Learning Launch to Build an Early Warning System

Challenge: I am a member of my organization’s Transformation team. Our challenge is to build a  mechanism which will help one of our biggest clients to proactively identify future dissatisfied customers  (the end users). Currently there is no such mechanism available with the client to capture the same.  Implementation of a robust mechanism will help the client to improve customer satisfaction and protect  revenue. In terms of customer base, the project scope includes all their customers in the USA region, in  terms of data accessibility and usability, the project scope includes only the customers’ change request  emails that they send to the client.

I want to mention one very important point here, doing a customer survey to identify dissatisfied  customers is not an option as the client wants to do it without the customers/end-users realizing that  they are being assessed so that client can take proactive actions.

Selection: We have formed a small team of 6 people who are all familiar with Design Thinking tools and  activities. Our team is a combination of innovation, analytics, transformation expert from my  organization and Subject matter expert, account manager from our client organization. We want to  build a mechanism/tool to identify dissatisfied customers using their change request emails database.  We are now ready to try learning launch. A learning launch is an experiment/a series of experiments  conducted quickly and inexpensively.

Application: For our first learning launch we focused on gathering our thoughts on how the data to be  used. We made a list of criterion basis which the emails will be classified between good/bad/neutral. We  also decided on how the mechanism is going to work at a high level (as depicted in the below image).

Instead of building and testing a full-blown tool, we created a macro-based tool on excel. We used a  sample of 300 emails to test if our assumption that dissatisfied customers can be identified from the

tones of their emails is correct. We took the learnings from our first learning launch and moved ahead  with the second learning launch. In second learning launch we built a tool called ‘Early Warning System’  using advanced NLP, N-Gram charts & sentiment analytics. This tool analyzes unstructured text from  emails, gauge customer sentiment, flag negative emails quickly & accurately, thus enabling concerned  teams to take prompt remedial action.

Insight: By using learning launch tool, we quickly tested our assumption and concept. We spent very  minimal money to test if our hypothesis was right. We gathered our learnings and built the final tool.  The client was supremely delighted to see the results. The tool has been deployed and currently it is  running with an accuracy of 65%. As time passes the accuracy will keep on increasing.

Approach: Learning launch is a great tool to be used especially during prototyping phase. It lets you test  key customer, execution and defensibility assumptions. If the hypothesis is incorrect, it helps you to  learn fast and modify/change your hypothesis or gives you a confirmation to scrap the idea. Although  the tool has been deployed, we are still gathering insights to improve the tool’s accuracy. We will be  using these insights in our next learning launch activities.

