Challenge​:   My company provides Human Resource Management software, and the customers are small  businesses who use the platform to buy health insurance for their employees.

As part of buying health insurance, the small businesses have to upload documents which verify the  company and the employees. We were seeing a large number of 1) incorrect documents there were  uploaded (ex. instead of state certified payroll proof they were uploading an excel sheet) 2) wrong  quarter or year for the documents 3) not all employees and owners were in the documents. This led  to back-and-forth between us and the customer, which overall delayed the process and made it a  bad customer experience. This also led to higher operations cost for us.

Selection:  Given that we had over 87 different types of documents, we wanted to experiment with a few  different solutions before implementing a larger solution. I decided to use ​Learning Launch​ to  experiment with a few different solutions the cross-functional team identified as part of the “what if”  state of the design thinking process. The goal was to find the best self-service solution which would  enable the customer to upload the correct document with all the information required. This would  decrease the time to get the health insurance and decrease our operations cost.

Application:  Our hypothesis was that if we were to give a sample document, the timeframe of the document and  a confirmation modal to ensure all employees are listed, users will upload the correct document.

I decided to experiment with just 2 different documents (instead of 87 types) to learn quickly. The  design team quickly put together a mini-document-upload flow. This had a sample document type,  instead of saying last 2 years or last quarter, we clearly displayed the years. Example: we displayed  April-June 2017. We also had a confirmation box that prompted the user to confirm all employees  are listed.

This was all developed in less than 2 weeks and launched as an A/B test. This was the quickest way  to ensure our hypothesis was tested. I also identified that if this hypothesis would work, we would  work on building the mini flow for all the different types of documents.

After running the experiment for a week, we saw a 93% decrease in incorrect documents that were  uploaded. It was evident that our hypothesis was validated. We then expanded the solution to the  remaining document types.

Insight:  The insight here was that the Learning Launch is a power tool to quickly experiment with different  hypothesis without investing a lot in design and engineering. A clear hypothesis really helps guide

the experiment. It is also important to identify the next steps in case the hypothesis fails or is  confirmed to align the teams.

Approach:  Overall Learning Launch was a powerful tool for us to test hypothesis. As a retrospective, I would  have broken the test further and tested sample document, displaying the dates and the employee  confirmation modal separately. This would have clearly signaled which solution had the biggest  impact.

