The use of big data — derived from applications such as enterprise resource planning, customer relationship management, social media and web — to deliver enhanced customer experience is radically transforming the marketplace
Leela is a regular shopper at one of Bangalore’s leading departmental store. This Diwali season as she made the final payment at the counter of her favourite store for the groceries bought, she received a text on her mobile that left her pleasantly surprised. It offered her a discount of 20 per cent on the purchase of a new set of jute furniture. She was awe-struck by this offer because she had mentioned a plan to buy a new sofa-set for the house only to a friend on Facebook.
Welcome to the technology that is radically transforming the marketplace. The last few years have paved the way for IT industry’s inflection point. New technologies have emerged as critical areas of offerings and differentiators for service providers, given the increasing benefits offered by them. One of these disruptive technologies that have become the centre of executive attention across industry segments — more so over the last year-and-a half — is big data. To be sure, big data is defined as the combination of structured and unstructured data that needs massive storage architectures. Structured data is derived from traditional applications like Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) tools and other applications while unstructured data refers to social media and other public cloud platforms beyond the perimeter of an enterprise.
Big data, converged with the exponential climb in enterprise mobility trend, is leading a disruptive phenomenon across markets. The application of statistical model and predictive analytics on big data for actionable insights, served at the point of consumption (mobile), is the next sunrise area for CXOs. Customers, system integrators and product vendors are all working on various big data initiatives as this technology is roaring into a strong potential.
According to a leading analyst group, the big data market will be anywhere between $80 billion to $120 billion over the next five years. Financial services will account for more than 40 percent of the potential spend.
The financial services industry is arguably the earliest adopter of big data solutions, owing to the major benefits for this industry such as risk management, fraud detection, anti-money laundering, and a whole new way of delivering unique customer experience. According to a recent McKinsey report, 15 out of 17 key sectors in the US have more than 100 terabytes of data stored and managed and the volumes are still growing. BFSI is the lead sector that manages multiple terabytes of data.
Many factors contribute to the swift adoption of big data in the financial services industry. Risk management is one of the key reasons; post the financial meltdown in 2009, government and financial institutions across geographies consider customer risk profiling and management as top agenda. Further, the apex banking bodies across countries are laying down rules for adherence to liquidity ratio levels, credit risk and regulation of the microfinance vertical. Traditional customer data alone is not sufficient for modeling the risk, hence spelling the need for analytics on a plethora of unstructured data too.
Another key factor is compliance to regulations and norms. With increased cross border transactions and FII-led investment, the banking industry has found itself looking for agile ways to ensure compliance with many laws like Dodd Frank, Basel II, among others.
Consumer is the king. Retail banking is undergoing the challenging phase where the customer has a choice — to interact, get informed, transact and sort her financial needs by picking one out of the several choices available. Provisioning an integrated and uniform customer experience across channels like web, mobile and in-person by predicting potential customer behaviour is a major initiative run across banks today.
Let me take you through a case scenario to show how big data is building itself as an enabler for banks to maximise customer experience and retention.
John Doe, a high networth individual (HNI) is the savings account holder of a multinational retail bank with his details existing in the structured core banking application of his bank. Consider a day when Doe transacts online for transferring money into his daughter’s account using his iPad. The system seems to have thrown an error and John leaves a complaint at the customer service hotline. A week down the line, he is chatting with his friend about his interest in buying a new house. This chat, on a social media platform, is also the same where he mentions to his friend about poor service quality of his bank. This is an unstructured data conversation occurring in real-time, on which his current bank has no information or control. Now, there are three data avenues: structured data from the core banking app, unstructured voice conversation on the customer support system that records it as a voice file and the social channel conversation which cannot be stored or controlled. Yet, it is Doe who is in the centre of all three, and could potentially churn away from the bank.
The big data usage applied in this context executes something like this — the know-your-customer (KYC) information is captured from the core banking application, social analytics tool captures the real-time social platform conversation and exercises sentiment analysis on it, big data content mining engine is used to analyse the voice and convert this to meaningful text and data. Further, the data record is modelled and interpreted and analysed to explore the consumer behaviour and present a Single View of the Customer (SVoC). With this intelligent and efficient analysis of the data that could have otherwise been left unrecorded and unheard, the bank will be prodded to immediately action on some key strategies that will turn this almost-lost customer into a happy one. A dedicated HNI relationship manager pops up on Doe’s iPad using Facetime and assures him that the money transfer issue will be resolved within the next one hour. A separate application trigger dovetails the home loan and realty management division of the bank to engage John in a conversation whereby he is offered a tour of potential up-market properties (based on customer analytics derived from the KYC) and also an immediate sanction of a specially tailored housing loan.
Doe is now a ‘delighted’ customer of the bank as his unstated customer needs are immediately serviced by the bank. This is how the bank manages to turn around the customer experience and avoids customer churn.
Given the benefits , it looks like banks will soon have to embrace this emerging trend. However, there are some crucial key performance parameters (KPPs) that business leaders and IT leaders in these organisations must consider. Instead of getting lost in the big world of big data, some key parameters must be judged regularly to ensure the success of investments made.
So, the siloed customer data and interactions must move into a centralised repository or dashboard of customer data and insights, sales and marketing departments must graduate to taking up social listening role and manage lasting relationships with customers rather than managing customer transactions. This functional scenario will in turn help the banks to offer customer solutions, instead of bank products and generate brand loyalists, instead of just satisfied customers.
In order to maximise the aforementioned KPPs, CXOs of financial organisations should be looking to engage with service providers who can work with them closely and arrive on business models with clear RoI intent. The project team that will be a combination of the client organisation and service provider should hence be a multi-disciplinary team of some specific skill-set holders like statistical data scientists, solution architects with sound knowledge of staging and platforming the data in Hadoop and other technologies, business analysts, infrastructure professionals with data virtualisation experience in parallel architectures/cloud architectures.
We are inching closer to the period when CXOs of organisations across sectors will have to exploit big data advantage to effectively enable meaningful insights from the enormous data lying within and outside their offices for obtaining competitive advantage in the market place. With the rise of social media, the need to harness data outside the enterprise has assumed an even higher significance. In the era of socially wired king-like customers, organisations can stand out amongst competition if they understand and approach their customers in the most informed and nimble manner and on a platform they prefer. Big data is proving to be a catalyst of such differential strategy of enterprises; as aptly articulated by Gartner, the leading analyst firm, “Information is the oil of the 21st century, and analytics is the combustion engine.”