Banking On A Customer-Centric Approach To Data

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1 Banking On A Customer-Centric Approach To Data

2 Putting Content into Context to Enhance Customer Lifetime Value No matter which company they interact with, consumers today have far greater expectations from their user experiences especially from their favored vendors. New, more technology savvy companies are raising the bar when it comes to customer service, helping to inflate your customers expectations. This is happening despite the fact that traditional banks have more information on their customers than these new players. Traditional banks need to use their customer knowledge and go beyond traditional thinking. The ideas of service is king and the customer is always right are being taken to a new level when it comes to providing the kind of experience needed to impress and retain customers today. While at the same time, banks need to keep in mind the best vehicle for information and offers, which are being consumed through a growing number of channels including mobile devices, social media, web, mail, , television, etc. Making broad offers through Banks and financial institutions can no longer treat or fixed channels once a perfectly view customers in aggregate, demographic categories. acceptable course of action Making broad offers through fixed channels once a perfectly acceptable course of action that, if done well, that, if done well, could help could help organizations keep their customers - now falls organizations keep their far short of what is needed to be an industry leader. customers - now falls far short of Financial organizations today need to see customers as what is needed to be an industry individuals who they know well, protect and serve like no leader. other. In order to succeed, banks must continue to be the trusted bank as always, protecting the customers financial and personal information. They need to understand their customers and know all aspects of that customer behavior, context, interests and preferences in order to properly wow them and turn them into loyal customers. They have to reach the right customers via the right channel, with the right content, at the right time to improve the customer experience, enhance brand loyalty, and increase the customer value something that sounds simple, but has proven elusive for many. Data + Context + Action = Better Content and Better Customer Experience Banks have so much information at their fingertips, but unless they can use this information quickly and effectively, it s wasted. For example, based on [customer] John s available information (i.e. location, engagement, transactional, CRM, website history, social ), you might have learned that he recently purchased a house. If you had merely looked at his age and income level, you might have sent content to John - via offers through , social, mail, etc. pertaining to loan options and mortgage-related products. How is this of any value to a new home owner? 2 Copyright 2014 NGDATA

3 Without understanding a particular customer s context, you have not delivered a timely nor appropriate content/offer for him. These offers would have been a lot more effective had your bank taken action and delivered these offers to John before he purchased a home, or while in the buying cycle. As a result, John s relationship with your brand diminishes, as he recognizes that you don t truly understand his needs. Or, for instance, you have a program where you offer a credit card to all customers. This involves a physical mailing, with all its printing, collating and postage charges. With no real connection between those customers who have taken you up on the offer, nor those who visited a website or called to inquire further, you continue to r offers until the program has run its course. This wastes money and likely annoys both those who have already taken you up on the offer, and those who will never take you up on that offer. With a deeper knowledge of the customer their likes, preferences and propensities and the resulting ability to focus on which ones were most likely to respond, and their preferred offer channel, you would have saved time, money and, perhaps even, a customer rant. Real-time Personalization As customers consume content via the various channels mentioned above, something critical is happening that customer is becoming a significant factor in a company s distribution or referral program. After all, it s the customer that ends up referring content on social platforms and word of mouth. It s critical, therefore, to build a personal relationship with your customers, one whereby you truly understand their needs and preferences. Technology companies built from the ground up to be data-driven are good examples of how customer experience management is a true asset. Take for instance, Google. Google gathers massive amounts of data about its users activities, locations, interests and more merely from its web activities. As a result, a user s experience with Google is more personalized than that of say, your bank. Google Now goes so far as to tell you today s weather before you start your day, how much traffic to expect before you leave for work, when the next train will arrive as you are standing on the platform, or your favorite team s score while they re playing. And the best part? All of this happens automatically, without any human interaction. Subscription-based companies must leverage the enormous amount of existing user data they have stored and are constantly receiving. This will enable them to create user experiences that are much more personalized than those created by technology companies that count merely on web traffic activity and information. The services and offers banks make must be as beneficial as a virtual, personal assistant. Because end users expectations of their vendors are increasing, and consumers have more of an affinity for those vendors that offer more pertinent information, instruction, and offers that add convenience to their lives. 3 Copyright 2014 NGDATA

4 Not Without Hurdles (Overcome-able Hurdles, no less) It is no secret that banks have much more data on their customers than technology companies which rely solely on website data for their personalization. This presents an exciting opportunity for banks to leverage their data to better personalize user experiences. However, it is clear that there are issues that banks have when dealing with that data, which include: Hundreds of internal data sources: The number of data sources is ever growing. After every M&A, banks add new systems and many more streams of data into their IT infrastructure. Each system and data store holds different information and a limited view of the consumer. The data could be stored in many forms including relational databases, XML data, Data Warehouses and enterprise applications such as ERP and CRM. This creates hundreds of data silos, each reflecting a small slice of the consumer. This fragmented data must be addressed in order to gain a better view of the customer. Growth of external and unstructured data: Banks also have a large amount of external and unstructured data about their customers in the form of tweets, Facebook posts, searches, website visits, streams, videos and so forth. In fact, a large portion of the data being created is either unstructured or semi-structured, and cannot be easily stored and analyzed using traditional systems. At the same time, the percentage of data that businesses can process is steadily decreasing as traditional systems, which are not designed for today s depth and breadth of unstructured data, are inadequate. Storing, indexing and analyzing massive data: Dynamically scaling storage capacity without any disruption to mission critical applications is a big challenge. Finding actionable insight among the massive structured and unstructured datasets, and delivering that with sub-millisecond latency is like finding a needle in a haystack. Being able to query data across multiple clusters of commodity servers and aggregate the results into meaningful insights is increasingly difficult with traditional technologies. Velocity of data creation: The speed of data creation across multiple channels is unprecedented. Banks need to be able to process data more quickly than in a batch mode or they will lose precious time in making offers to gain customer value. It has become critical to not only process static data and consumer profiles, but also their interactions with the data in real time so banks can gain actionable insights to make more timely offers. Fragmented view of consumer: Even if banks were to aggregate data from hundreds of internal and external sources and put it into a unified system such as Hadoop, the information would still be in multiple silos. Additionally, matching information about a customer from multiple data sources will be important especially down to the individual level. In a nutshell, simply integrating and aggregating data from multiple sources does not provide a single view of the customer something that is essential for more sophisticated personalized marketing and loyalty programs. 4 Copyright 2014 NGDATA

5 Organizational readiness and skillsets: The volume, velocity and variety of unstructured data makes it impossible for organizations to store, index, search and analyze massive amounts of data using traditional systems. In fact the traditional systems are inadequate for unstructured data, rapidly changing schema and elastic scaling of storage. On the other hand, most banks do not have sufficient organizational expertise and skillsets to deal with the complexities of Big Data management systems such as Hadoop. The learning curve, complexity of data management and need to integrate different modules from the Hadoop stack makes it necessary for banks to staff and have the right technology to pursue meaningful Big Data projects, where you can gain true customer intelligence. Banks used to have to rely solely on their customers intent, and product considerations and purchasing decisions were entirely customer-driven. Now, once you are able to acknowledge and overcome the hurdles, productively utilizing customer data can allow businesses to determine what a customer is most interested in and create a personalized experience where content, products and/or services are presented to customers before they even realize they need them. Key emerging truths Amidst the larger trends, challenges, and opportunities driving banking into the future, several key truths have emerged: Transactions are a source of intelligence. For many years, credit and debit card transactions were processing tasks to be recorded and stored. Now they are essential sources of data that can help you understand each customer s behavior, needs, habits, and preferences. As the primary holders of that data, banks are in the best position to wield that intelligence to better serve customers. Customers are demanding more control. Financial services is an increasingly self-service world, and customers expect services at their fingertips -- anytime and anywhere. Equally important, they want more choices, more convenience, more personalized offerings, and a more holistic view of their banking relationship across products, services, and organizational silos. Banks must accommodate these demands. Location is an increasingly important factor. To play a bigger role in each customer s financial life, you need to have greater awareness of where your customers are conducting their financial transactions. The gold standard for 1:1 marketing is to deliver the right offer to the right person at the right time -- and in the right place. Banks that can make use of location data have an advantage. Real time is the only time that matters. Integrating, analyzing, and acting on data from multiple sources can t happen fast enough. You need to be able to glean insights from multiple data streams and act on those insights immediately -- before opportunities are lost. Partnerships are necessary to growth. While banks possess the financial transaction data that can fuel new insights, turning those insights into new products and services requires a team of professionals with resources beyond the traditional banking world, including technologists, retailers, and telecommunications service providers. 5 Copyright 2014 NGDATA

6 Customer-Centric Banking Banks that can turn these truths into advantages will be more likely to succeed and prosper in the near future, ushering in the era of Customer- Centric Banking. This customer-centric strategy requires the anticipation of future needs looking at behavioral patterns, market trends, and user experiences for proactive measures to secure a personalized, unique and memorable experience across multiple channels. This, in turn, enables the customer to feel understood and valued, and more likely to develop a loyalty that will be a good basis for customer retention, up-selling and cross-selling. It also requires that companies go beyond placing customers in aggregate categories and create what NGDATA likes to call individual Customer DNA to specifically target content at the individual level, based on preferences derived from all available data sources. Customer DNA must include up-to-date, well-organized data points of each individual customer, prepared and ready to deliver content at the most appropriate time and place. No more hunting in pools of raw interaction data, no more batch processing or broad, static segmentation exercises companies now have access to thousands of relevant metrics for immediate action. By creating Customer DNA, companies can access all the data on a customer to predict the propensity they might have for any [new or existing] product, a service or a particular content offering. This propensity is calculated based on a number of machine learning algorithms, and also updated in real-time, using all incoming interactions. Data-driven Applications and Machine Learning for Customer Satisfaction and Privacy Thousands of companies are using big data and analytics to gain insight into their data. And while visualizing data can be helpful, graphs alone don t cut it. What businesses need are data-driven applications that help employees do their daily jobs better while wowing the customer. More importantly, these data-driven applications must be actionable and based on individual preferences. For instance, they should alert a marketing or salesperson each morning with a notification such as: Here are the 50 customers that might churn in the next 30 days. It is very likely that the customer would appreciate the effort of his bank to deliver a message or service that would give him a reason to stay. He knows you have sufficient information about him to deliver a more personalized experience now do it. This is how big data processing can create real business value by providing finite and actionable insights for employees that allow them to better serve their existing and prospective customers immediately. 6 Copyright 2014 NGDATA

7 Data-driven applications create true business value because they provide users with actionable tasks in real time, are scaled for the enterprise, and remove human subjectivity via machine learning. Machine learning encompasses the algorithms, optimization and learning tools that interact with the data, thereby eliminating any human interaction/intervention between the data being generated and the offers or services being delivered to the customers ensuring customer data remains secure and private. The sheer mass of data on customers is not possible to process in one data scientist s human brain. Machine learning must be used to analyze and deliver instruction on what should be done to better the business. So, instead of a data scientist looking deeply at a section of the data, the systems are looking at and devising outcomes from all the data mainly due to the ever growing volume of data and the need to quickly make something of it. And as more data is fed into the system, machine learning continues to get smarter to deliver the best, most relevant content to customers. But, as mentioned earlier, it doesn t make any sense to simply keep making graphs about data and big data. Graphs don t immediately change the situation, improve results nor increase customer experience. Companies need to focus on the business problem, have clear goals, and introduce data driven applications based on machine learning to deliver more automated and actionable results for the problems of the business. There are a lot of solutions available to work with big data, and now they are not only allowing the ability to search many of the databases that hold big data, but also aggregate, analyze and visualize that data. At the end of the day, the more content and data companies have on their customers, the better their ability to quickly drive actionable results and deliver greater revenue to the business, while ensuring privacy and convenience for the customer. But, remember, the key to delivering superior customer experience is to contextualize their data, and get personal understand your customer at the individual level, understand lifestyle to deliver products, services and content that are pertinent to them, via the right channel, at the right time. The end result is a happy customer and happy business! To discover more about NGDATA or how Lily Enterprise can help you solve your customer experience management challenges, please go to or contact us at 7 Copyright 2014 NGDATA

8 NGDATA, Inc. All rights reserved.

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