Growth Hacking with Data Science — 600% Increase in Qualified Leads with Zero Ad Budget

About 2 years ago, I worked as a data engineer alongside a team of extra-ordinary gentlemen where our daily activities involved keeping the data lake (3 node Hadoop Cluster) running. We had standard Hadoop ETL jobs starting with Sqoop jobs extracting data from our OLTP system (MSSQL Server) and loading into HDFS. Transformations with data flow language, Pig Latin,
and loading into Elasticsearch for search analytics and Kibana dashboards for visualizations.

As with every other data engineer in a space where business expectations assumed that your role was more than maintaining data lake infrastructure and ETL pipelines, we expected to generate insights from the data as well as create predictive algorithms.

In the rest of this blogpost, I will be diving into one of the use cases where we built an end-to-end ML solution, Lead Scoring Engine. The solution started as a decision-support for Customer Care Representatives in qualifying leads into an application that created a viral effect yielding about 600% increase in qualified leads with zero ad budget.

Business Expectations of Data Science Projects

After a series of stakeholder consultations in formulating a business problem to solve using data science, we committed to helping the business unit achieve one of its key objectives — Increase the number of leads by X% through a…