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.

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