Challenge  I work as a consultant in banking and my client was preparing the launch of a new pure-digital  bank for young professionals. The core of the product would provide an app to open and use  current & savings accounts, and debit & credit cards.    As they were preparing the market entry, they wanted to put aside some budget to incentivize  new customers to actively use their new account & card. For example, they could do it by  providing “cashback” on transactions (e.g. you get 20$ back in your account if you use your card  at least 5 times in a week) or by “promoting deals” with third party merchants (e.g. you get 20%  off a burger at a popular chain if you pay for the burger with your new credit card).  In the case of “promotion deals”, merchants would fund part of the deal with the bank, making  this option cheaper for the bank than the “cashback” option. Let’s assume, for example, that with  20$ the bank could either incentivize the customer with cashback (i.e. I give you 20$ if you  transact with your credit card this week) or give a 30$ equivalent promotion deal (i.e. you get 30$  discount off your restaurant bill if you pay by card – with 20$ funded by the bank and 10$ funded  by the restaurant)    Their challenge was the following: how do we invest our 20$ budget on incentives? Specifically,  how can we know if it is better to provide the customer with 20$ cashback or 30$ discount at a  popular restaurant?      Selection  I decided to propose to my client to use the Learning Launch tool to solve this challenge. Instead  of deciding internally based on bank’s biases and any public data available (which was very  limited on this topic), I thought that, as the video lesson described, learning launch could be use  to test customer preferences directly with the bank’s customers and let them decide directly.  In addition, as the bank was planning to gain customers organically and progressively, the  learning launch tool was relatively low risk, as the bank was able to focus the test on a limited  customer sample (e.g. first 200 customers)    Application  We therefore included a feature in the new bank app that would allow the following: a new  customer, after getting their new account and card, would receive smartphone notifications  providing them with the following message: “Enjoy our new customer promotion – open to see  your reward!”.  Then, when opening the link, customers would be prompted to a choice between two offers: they  could choose between the 20$ cashback promotion, or the 30$ restaurant discount promotion.  In this way, the bank was (i) sure that they would provide customers with their preferred choice,  and (ii) able to get a better understanding of customers preferences around incentives    Insights  This solution provided the bank with great insights:

  They understood that majority of customers would choose the 20$ cashback option

  They were able to segment customers based on their choice (not all customers like that  restaurant chain, but if the discount was on another product maybe they would pick it  instead of cashback)

  After adding additional “promotional deals” options to customers (e.g. with electronics  stores, e-commerce websites, retailers), they found out that some merchants are valued by  customers more than others

Approach  This approach allowed bank to understand the value of learning launch. They gave options to  their first customers when they launched the new app and, based on the feedback gathered, they  were able to adjust and complete their offering.  For example, they started adding additional promotional deal with more merchants and remove  deals that customers did not like.    Also, they also used the approach a bit differently on a small number of customers. They started  increasing the 30$ restaurant discount gradually until they reached the level where customers  chose that instead of the 20$ cashback. In the example, they found out that with a 40$ discount  most customers would choose the discount and not the cashback anymore.  In this way, they were able to go back to the merchant with real data and use that to negotiate a  new deal were the restaurant would fund a larger part of the promotion.

