find: Resolution to the analytic barriers of time, effort, and pain Deeper accuracy and efficiency through reuse and collaboration Real examples of quick-win projects and meaningful reports Break down data barriers keeping analytic teams from getting the insights that matter. It's a new age for how companies innovate with analytics, and it's time the analytics process caught up.