DIGITAL BANK GROWTH – LEARNING LAUNCH APPROACH

Challenge & selection

I’m part of the product team at the first digital bank in a developing country in South East Asia. We have  launched our digital product, which is a current account and debit card (as a bundle) opened 100%  online via eKYC in partnership with a software company who provides solutions to fraud prevention and  credit data scoring (“trust score”). With this convenience, we have more than 2000 customers registered  on daily average. However, after careful analysis, we realized that this number is mostly “registered  customers” to receive our onboarding gift (vouchers and cash-back benefits), not a loyal/ long-term  customer. This means we are facing financial loss by giving offers to customers who won’t stay with us.  For this reason, our team is conducting data analysis to gain an insight about customer behaviors and  patterns of usage upon opening our product vs what the other rival banks are offering to turn “one-time  customers” to “loyal customers”. I chose the learning launch approach because the marketing growth  team has come up with a lot of interesting ideas (via crowd-sourcing and competitive landscape analysis  and even rich experience) and this approach allows us to test whether one idea is validated as a  business opportunity and worths investment.

Application

First, we reconsider our registration process to see if a better filtering/ categorization of potential  customers can be obtained (how they know about our products – organic, social media, affiliated links,  etc. and whether they have accounts at other banks as well as their daily spending behaviors). As we  have realized the gap to be improved internally, we started working with our vendor to enhance the  process as well as system compatibility. After some enhancements, our product team developed test  cases and started testing the enhanced environment before going live. We created many profiles of  persona to check if the filtering/ categorization works well. The whole process will take approximately 1  month from idea initiation to going live updated version.

Next, with these qualified customers, we started tracking their behaviors with weekly reports (times  logged in/ out, numbers of purchases made per week, the average amount spent, on which platforms,  linked to any e-wallet, etc.). From such, hypothesis can be generated. For example, a large amount of  customers aged between 25-40 tend to use the account to pay for electricity/ water bills and make  purchases on e-commerce. Then we can think of:

1. Launching the cash-back scheme for those who use their account to make electricity/ water  bills. (The finance team then started calculated the good ratio of cash back so that the bank can  make some marginal profit)  2. Establishing partnerships with the e-commerce companies for discount vouchers to those who  use the bank account to make payments

The aim of these initiatives is in the end retaining valuable and lifelong customers who will bring profits  to our bank.

For each of these schemes, we designed a survey to ask for customers’ selection and whichever wins will  be launched first within one-month trial timeframe. We continue tracking customers’ behaviors with

this new campaigns to see their reactions and decide whether to move on with it or consider other  alternatives.

Insights

With this approach, our marketing team and product team have coordinated to make assumptions and  launched them to verify the effectiveness. We have compared the expected results to the realistic ones  which display quite a gap. We have been able to identity it, revise and improve it as a long-term project  with clearly defined KPIs.

Approach

Depending on the problems to solved next time, either this approach or others (visualization, story  telling, mind mapping) will be applied.

