Using real-time analytics to transform customer experience


Senior Vice President – Product Management

 Vinoo Prakash is responsible for formulating the product and platform development strategies at SunTec Business Solutions. He plays a pivotal role in defining platform and product strategy and coordinating product life cycles. His responsibilities include evaluation of the platform architecture and technology used for SunTec’s products, development of road-maps and definition of feature requirements. A seasoned professional with more than 15 years of experience in SunTec, Vinoo previously held roles of Head – Pre Sales & Consulting in SunTec.

Jason is a private banking customer with one of Britain’s big five banks. As a busy professional, time is of the essence to him. He has made an appointment to see the mortgage advisor at his local branch this afternoon to discuss a new fixed rate mortgage deal as his current mortgage product is coming to an end next month. 

As Jason walks into the swanky bank branch, he is Led into a brightly lit meeting room where Brian, his mortgage advisor sits him down to discuss his mortgage requirement, sifting through over 700 compatible products. As they discuss Jason’s requirements further, Brian launches a quick report on Jason’s on-going relationship with the Bank. He notices that Jason has been a loyal customer for over 13 years and over that period has held multiple savings, investments and loan products with the bank. At this point, as Brian scrolls through the customer relationship page on the corporate CRM, he notices something interesting – the CRM has recommended a 5 year fixed 2.7% offset mortgage product for Jason tied to his existing current account with the bank. In recommending this mortgage product, the system has taken into account not just Jason’s current relationship with the bank, but also historical business intelligence data from the bank’s previous run-ins with customers of Jason’s profile. As a 36 year old married male who is an architect by profession, has an annual income of £50,000 and a relationship at one of the premium branches of the bank, Jason is one of over 18,000 customers with a similar profile at this bank. 

The CRM software took into account this profile information and recorded purchasing behaviour within this particular customer segment along with other external information about the current mortgage market.

Brian goes on to recommend this deal and also cross-sell a mortgage protection product to Jason, who shortly walks out of the bank happy with the prompt service he received and being able to get his mortgage and insurance sorted within 30 minutes. 

Brian is excited because with the real-time recommendations suggested by the system, not only was he able to further engage the client but also cross sell an additional insurance product. 

Welcome to the future of banking. Welcome to the power of Real-Time Business Intelligence. 

Times have changed and so have today’s businesses that run the global marketplace. Today’s customers are well-informed and demand services on their terms. 

To serve today’s customer requires the ability to make instantaneous decisions on products and services, taking into account a full spectrum of not just their relationship with the business, but also other exigencies of the market and ecosystem the business is operating in.

Traditional database solutions have been limited in catering to these needs in that they have been historically used to make long term, strategic decisions due to the rigid data latency issues involved. This had left a gap in the market for a “run time” solution that could help businesses make on the spot, tactical decisions with their data – this gap is now being filled by the state-of-the-art OLAP systems that power most Real-Time Business Intelligence platforms. 

An OLAP system is different from traditional OLTP systems in that it is geared for fetch data quickly (using efficient SELECT queries) whereas OLTP systems are aimed at processing transactions quickly (i.e. INSERT/UPDATE/DELETE queries). And because of this very difference, it is difficult for most data management systems to cater to both. 

When designing systems for the modern business, specific stress must be Laid on the quest for “fresher data”. Some of the key techniques used to generate this level of Real-Time Business Intelligence involve –

 * In-memory analytics: Allows for analytical computations and big data to be processed in-memory and distributed across a set of nodes.

* In-database analytics: Allows data integration and analytic functions inside databases so you won’t have to move or convert data repeatedly. 

* Grid computing: Allows for processing of jobs in a shared, managed pool of IT resources. 

Big data provides a fantastic opportunity to understand customers in ways that can genuinely transform the business. 

High-performance analytics can help organizations reach customers at precisely the right time and place, and hit them with the right message, so organizations can keep and grow their profitable customer base.

This helps make better pricing decisions, spot signs of customer disenchantment early on, and strengthen customer interactions.

As a Leading Revenue Management and Business Assurance solution, SunTec’s Xelerate platform comes with built-in real-time analytics. Many of today’s Fortune 500 companies are using the Xelerate platform to make informed business decisions and offer pointed solutions to their customers. Using the built-in realtime business intelligence, Xelerate can deliver real-time insights into customers and product offerings they are Likely to choose, aside from giving their business leaders a real-time view of profitability and revenue figures. 

On the technical side, Xelerate is compatible with the Service-Oriented-Architecture (SOA) which ensures flexibility, scalability and the quicker adaptation of the “system to market” demands in an enterprise. Xelerate can also be easily further extended to other business lines. 

While there are a number of upsides to implementing Real-Time Business Intelligence systems, there are challenges too. The inflexibility of legacy systems to transition data, the need to manage unstructured data that often requires significant manual effort and the fact that there might be security and compliance requirements add to the challenges.

As the global economy embattles the need for increased regulatory and compliance checks, today’s modern businesses need to put into use all of their resources to survive and thrive. Big data – both internal and external-provides this natural resource. Using high performance analytics as the fuel, businesses can often profit from the insight required to make instantaneous and homogeneous decisions across the organization, catapulting business towards success.