A new model of personalized banking in the future

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1 A new model of personalized banking in the future Universal Banking Solution Systems Integration Consulting Business Process Outsourcing

2 A new model of personalized banking in the future A recent report by the Boston Consulting Group states that worldwide, retail banking accounts for more than 50% of all bank earnings. Considering the importance of this segment, it is worrying that the uptake of new customers is slowing down, especially in the western world. Hence it is becoming even more critical for banks to focus on existing customers, not just because new ones are hard to come by, but also because they (existing customers) cost much less to serve. In a hard fought marketplace, abundant with choice, customer retention doesn t come easy. It takes a deep understanding of customer needs, and ensuring that the products and services are designed in a manner to fulfill the same. What s more, with customers demanding that banks pay attention to their smallest requirement, the only way to keep them satisfied is through highly personalized offerings. But the sheer size of retail customer numbers makes this task very difficult. In a workaround of sorts, limited by imagination, technology and implementation costs, banks across the world employ one or more of the following in order to personalize their products and services. Business intelligence using analytics Analytics typically deals with historical data and enables banks to use past customer transactions to predict future behavior. Customer and transaction data is run through an analytics engine and statistical models are used to unravel the product usage pattern following which customers are bucketed into groups. For instance, if analytics indicates that people who have just started to work look for personal loans, car loans and so on, banks, armed with a list of such products, can undertake a suitable marketing campaign targeting those in the newly employed group. The advantage is that different data sources are connected to a single warehouse that throws up valuable information, opening up selling opportunities. But, given the time lag between the analysis and the actual campaign, there s a strong possibility of critical parameters in the customer s status changing, rendering the offer irrelevant. For instance, a customer who wanted a mortgage a couple of months earlier, might have already arranged for one with another financial institution by the time the bank approaches him with their product. Also, this model targets the mass market and does not provide single customer information, and hence does not enable personalization in the true sense. Further, this method is quite inefficient as people in the same bracket might have very different needs, which a standardized offering cannot satisfy. Real-time analysis of customer interactions Under this method, banks track customer transactions irrespective of the channel used by the client and try to make an instant offer based on the intelligence built into the system or they issue a standard offer based on predefined rules, irrespective of the existing relationship. While a customer is transacting, a customer interaction management engine, configured with a set of business rules initiates (or prompts the customer service executive to take the next best step. If this happens to be a product offer, and the customer accepts it, he or she is guided through the entire purchase procedure. In case the customer declines, the rejection is recorded and the bank will offer another product in a subsequent transaction. This is analytics of a different kind at work real-time, specific and 02

3 quick that offers a greater degree of personalization than the first model. Although the real-time nature of the interaction increases the probability of conversion to an actual sale, this technology-empowered approach is again based on historical data primarily, the customer s transaction data which means that it suffers from both, a degree of latency and an incomplete customer view. Moreover, success largely hinges on the ability of the system to integrate with disparate banking channels/ back end systems to gather customer intelligence, as well as the ability to integrate with existing origination systems in order to facilitate purchase by the customer. While the front end is required to display all customer transactions Credit Card, Mortgage, CASA (Current and Savings Account) and even Forex the front end systems in use might not support real-time viewing of the complete customer relationship. So, despite the presence of an engine to utilize the information and the facility to process and make a suitable offer, the bank might fail to take into account a particular transaction resulting from a particular system and this could lead to a loss of potential opportunity. Building one-on-one relationships This is a feature of the traditional brick and mortar model, wherein dedicated relationship managers connect with customers, understand their needs and suggest suitable banking products. Although accuracy levels are high in this approach, as the relationship manager is conversant with the context of the need, enabling the bank to make a personalized offering, there are issues of scalability and cost, because building one-on-one customer relationships is time consuming and expensive. It is therefore sustainable only with segments such as High Net Worth Customers, as the investment yields returns only when the ticket size of transactions is high. While banks continue to implement the above-mentioned strategies, they will also have to decide if these are indeed the best ways to identify specific customer needs and provide personalized offerings, as technology adoption by customers has undergone a sea change in the last 2-3 years. For instance, use of the Internet for purposes beyond traditional applications and the adoption of the smartphone. The Internet and personalized banking Although most banks use any or a combination of the above approaches, these models provide only limited customer understanding as they primarily use previous interactions to identify customer needs. This drawback necessitates the use of a superior model, one that will indicate current customer requirements and thereby allow banks to make right and timely offers. This is where the Internet and emerging technologies can play a major role. The Internet has become an integral part of everyday life, so much so that the old adage, Tell me who your friends are and I will tell you who you are, could well read, Tell me what you do on the Internet and I will tell you who you are and what you want. Given the pervasive use of the Internet, it is quite possible to read customers thoughts simply by knowing what they do on it, as it reflects their current state of mind. As of December 2011, the world had over two billion Internet users, nearly half of them from Asia, over 22% from Europe and 12% from North America. Statistics say that in the three months between October and December 2011, on average, Internet users in Canada spent 45.3 hours online; the corresponding figure was 38.6 hours for the U.S., 35.4 hours for the U.K. and 30 hours for South Korea. 03

4 The rapidly growing number of Internet users and the enormous amount of time they spend on the Web give banks the perfect opportunity to gain valuable insights into online behavior, understand customer needs and make personalized offers. The fact that they have access to the entire banking relationship further helps matters. This can be understood better with a few illustrations: A person who is searching for hotels, places of interest in a tourist location and comparing travel fares online, is in all probability a potential traveler to whom banks can offer travel cards, foreign currency and travel insurance. Banks can also direct such a client to travel companies with whom they have tied up. Banks can proactively approach someone who is visiting property sites and comparing mortgage rates online, with their own housing loan products. If the person concerned is also searching for rental information, they can offer suitable advice on the assumption that he or she plans to buy property for investment purposes. Conversations on social networking sites are also indicative of a person s habits and future plans and can be used effectively to target customers with appropriate products and solutions. A futuristic model But how can banks access this treasure trove of information, understand customer needs better, make informed decisions, and effect successful sales? One way to go about this is to build components that can reside on customers desktops, tablets, mobiles or any other electronic devices used to connect to the Internet, that can listen to what the customer is doing and relay this to the bank, who can use this information to understand customer needs better. Take the case of Facebook apps (although not in the same league of comparison), which when downloaded, enable the site to access user information. In a similar manner, these proposed components can listen in to the customer s Internet usage. But banks will need to seek customers permission to install these to track their online behavior, website visits and data searches. This is just the first step and they will need to build guidelines to ensure privacy and prevent misuse over a period of time. This will ensure that the bank is not privy to everything that a customer does and it is the customer who decides what the bank can listen to (by means of a provision to adjust the settings). Admittedly, this is an idea that is yet to be tested, and besides technical feasibility, also needs to be evaluated on the following practical aspects: Infrastructure and process Banks have to ensure that they have the required infrastructure, which facilitates tracking and storing of such data feeds, and defined processes and time frames for archiving and purging old data. They also need to define the process of feeding this information into an opportunity engine and using this information to generate relevant offers for the customer. In addition, they need to formulate the metrics for success measurement and fine tune the listening engine alongside the opportunity engine, to improve the accuracy of the opportunities identified. Privacy policy It goes without saying that banks have to respect the privacy laws of the various countries in which they plan to implement this model. Among other things, this involves seeking regulatory permission and convincing the regulators that the data so acquired will be used only for business purposes. Banks should also have their own privacy policies defined and implemented. 04

5 In addition, they need to formulate procedures to protect the collected data, ensure compliance and also set up a robust security mechanism. Customer trust Customers might be hesitant to share not just their financial data, but also personal information related to their behavior, interests and the like. Banks have to be prepared for questions like How will the data be used?, Will I benefit in any way? and Can I opt out from such an arrangement in the future?. More importantly, they also need to reassure customers that the information will not be misused. It is therefore vital for banks to set about gaining customer trust. A strong brand name will definitely help. It would be even better if regulatory authorities could certify banks with a robust security infrastructure. Business case There are various costs involved in building this model, related to policy formation, software development and distribution, customer awareness building and so on. Banks should therefore conduct cost benefit analysis to determine if it is worthwhile to make the shift. Advantages This approach is very beneficial for customers with low product awareness. Take the case of a person traveling abroad for the first time who has absolutely no knowledge of international travel cards. His bank can offer him one upon learning of his possible travel plans from his Internet searches. Another customer wanting to invest in a mutual fund might be facing a problem of plenty, and could really benefit from some targeted advice from a bank tracking his Internet search activity. The offer of a tangible benefit in exchange for personal information a shopping coupon, for instance will further encourage customers to participate in such programs. Conclusion In the light of the ease with which customers switch banks today (8.7% of retail banking customers in the U.S. shifted banks in 2011 according to a study), it is imperative for banks to turn the spotlight on customer retention. The need of the hour is to gain deeper customer understanding and bridge the gap between customer wants and bank offerings. The development and implementation of the new, sophisticated Internet-based model, with its high probability of success, will give a fillip to personalized banking and bring about a paradigm shift in the retail banking space. While the idea sounds somewhat farfetched right now, a few banks have already taken baby steps in this direction by integrating social media into their regular banking channels. A few years hence, this model will very likely evolve to complement the existing traditional models and perhaps even overtake them. 05

6 References thefinancialbrand.com/17355/jd-powersresearch-new-bank-account-customers/ /02/canadians-more-time-online.html T C Dinesh Senior Principal, Business Consulting, Infosys 06

7 About Finacle Finacle from Infosys partners with banks to transform process, product and customer experience, arming them with accelerated innovation that is key to building tomorrow s bank. For more information, contact Finacleweb@infosys.com Infosys Limited, Bangalore, India, Infosys believes the information in this publication is accurate as of its publication date; such information is subject to change without notice. Infosys acknowledges the proprietary rights of the trademarks and product names of other companies mentioned in this document.