Loan Application Reflection - Using learning launch tool

Challenge

I am working for a very large multinational bank. My team is responsible for managing customer facing digital financial products. One of our products is an Online AutoLoan origination application. Auto buying customers use this online application and to pre qualify themselves for auto loan they need to buy an automobile of their choice. Our team consists of Product team, IT Tech team, UX designers, Customer service Agents who own the application end to end.

The loan application pre-qualification is a 5 stage process in which we collect information from customers and allow them to submit a prequalification application.

Personal information (First Name, LastName, Email, Date of Birth, SSN), Residential Information (Residential Address, Address history), Employment Information (Current Employer, Employment history), Income details (Monthly, yearly household income, Expenses, assets). Finally, at the Application Submit stage customers can verify the information they entered and submit a loan application.

The challenge we are facing is the high dropout rate of customers who had begun the Loan application at the Personal information page at stage 1 but did not complete and submit the loan application at stage 5. Drop out customers discontinue application filling in between stage 1 to final Stage 5. We are not sure about the problems that are causing customers to drop out. We would find out these problems our customers facing which directly or indirectly attributing to the dropouts

Selection

Our team has already gone through several design thinking activities/tools. We emphatically gathered survey data from customers. After deeply reviewing and collectively brainstorming these customer reviews, we identified potential problem areas and end ends we can address and we also researched potential solutions while working with the process constraints. We are finally ready to try learning launches. We planned to quickly prototype user interface options of loan application and launch them to do AB test and multivariate testing

Application

For our First learning launch we made changes to our loan application user interface, instead of collecting the SSN of the customer on the first personal page we moved the SSN field on the last application submit page. Our hypothesis was customers are concerned about providing SSN. By moving the SSN field on the last page, customers will not need to make a decision until the last page and after filling all 5 pages customers would not like to waste their effort by abandoning application on the last stage. We also tagged each page with a UI tag so we can use web analytics to know on what pages customers are abandoning the process.

In our Second learning launch we simplified the application user interface. One of our hypotheses was the number of application dropouts might be due to ambiguous application questions so instead of asking users the annual salary we modify the application to include Per hr salary and number of hrs per week user work. This is mainly to include users who don’t have a single permanent job for the entire year.

In addition we also conducted a few additional learning launches for simplifying 5 page applications to one page application with prefilling the userdata on the application if the user already applied.

Insight

By applying a learning launch tool we AB tested our hypothesis. Our first AB test results showed that the users would not like to provide SSN immediately at the start of the application due to overall concern with application and data security. By changing the SSN field from first page to last page there is an overall 20% increase in application submission. The SSN shift allowed customers to enter data on 4 pages of the application; they were able to notice the changes to ensure customers about the security of the data. Results reflected that these changes created some stickiness and users are not abandoning the process after they fill 4 pages worth even when the 4th page had an SSN field.

Our second learning launch results showed there are almost no changes on application drop outs due to User interface modifications to reduce the income accuracies. Our hypothesis about drop out rate relating to the complexity of the question seems to be incorrect. We noticed that reducing the length of application from 5 pages to single page application was a great success. We were able to record almost 30% overall reduction in dropouts. Most users seem to prefer simple one page applications over longer ones. Results showed that users clearly like to prefill the application data instead of manually entering all fields.

Approach

After several learning launches we are able to accurately analyse most of our hypotheses. Learning launches results help to determine the best way to meet our customer needs and we made necessary changes to our online application and thereby reduce the application users dropouts. Using Learning launches we ran user interface testing in cycles and advanced our efforts for application drop out reduction. .


Loan Application Reflection - Using learning launch tool Challenge I am working for a very large multinational bank . My team is responsible for managing customer facing digital financial products . One of our products is an Online AutoLoan origination application . Auto buying customers use this online application and to pre qualify themselves for auto loan they need to buy an automobile of their choice . Our team consists of Product team , IT Tech team , UX designers , Customer service Agents who own the application end to end . The loan application pre - qualification is a 5 stage process in which we collect information from customers and allow them to submit a prequalification application . Personal information ( First Name , LastName , Email , Date of Birth , SSN ) , Residential Information ( Residential Address , Address history ) , Employment Information ( Current Employer , Employment history ) , Income details ( Monthly , yearly household income , Expenses , assets ) . Finally , at the Application Submit stage customers can verify the information they entered and submit a loan application . The challenge we are facing is the high dropout rate of customers who had begun the Loan application at the Personal information page at stage 1 but did not complete and submit the loan application at stage 5 . Drop out customers discontinue application filling in between stage 1 to final Stage 5 . We are not sure about the problems that are causing customers to drop out . We would find out these problems our customers facing which directly or indirectly attributing to the dropouts Selection Our team has already gone through several design thinking activities / tools . We emphatically gathered survey data from customers . After deeply reviewing and collectively brainstorming these customer reviews , we identified potential problem areas and end ends we can address and we also researched potential solutions while working with the process constraints . We are finally ready to try learning launches . We planned to quickly prototype user interface options of loan application and launch them to do AB test and multivariate testing Application For our First learning launch we made changes to our loan application user interface , instead of collecting the SSN of the customer on the first personal page we moved the SSN field on the last application submit page . Our hypothesis was customers are concerned about providing SSN . By moving the SSN field on the last page , customers will not need to make a decision until the last page and after filling all 5 pages customers would not like to waste their effort by abandoning application on the last stage . We also tagged each page with a UI tag so we can use web analytics to know on what pages customers are abandoning the process . In our Second learning launch we simplified the application user interface . One of our hypotheses was the number of application dropouts might be due to ambiguous application questions so instead of asking users the annual salary we modify the application to include Per hr salary and number of hrs per week user work . This is mainly to include users who do n’t have a single permanent job for the entire year . In addition we also conducted a few additional learning launches for simplifying 5 page applications to one page application with prefilling the userdata on the application if the user already applied . Insight By applying a learning launch tool we AB tested our hypothesis . Our first AB test results showed that the users would not like to provide SSN immediately at the start of the application due to overall concern with application and data security . By changing the SSN field from first page to last page there is an overall 20 % increase in application submission . The SSN shift allowed customers to enter data on 4 pages of the application ; they were able to notice the changes to ensure customers about the security of the data . Results reflected that these changes created some stickiness and users are not abandoning the process after they fill 4 pages worth even when the 4th page had an SSN field . Our second learning launch results showed there are almost no changes on application drop outs due to User interface modifications to reduce the income accuracies . Our hypothesis about drop out rate relating to the complexity of the question seems to be incorrect . We noticed that reducing the length of application from 5 pages to single page application was a great success . We were able to record almost 30 % overall reduction in dropouts . Most users seem to prefer simple one page applications over longer ones . Results showed that users clearly like to prefill the application data instead of manually entering all fields . Approach After several learning launches we are able to accurately analyse most of our hypotheses . Learning launches results help to determine the best way to meet our customer needs and we made necessary changes to our online application and thereby reduce the application users dropouts . Using Learning launches we ran user interface testing in cycles and advanced our efforts for application drop out reduction . .