One of the perpetuating myths about decision capabilities and steering the organization is that growth is in the hands of a few – the strategic and tactical analysts and the decision-makers. The core reason for such a myth was because complex decision support systems, data warehouses and analytical tools were only provided to senior management.

In contrast, for the last few years we have witnessed increasing numbers of people within an organization making decisions at the operational levels, be it logistics operations on the ground, customer care center representatives interacting and resolving critical customer queries and complaints, or bank representatives at the branch opening accounts and cross-selling insurance and other loan policies to customers, or marketing teams doing promotional campaigns and providing customer offers on the spot.

The CEO of any organization would be more than happy if such a myth is shattered, because that will provide senior management under the CEO to delegate a lot of operational/tactical decision-making and focus on more strategic initiatives to explore growth opportunities. Another key advantage for the CEO is increased employee engagement and motivation in organizational growth because employees would feel empowered to make core customer-related decisions – such as decisions that ensure the customers are satisfied with quicker resolutions of their problems and needs. The operational decisions may not be strategic in nature, but if we take the combined effect of those decisions over a period of time, they become critical in contributing to the bottom line and also smooth running of any organization.

One of the differences between traditional BI users and operational users is the time window in which they have to make decisions is very small, and yet they have to be aware of the various recent events and actions to make informed decisions, because some of those situations requiring operational decisions (e.g., account closure, customer defecting to competition, an order cancellation, shipment delays or legal lawsuit) can have business impact.

Over time, organizations have matured enterprise information management systems, with data warehouses as the core foundation. EIM provides a knowledge base from various corporate data assets, with insights into the trends helping strategic and tactical users realize the business value. Even today, the data warehouse and EIM systems’ reach has been under the control of the business analyst doing query/analysis, executive management dashboards showing performance measurements, KPIs and aid in decision-making processes at strategic and tactical levels. There is a growing need to extend this wealth of information to the number of people involved in operational decisions, and quickly, too. This is the true spirit of EIM systems: enterprise-wide information insights for decision-making at strategic, tactical and operational levels.

The financial industry is becoming increasingly more competitive. Having knowledge of a consumer, his or her business behavior, socioeconomic relationships, lifestyle and demographic information is fast becoming a tool for actionable insight that provides ammunition to companies for a competitive edge. The critical success of an organization is directly dependent on its ability to extract data-driven insights and react faster than its competition.

Why Analytics? The Industry Challenge

Every growth opportunity comes with its own challenges and various factors which influence the outcome of actions we take. Figure 1 shows a four-dimensional view of factors that are deciding the future course of information’s evolution.

Volume factor: There has been a huge breakout in terms of data volumes being processed, gathered and managed for each customer-related characteristics, which could be related to demographics, social networking, buying patterns, payment trends, credit ratings and history, interaction via multiple channels of customer services, customer responses to various marketing campaigns, feedback, blogs and several others.

Technology factor: The technology scaled up. It is trying to keep pace with demand to process huge volumes of data, and we are seeing various kinds evolving in the market today. In the early ‘80s and ‘90s, people were more interested in knowing what happened and based on that were able to work on forecasting the revenue figures, product demands and other such strategic decisions. Today, we have dashboards and scorecards at the fingertips of decision-makers and allow them to do various kinds of analysis to see different perspectives. Figure 2 shows the technology evolution wherein the “What happened?” query has moved into a “What will happen?” sphere.  The next stage from here on, “How can I make it happen?” is about decision automation.

Number of decision approvers and factors: The increasing customer base, business operations, global ventures as well as the products/offering portfolio increase have effectively led to a major change in the way that organizations work. Decision authority has been on an uptrend in the operational space, and the number of people making daily decisions that directly impact the customer revenues and other customer-related aspects critical to an organization are increasing. This factor has put a major thrust on the technology in the information management space to reach out to operational-level people. These people need to be better equipped and to have access to the huge analytical data warehouses hosting the corporate customer transaction information necessary for making decisions at various levels.

Decision latency factor: A critical aspect considered the key differentiator from competitors is the power of providing context. Get relevant information at the right time to make the right decision. Decisions at operational levels are now being made in minutes, or even seconds (as in automated systems such as airlines reservations).

Every customer interaction offers an opportunity to influence buying behavior, churn and decisions that affect profitability. Higher expectations from customers for personalization require context sensitivity for relevance. The only way to manage this effectively is for organizations to have a 360-degree view of the customer at every touchpoint of interaction.

Such influence on customers and profitability can only be achieved via analysis. Analytics and integration of such systems bring the key operational insights from various perspectives to the front. Touchpoints include marketers, salespeople, merchandisers and customer care representatives who typically do not have analytical capabilities or skills in their jobs. However, today BI systems with data analysis, mining and analytical capabilities are closing the loop between operational and analysis processes toward effective business actions in everyday activities.

To fully operationalize BI is to embed the BI capability directly into the processes and systems where the majority of customer transactions/interactions are happening.

Embedded Analytics in Action - Credit Card Processing Example

To better understand the applicability of the various analytical solutions at different stages of a banking business process, consider a credit card processing example. The different stages of how a typical business process would be executed are showcased in this section. We’ll look at a very high-level business view of four key stages of any customer lifecycle, with banking as the example industry. Figure 3 depicts the customer lifecycle management phases applicable to any banking organization.

Figure 4 is the elaborated process that can be applicable to any banking process, be it loan request or a credit card processing request.

The starting point of any customer/business interaction is the customer acquisition subprocess, which includes marketing campaigns, mailers, online advertising, branch staff interaction phone calls or referrals. Once the customer is interested in product/services offered by the banking organization, the process of applications, documentation and collaterals take over. This cycle eventually ends with the customer opening an account for the services offered. Ongoing risk monitoring on activities of customer and customer buying/paying behavior, and support related to various customer queries are operational tasks being performed throughout the lifecycle of customer and relationship with bank.

Opportunities to do analytics exist at each stage of the cycle, starting from insights into which customer segments to target, down to once the customer is engaged, continuous monitoring to observe the buying behaviors and payment patterns, detecting fraudulent transactions or exploring the opportunities to do cross-sell based on certain events and transaction activities in the customer account.

Embedded Analytics in Action – Credit Card Processing

Traditionally, BI and analytics have been applied to the information collected and the transaction with the customer has been complete. The key reason was to delineate between the transaction processing systems and the analytic decision support systems. However, with the key factor of decision latency getting reduced, the only option available is to start embedding BI during the process of customer transactions and yet keep the process fluid in terms of performance.

Figure 5 represents an online credit card processing example. During the customer lifecycle, it exposes opportunities to leverage embedded analytics using the data warehouse/BI infrastructure for automating the decision power and bringing a more practical approach of operational BI.

Each of the dotted lines represents analytical opportunities that can be explored during that particular business process stage. It is important to start right from the selection of which market/customer segment to target for the marketing campaigns, analyze the buying behavior/patterns of customers in that specific segment, and look at the competitors’ market size and spread before deciding on what product/services to campaign for. Use of fraud detection through patterns analysis techniques and establishing relationships between unrelated events that otherwise were never possible to explore can give better insights on cross-sell, customer churn and frauds. For example, credit card customers buying at specific retail outlet or malls are the largest defaulters when it comes to making their payments on time.

These are seemingly unrelated events and it is difficult to come to a logical conclusion that such events are for real. However, examining analytics within the context of the business process would reveal shortcomings either in the proper segmentation for marketing campaigns, a lack of proper or complete financial background information or other customer demographics gathered during the process, or a flaw in the credit card approval process itself.

Applicability to the Financial Industry

Because of the dynamic nature and influencing factors on the banking industry, the products and services offered by banks are always under constant threat of risk exposure. Banks need to work with factors including market conditions, global economic situations, central reserve bank rate variations, currency fluctuations, prevailing inflation rates and other dynamic factors which can impact the profitability of banks significantly if not factored into the forecasting and planning analysis. Factoring such variables is not an easy job, and certainly not possible through standard analysis and manual reporting techniques. Statistical and mathematical models are tools  to help define the right models and embed those models in the operational systems for right-time decisioning.

In banking, identifying risks and managing exposure are the most critical aspects for improved profitability. Banks providing a collection of products and services use various analytical models based on customer activities and transactions; these in turn provide various risk variables that need to be tracked.

Substantial changes in recent years with volume of data, evolving technology, the number of decision-makers and reduced decision latency are putting extreme pressure on organizations to look for innovative ideas in the technology space to ensure they continue to grow, remain agile, competitive and derisked. A closer look reveals at analytical opportunities available during the customer lifecycle with any organization. The majority of interactions are with the operational business functions including customer care centers, branch offices, marketing and sales personnel and online systems. The most interesting aspect of traditional BI/analytics has been that only recently are operational users being viewed as core decision-makers affecting the organizational bottom line and customer base.

Hence, the need arises to embed analytics into the operational systems and touchpoints with customers. This translates to decisions backed by insights and customers satisfied with products/services and quicker turnaround on their queries, as well as organizational opportunities to expand business in geography, products/services and customer segments.

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