Customer is one of the largest private sector bank in India and provides a wide array of financial services to corporations, MSME, Agro, Retail and many more industries. The banking major envisions excelling in customer delivery through personalized banking solutions for all their customers. One of the core values of this organization is to focus on the needs of their end customers.
Challenges in providing personalized banking solutions
In a service-based economy, organizations strive to derive revenue by creating and nurturing long-term relationships with their customers. To realize its goal of increasing customer engagement and providing an enhanced experience to their banking needs, the banking major wanted to appropriately evaluate opportunities and risks. This required a robust, agile and scalable analytics tool to provide rich insights from the available customer data.
Providing personalized banking solutions
In retail banking, where excellent customer experience is at the core of their operations, this bank needed to maintain quality of the services provided to them. Evolving technology has resulted in creating multiple channels of communications to access banking services. Hyper-competition and loss of “personal touch” resulted in reduced stickiness and switching costs of customers.
Understanding unstructured data and converting it into usable data
The bank wanted to differentiate itself from those in competition, it wanted to make use of the large pool of available data to offer targeted products and services designed to cater to their customers’ specific needs. The bank received huge amount of unstructured data, typically in the form of deposit and withdrawal transactions, ATM transactions, service requests, reversal requests, loan related transactions, etc. The organization had to identify traits from millions of transactions every single day. It required an integrated data analytics solution that could process, analyze, and tag the deluge of unstructured data generated every day to make effective, customer-focused decisions.
Big bets on Big Data saves time and costs
MediaAgility closely worked with the bank’s IT function, BIU (Business Intelligence Unit), and middle/upper level management. The functions encountered a significant challenge, which was to build personalized product offerings and communications based on analytics on huge data to enhance customer experience. All the while following strict security compliances related to customer data. There was constant pressure on the bank to spend a large part of their budget on building systems and processes. In order to keep up with the fast paced changes, the organization wanted to evaluate and improve their day to day operations.
Transactions > 10Million/day
Processing Time 35 minutes
Look-alike data modeling < 1 minute
MediaAgility consulted the banking major with the idea of fast data analysis possible through Google BigQuery. The bank receives and runs millions of transactions every day. The implemented analytics solution helped the bank analyze transactional behavior patterns (for example, frequency, monetary value of a customer’s profile and transaction). The process previously took more than 24 hours which eventually reduced to a mere 30 minutes. That’s how MediaAgility helped the private bank make sense of big data and deliver and market personalized banking to all its customer base.
The ETL (Extraction Transformation and Loading) tool developed by MediaAgility used BigQuery powered by Google Cloud Solution in the background of its data operations. ETL is the set of functions combined into a solution that enables easy ‘data extraction’ from numerous databases, applications and systems, ‘transforms’ it as appropriate, and ‘load’ it into another database, a data mart or a data warehouse for analysis.
Bank’s business operational unit heavily relied on an on-premise statistical tool for data analysis. The flow of action in the tool comprised of Compilation Phase and Execution Phase.The internals of data processing involved complex steps to tag and categorize transactional data. This took the bank and its operations more than 24 hours to get through with the processing. MediaAgility designed a seamless solution to ensure that customer enjoyed uniform access to customer data in lesser time. This was done by leveraging BigQuery that could process millions of data in few minutes. The cloud implementation enabled consistent customer experience across all channels. The chosen solution serves following parameters that ensured project success:
- 360 degree view of customer data: The analytics implementation gave banks a comprehensive view of customer information. The bank has a fully-functional business intelligence division which run campaigns based on customer behavior and demographic data. The division now proactively works on bank-wide loyalty program to enhance customer experience.
- Faster time-to-market: With a structured data in place, the bank now experiences quick decision making, hence faster time-to-market.
- Recognize customer value: With an effective mechanism to understand real value of a customer’s profile and assets, the bank was able to deliver targeted and differentiated service.
- Personalized product offering: The solution has fully transformed their functioning. The bank now offers a consistent and context sensitive experience.
- Data masked before restoring to cloud to ensure compliance: The solution ensures sensitive information stays safe while copying production data into non-production environments for the purposes of data analysis. The original sensitive data is replaced with fictitious data so that production data can be shared safely with non-production users.
MediaAgility provided the bank with a platform to understand their customer’s banking needs and service them better through tailoring personalized banking solutions, all the while saving time and money.