Enhancing branch performance reports with natural language and reporting automation.

CLIENT: HDFC BANK 

INDUSTRY: BANKING AND FINANCE

FUNCTION: SALES

USE CASE: BRANCH PERFORMANCE REPORT

HDFC Bank, one of the largest private sector banks in India has its presence in both digital and brick and mortar branches. The banking giant has a distribution network of 5,130 branches across 2,764 cities. And recently having entered the digital space, HDFC churns out almost 85% of its transactions through digital channels.  

To increase customer satisfaction and ROI through digital channels, it had become imperative for the Branch Banking Heads (BBH) to closely monitor the performance of the branches in their regions.  

Extensive Manual Efforts for Building Branch Performance Reports

With a wide branch distribution network, HDFC bank has a long list of hierarchy starting from the branch banking heads (BBH), regional heads (RH), circle heads (CH), cluster heads (CH) and lastly branch managers (BM). Every month the bank sends out a monthly performance report to the branch manager. These performance reports are mostly in the form of tables and charts. 

The branch managers had to manually analyze these numbers, comparing it with the monthly allotted targets and interpret the insights to share it with the next immediate head – Cluster Head. The subsequent senior managers had to undergo the same procedure of manually collating all the branch performance reports of their region into one comprehensive report and present it to the next inline senior manager in the hierarchy. 

The existing system of report creation proved to be very time consuming and inefficient. The managers had to spend hours collating the data and creating personalized reports for the next inline, explaining the metrics in charts, graphs, and complicated diagrams. The manual effort required to provide personalized comprehensive reports was substantial and slowed down the overall deliveries.

Key problems identified:

  • Unavailability of personalized reports across all branches.
  • Branch employees spent too much time analyzing & interpreting data in hand.
  • BBHs could not do a quick analysis of the sales performance.
  • Manual data extraction from MIS for report creation.

Overcoming the hurdle of manual reporting

To address these reporting deficiencies, HDFC bank turned to vPhrase, a pioneer in Reporting Automation and Business Intelligence.

  • Using Phrazor, a natural language generation platform, HDFC bank automated its entire internal reporting process to quickly obtain on-demand reports in real-time with summarized insights in bullet points.
  • Sourcing data from MIS, multiple datasheets and files, Phrazor created a comprehensive overview of the performance of their digital channels right from the top.
  • The senior managers could parallelly compare the performance of each branch against allocated monthly targets and other regions at a granular level and identify shortcomings and growth opportunities.
  • Adding natural language to their reporting techniques, opened up worlds of time for the managers, enabling them to concentrate on other high priority tasks.
  • The Senior managers could now easily analyze and identify the performing and non-performing employees based on the KPIs.

How HDFC bank leveraged the natural language generation platform, PHRAZOR?

Using Phrazor the private sector bank could experience:

  • Minimized manual efforts in report creation.
  • Efficiently monitor and evaluate employee productivity in every branch.
  • Analyze & set realistic targets and goals for different divisions.
  • Introduce new levels of speed and data accuracy to the branch.
  • Make data-driven decisions to increase customer satisfaction.

IMPACT

6,250 Reports
Generated monthly for all branch managers

05 Days
Time expedited for monthly report generation

450 Hours
Of manual effort in MIS preparation saved

This is one of the many use-cases which Phrazor executes in the banking and insurance industry. To learn more about how Phrazor can help you, write to us.