Challenge Overview
Challenge Objectives
Two previous challenge in this series focused on predicting a most likely buyer from the list of first round bidders.In this challenge we want to focus on these improvements
-
Find characteristics of successful and unsuccessful bidders
-
Find characteristics of successful and unsuccessful deals
Project Background
Our client, a global investment bank, is looking to build a predictive analytics algorithm
to obtain characteristic insight based on historic deal data taken from their CRM
matching potential closing bidders on assets based on behavior in previous biddings.
-
Prediction algorithm will be used in client environment to predict most likely company to close the deal
Technology Stack
-
Data analysis in this challenge can be done using Python, R, .NET or Excel
Data description
Historic data is provided in two Excel sheets:
-
Bidder profiles - contains data about actual bids - Buyer’s lists from previous transactions, including details such as firm size, revenue, industry, etc. for each bidding firm and firm sold. Data also includes each bidding firm’s progress through each round until the final closing bid. This data is considered as ground truth. There is total of 42 variables (columns)
-
Seller profiles - contains data about firms sold in the bidding process - includes firm size, revenue, industry, etc. There is a total of 37 columns.
Explanations for all columns are provided in the forums.
Prediction requirements
Main goal in this challenge is finding useful characteristics of successful (or unsuccessful) bidders, or deals - essentially a heuristic that could be used as recommendation for the future bidding engagements. For example, we’re looking at these kinds of characteristics (this is just an example, these statements might not even be correct):
-
Successful bidders (they closed the deal) typically bid x% higher than other bidders
-
Deals that die usually have bids less than their asking price
It is up to you to figure out these characteristics and a way to get there - be creative! All the characteristics should be clearly derived from the input data set, without using any other external data sources. All characteristics should be backed up by data analysis done in Python, R, .NET or even Excel.
Our data set is not very large so we would like your input on which information you think would be helpful in the future to enhance the analysis. For example, financial data would be helpful, but are there any other fields that we should consider in the future?
Previous challenge winning submissions are provided in challenge forums.
Review will be highly subjective and done by the client. No appeals will be allowed.
Your submission should contain:
-
Data analysis scripts
-
List of characteristics
-
Summary document explaining the data characteristics and outlining the main findings - graphs and other visuals are highly encouraged
NOTE:
3 bonus prizes of 200$ will be awarded to the submissions having most useful characteristics - they will be awarded at reviewer discretion
Final Submission Guidelines
-
Data analysis scripts
-
List of characteristics
-
Summary document explaining the data characteristics and outlining the main findings - graphs and other visuals are highly encouraged