Demystifying Data Analytics

By: Rajeev Kakar

Why is Analytics Important?

In businesses where there are large quantities of customers, Analytics is a true differentiator and a competitive advantage. There is abundance of data available to decision-makers, thanks to enhanced data storage capabilities and increased computing power due to advancements in information technologies. Businesses across all sectors, especially the ones serving large number of clients, such as healthcare, telecommunications, hospitality, insurance, and particularly banking, increasingly rely on Analytics, in order to gain competitive advantage and to predict customer needs and behavior so as to serve them best. Every instance a client has an interaction with the business, in form of a financial transaction, or new product application or customer service calling, or a payment, produces multiple data points. Considering millions of clients having thousands of such interactions in a month, significant amount of data is produced by the business daily. 

“Analytics is the art of turning data into business intelligence in order to maximize business growth, to enhance profitability and to help organization achieve its strategic objectives.”

The primary challenge of the Analytics function is how to create business intelligence out of this raw data, by applying business process knowledge, financial tools, quantitative techniques, statistics and optimization, leading into sound strategic decisions by business leaders. In order to achieve this core objective, not only a sophisticated and experienced team with a diverse set of skills has to be composed, but also the raw data needs to be organized in a logical and easily accessible structure. 

In order to make the most out of this function, Analytics has to be driven from the top at the CEO level, needs to be made a part of the company’s DNA, must be adopted by all business seniors and should be used in all critical business decisions. Organizational alignment of the Analytics unit should ensure full independence and involvement in all aspects of the business. 

Adopt the 3 R’s of Analytics

Data analysis should be used to do a predictive analysis on how to optimize 3 R’s of any business and to addresses the trade-off among the Three (3) R's : Risk, Revenue and Response

Managing risk has been the traditional way of utilizing Analytics. That is why this function has traditionally not been placed in the front-end of most organizations. However, this is a sub-optimal use of Analytics. Not only can Analytics can achieve significantly much more than risk minimization, but also businesses should NOT aim to minimize risk but to optimize it. Trying to minimize risk is a short-sighted strategy that often leads to businesses failing to realize the inherent opportunities that exist. As a principle, a business should NOT avoid risk but should arm itself to be able to MANAGE risk. The goal should be to make risk management into a competitive strength for any business because, inherently, any business has a whole spectrum of risks that need to be managed. If risks did not exist, the entry-barriers would be low and the specific business opportunity would become unattractive. So, it is important to understand that risk needs to be accepted and to be narrowed down to an individual customer level, so that a customer-centric risk management process can be deployed. The use of analytics to understand customer risk profiles based on the raw data available, is what helps businesses identify risk profiles and patterns in a manner which is predictable, controllable, measurable and price-able, profitable and sustainable. This approach helps identify situations where resulting revenues and profitability can more than outweigh potential downside risk, leading to the desired objective of maximizing profitability. Predictably measuring this is a key responsibility of Analytics.

"Therefore the key objective of Analytics is to identify risk situations and convert them into opportunities that are predictable, controllable, measurable and price-able, profitable and sustainable"

Maximizing risk- adjusted Revenue is the important business objective to ensure that any risks assumed by a business are predictable and more than offset by the revenues on a sustainable basis....and the only reliable way of enabling businesses to be able to predictably measure and calculate such trade-off lies in the power of leveraging analytics-led real-time decision making. While, naively, businesses focus more on investing in the tools, databases and data warehousing capability to achieve this, what is often ignored is the need to create a culture which empowers the Analytics resources to safely focus on opportunities which seemingly involve potentially increased risk-taking so that business actions are executed in a customer-centric manner using a personalized data-driven approach. The softer power of the right culture and empowerment on the end-to-end design of SOPs where decisions are data-driven with high precision is more powerful to achieve than in merely investing in creating a functional which has all of the latest tools, techniques and data available, but not the empowerment. Such investments in building analytics capabilities in organizations mostly end up failing or not delivering the desired value because, despite being fully equipped with tools, the functional team of specialists often lack the fundamental empowerment to question existing risk management norms, to challenge actions based on the use of measures that efficiently trade-off between risk and revenue, and to create campaigns that are aligned with such a risk-reward balanced approach while ensuring that each stimulus is individual customer-centric. 

As per classic, high risk means high returns. The science and art of Analytics is to be able slots customers based on their revenue potential across a spectrum of high to low ranges. Once this revenue potential gradation is established, the power analytics helps further refine the gradation against the respective risk-profile gradation to establish a customer slotting on a risk-adjusted basis. Needless to say, it is important to identify and accept risks that are predictable, then to identify which of these risk are price-able at a level that customers will buy, and only then can any customer offering be profitable on a sustainable basis. Such accurate customer selection and precise individual-centric design of customer offerings can only be done on a predictable and reliable basis by leveraging the data footprint that exists and is being created as footprint on a rel-time basis - reflecting the true inherent attributes of each indivdiual.

Accelerating Response third element of the 3-R philosophy, which helps build competitive advantages for businesses....as speed of decision making is of essence in today's world of multiple choices and customer distraction. Here again, the power of data and analytics is critical.

The definitions of 'response' can vary by situation. 

In its simplest form, response is the speed and efficiency at which a business gets back to and serves its clients. In that context, the faster the response, the higher is the risk and expense, hence this is also a component of profitability. Sometime, response time does not have any expense implications and it is just a function of focus and organizational alignment. Analytics can measure the effectiveness of response by looking at various customer satisfaction metrics, “turn-around time” statistics, approval rates, transaction data, etc.

In a more evolved form, response could also be defined as customer acceptance rate to a marketing offer, eg., in a direct mail, tele-calling and/or email marketing context. In that definition, risk and response rate of a client are typically correlated. The reason is that if the risk of doing business with a client is low, the n such a client may typically be spoilt-for-choice have many competitive offers to choose from and revenue pools shrink, as such a client demands better terms. also, such a customer tends not to be very responsive to marketing offers. Therefore, low risk also means low response. The real power lies in identifying those segments of customers who are not easily identified for offerings and in designing customized value based offerings for them.

In summary, the correlations among the 3 R’s of Analytics are complex, and require multidimensional and multivariate analysis to be rigorously performed. However, what is needed more for delivering success through analytics is to ensure that an empowered team of specialized analysts are selected and placed at the forefront of the organization, independent of all other functions, and provided a real P&L responsibility.

If analytics is allowed to take real-time data-driven decisions that are customer-centric and situation-centric, only then can any business derive real value by 'Empowering its people and enabling their success in enriching the lives of its customers and stakeholders'!