Problem statement
Most SaaS organizations spend a chunk of their revenue for various marketing initiatives - digital marketing, media outreach, search engine optimization, and more.
However, if there’s a way to target a highly qualified set of customers to buy your product, the organization reaps multiple benefits, such as enhanced revenue generation, higher deal closure rates, and increase in profit margins.
Task
An organization that offers a hiring assessment platform is looking at reducing its yearly marketing spends and you have been appointed as the Machine Learning engineer for this project.
Your task is to build a sophisticated Machine Learning model that predicts the probability percentage of marketing leads purchasing their product, based on information provided in the given dataset.
Dataset
The dataset consists of parameters such as the deal value and pitch, the lead’s source, its revenue and funding information, assigned points of contact for the lead (internal and external), and the like.
The benefits of practicing this problem by using Machine Learning techniques are as follows:
We challenge you to build a model that successfully predicts the probability percentage of a marketing lead to convert into a client and purchase the product.
Prizes
Considering these unprecedented times that the world is facing due to the Coronavirus pandemic, we wish to do our bit and contribute the prize money for the welfare of society.
Overview
Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experiences without being explicitly programmed. Machine Learning is a Science that determines patterns in data. These patterns provide a deeper meaning to problems. First, it helps you understand the problems better and then solve the same with elegance.
Here’s presenting HackerEarth Machine Learning challenge: Reducing marketing spends
This challenge is designed to help you improve your Machine Learning skills by competing and learning from fellow participants.
Why should you participate?
Who should participate?
Tutorials