To democratize the big data experience for business users
As the frequency and scale of data creation in banks becomes faster and bigger than ever before, the first challenge for many is how best to make this data comprehensible to every business user in the company, even the less data literate.
Traditional Business Intelligence (BI) systems in place still require the expertise of data scientists or data analysts to make sense of complex data and dish out insights. A great disparity is formed here. Front line business users and subject matter experts- those who are familiar with the real story behind the data- aren’t empowered to seek and find answers to their own questions.
The end result? Power is locked in the hands of the arcane few. Enterprise decision makers are unable to access the data in a meaningful way. And big data is held back from its full potential.
This in turn opens the floodgates to a stream of misinformation and serious, albeit accidental mistakes.
As Gartner points out, smart data discovery provides users the ability to “fully interpret [visualizations]… alongside the visualization, inform[s] the user about what is the most important for them to act on in the data.”
Such platforms empower mainstream business users to become “citizen data scientists” who can make big data a part of the day-to-day conversation. By taking advantage of natural language processing, interactive reports with data visualization and ‘explorable’ data from multiple sources, business users can perform advanced analytics and reach their goals in lesser time.
They can thus reach a whole new level of granularity in a single interface and have the freedom to engage in rapid decision making to drive critical business outcomes.
“Banking” on Smart Data Discovery to unlock customer value
In an era of waning customer loyalty and eroding product differentiation, banks have a rare opportunity to reinvent themselves again- with big data discovery.
According to EY’S Global Consumer Banking Survey, 40% of customers expressed “reduced dependence on their bank as their primary financial services provider and have used non-bank providers for financial services in the last 12 months.”
Smart data discovery can help banks better understand their customers and prospects to unlock customer value and achieve profitable growth. Here’s how:
1. Customer Segmentation
While demographics and customer transaction history have traditionally been at the foundation of customer insight, behavioral and attitudinal insights are now gaining importance as product use and channel selection become more differentiated. With the explosion of data from multiple sources, it is now an absolute must for banks to integrate data from internal sources such as CRM with various external channels (social, mobile, web etc.) in order to serve customers better.
What if an everyday business user in the remotest branch of a bank could draw on data from disparate sources such as social media, mobile application usage, clickstream data etc., for a well- rounded customer view? What if they could ask the data questions in natural language, in an intuitive format (text, voice-enabled, etc.) and find answers in the most pertinent and meaningful visualization? What if salespeople could look across data sets on their smartphones in a manner similar to data scientists analyzing information on multiple dashboards to figure out current needs of their customers? What if relationship managers are able to have instantaneous access to a meaty customer profile snapshots to suggest products that suit individual spending patterns?
Such are the capabilities that smart data discovery promises. It facilitates creation of sharper segments and helps to see the existing customer base in new ways, beyond basic segmentation.
Image Source: www.mckinsey.com
The above exhibit illustrates how big data discovery helps banks define & prioritize customers for retention, migration, expansion (acquisition), or deprioritization.
2. Personalization & Targeted Marketing
Data discovery and advanced analytics help to personalize customer experience and provide timely and relevant offers that customers are more likely to accept.
Based on deep customer insights in the form of predictions about financial status, life events etc., bankers can determine a customer’s propensity to buy/sell banking products and increase the effectiveness of cross-selling. This in turn improves the ratio of products per customer and enhances “customer stickiness.”
The frontline can be equipped with the next best solution so that they can easily figure out what’s the right next product to sell and quickly make that offer to an indecisive customer. With an up-to-date “propensity to buy” model built using knowledge collected from multiple channels about that customer, information about similar customers, as well as all major events relating to this one, the representative can offer the right product at the right time with much greater certainty.
For example, for a customer who has posted about his upcoming Japan trip on Facebook, the bank can not only help him liquidate his FD, but also offer him a forex card with low transaction costs.
Classification algorithms such as neural networks or decision trees provide powerful insights into customer preferences based on their lifestyle choices or life stage events. Nearest neighbor and linear regression are also powerful tools used to compare customers to their peers.
A noteworthy example here would be that of OCBC Bank. It responds to personal lifetime events and demographic profiles of customers to send them personalized messages and boost engagement.
3. Prevention of Customer Churn
With customer churn being high in financial services, bankers can also use advanced analytics and data discovery tools to find out the key causes of customer defection among certain groups of customers.
Customer profile and transaction data can be used to determine common characteristics of customers who have defected. For instance, if there has been a drop in transaction volumes or huge transfers made out of an account, it may signal that the customer is likely to leave. Social data from sentiment analysis can also indicate if particular segments are unhappy and are likely to jump ship.
Banks can then devise highly personalized retention campaigns to win such customers back. Slovakia’s Tatra Bank has used big data discovery to successfully predict and reduce customer churn for its credit card holders.
4. Reorientation of Distribution and Customer Engagement
As more and more customers demand to interact with their banks in many different ways, banks must develop omni-channel capabilities and thoughtfully designed end-to-end customer journeys. All channel decisions can be anchored in deep analysis of the bank’s existing data.
Using data discovery tools based on the bank’s existing data, a product-by-product analysis of each of the bank’s sales channels can be carried out to reveal channel opportunities outside traditional patterns. Armed with actionable insights into customer engagement and shifts in channel use, banks can realign their whole distribution approach to support omni-channel engagement and enhance channel effectiveness.
5. Fraud Mitigation
As financial institutions offer more and more options for mobile banking and payment services to satisfy consumer appetite, incidents of fraud have also seen a rise.
One of the key uses of data discovery today is in the area of fraud risk management. Advanced analytics can help in recognizing patterns of fraudulent transactions, identify the next fraud in progress, and recommend preventive action, thereby saving both the customer and the bank.
The reality is that, in the new digital banking model, data is a bank’s most vital asset. And banks that draw on big data discovery to create customer value will find themselves well placed to thrive in the financial industry of the future.
Latest posts by Krittika Banerjee (see all)
- 5 Steps: How to Embed Predictive Analytics in your Business? - April 12, 2017
- Intelligent Apps- Driving the Future of Tomorrow’s Enterprises - March 27, 2017
- The future of Underwriting with Big Data - March 14, 2017