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AmExpert-2019

Analytics Vidhya AmExpert ML Competition

Link: AmExpert Hackathon

Problem Statement

Predicting Coupon Redemption

XYZ Credit Card company regularly helps it’s merchants understand their data better and take key business decisions accurately by providing machine learning and analytics consulting. ABC is an established Brick & Mortar retailer that frequently conducts marketing campaigns for its diverse product range. As a merchant of XYZ, they have sought XYZ to assist them in their discount marketing process using the power of machine learning. Can you wear the AmExpert hat and help out ABC?

Discount marketing and coupon usage are very widely used promotional techniques to attract new customers and to retain & reinforce loyalty of existing customers. The measurement of a consumer’s propensity towards coupon usage and the prediction of the redemption behaviour are crucial parameters in assessing the effectiveness of a marketing campaign.

ABC’s promotions are shared across various channels including email, notifications, etc. A number of these campaigns include coupon discounts that are offered for a specific product/range of products. The retailer would like the ability to predict whether customers redeem the coupons received across channels, which will enable the retailer’s marketing team to accurately design coupon construct, and develop more precise and targeted marketing strategies.

The data available in this problem contains the following information, including the details of a sample of campaigns and coupons used in previous campaigns -

  • User Demographic Details
  • Campaign and coupon Details
  • Product details
  • Previous transactions Based on previous transaction & performance data from the last 18 campaigns, predict the probability for the next 10 campaigns in the test set for each coupon and customer combination, whether the customer will redeem the coupon or not?
Dataset Description

Here is the schema for the different data tables available. The detailed data dictionary is provided next.

train.csv:

Train data containing the coupons offered to the given customers under the 18 campaigns
Variable Definition
  • id Unique id for coupon customer impression
  • campaign_id Unique id for a discount campaign
  • coupon_id Unique id for a discount coupon
  • customer_id Unique id for a customer
  • redemption_status (target) (0 - Coupon not redeemed, 1 - Coupon redeemed)

campaign_data.csv:

Campaign information for each of the 28 campaigns
Variable Definition
  • campaign_id Unique id for a discount campaign
  • campaign_type Anonymised Campaign Type (X/Y)
  • start_date Campaign Start Date
  • end_date Campaign End Date

coupon_item_mapping.csv:

Mapping of coupon and items valid for discount under that coupon
Variable Definition
  • coupon_id Unique id for a discount coupon (no order)
  • item_id Unique id for items for which given coupon is valid (no order)

customer_demographics.csv:

Customer demographic information for some customers
Variable Definition
  • customer_id Unique id for a customer
  • age_range Age range of customer family in years
  • marital_status Married/Single
  • rented 0 - not rented accommodation, 1 - rented accommodation
  • family_size Number of family members
  • no_of_children Number of children in the family
  • income_bracket Label Encoded Income Bracket (Higher income corresponds to higher number)

customer_transaction_data.csv:

Transaction data for all customers for duration of campaigns in the train data
Variable Definition
  • date Date of Transaction
  • customer_id Unique id for a customer
  • item_id Unique id for item
  • quantity quantity of item bought
  • selling_price Sales value of the transaction
  • other_discount Discount from other sources such as manufacturer coupon/loyalty card
  • coupon_discount Discount availed from retailer coupon

item_data.csv:

Item information for each item sold by the retailer
Variable Definition
  • item_id Unique id for item
  • brand Unique id for item brand
  • brand_type Brand Type (local/Established)
  • category Item Category

test.csv:

Contains the coupon customer combination for which redemption status is to be predicted
Variable Definition
  • id Unique id for coupon customer impression
  • campaign_id Unique id for a discount campaign
  • coupon_id Unique id for a discount coupon
  • customer_id Unique id for a customer

Notes

I have joined the train table with the other tables based on the respective unique id and have done the aggregating on these tables before joining. And i have used the LightBGM model for the classification which gave me the CV Score of around 0.89 and LB Score on the Analytics Vidya Leader Baord is 0.8199

Disclaimer

I don't own copyrights to data provided here. All the data are provided just for reference and educational purpose only.