In the current landscape, it is very important for us to continuously engage with our customers and understand them. Analysis of the customer data leads waste of a lot of time and this can be detrimental to the companies profits. To tackle this problem this solution was created.
What it does
Uses ML along with the built-in report functions of QB apps ,using pipelines, to extract important data and present it in an easy to digest format. It also makes searching through the records much easier with a visual interface.
How I built it
Connects a QB app with a mailchimp campaign, and whenever a user subscribes it calls a pipeline and uses machine learning on its name to determine gender of the customer. All the information immediately gets inserted as a new record in the customer DB, and it uses the in-built record functionality to view all the information in an easy-to-digest format. It gives us a pie chart for gender, a pie chart for location and a graph which tells us the number of customers who subscribed or unsubscribed on any given day. When the user unsubscribes , the subscription status of the user changes and the data is changed in real-time in the reports of the QB app.
Challenges I ran into
Understanding how to use quickbase
Accomplishments that I'm proud of
Finishing the project solo
What I learned
What's next for Mailchimp Quickbase integration
Controlling the mailchimp campaign spontaneously using nothing but the reports produced by quickbase.