marketing_data

Context
A response model can provide a significant boost to the efficiency of a marketing campaign by increasing responses or reducing expenses. The objective is to predict who will respond to an offer for a product or service

Content
Age - customer's age at the date of the campaign
Education - customer’s level of education
Marital - customer’s marital status
Income - customer’s yearly household income
Kidhome - number of small children in customer’s household
Teenhome - number of teenagers in customer’s household
DtCustomer - date of customer’s enrolment with the company
Recency - number of days since the last purchase
MntWines - amount spent on wine products in the last 2 years
MntFruits - amount spent on fruits products in the last 2 years
MntMeatProducts - amount spent on meat products in the last 2 years
MntFishProducts - amount spent on fish products in the last 2 years
MntSweetProducts - amount spent on sweet products in the last 2 years
MntGoldProds - amount spent on gold products in the last 2 years
NumDealsPurchases - number of purchases made with discount
NumWebPurchases - number of purchases made through company’s web site
NumCatalogPurchases - number of purchases made using catalogue
NumStorePurchases - number of purchases made directly in stores
NumWebVisitsMonth - number of visits to company’s web site in the last month
AcceptedCmp1 - 1 if customer accepted the offer in the 1st campaign, 0 otherwise
AcceptedCmp2 - 1 if customer accepted the offer in the 2nd campaign, 0 otherwise
AcceptedCmp3 - 1 if customer accepted the offer in the 3rd campaign, 0 otherwise
AcceptedCmp4 - 1 if customer accepted the offer in the 4th campaign, 0 otherwise
AcceptedCmp5 - 1 if customer accepted the offer in the 5th campaign, 0 otherwise
Response (target) - 1 if customer accepted the offer in the last campaign, 0 otherwise
Complain - 1 if customer complained in the last 2 years
Country - customer's country

Inspiration
The main objective is to train a predictive model which allows the company to maximize the profit of the next marketing campaign.

本数据集旨在通过预测客户对产品或服务优惠的响应，以显著提升营销活动的效率，无论是通过提升响应率还是降低成本。具体目标为预测哪些客户将响应优惠。数据内容详尽，包括客户的年龄、教育水平、婚姻状况、年收入、子女数量、加入公司的时间、最近一次购买的天数、过去两年在酒类、水果、肉类、鱼类、甜品、贵金属产品上的消费金额，以及折扣购买、网站购买、目录购买、门店购买、网站访问次数等信息。此外，还包括客户在首次、第二次、第三次、第四次、第五次营销活动中是否接受优惠的情况，以及最后一次营销活动中的响应情况、过去两年是否投诉以及客户所在国家等信息。本数据集的灵感来源于训练一个预测模型，以帮助公司最大化下一次营销活动的利润。

搜集汇总

背景与挑战

背景概述

该数据集包含2240行和28列，记录了客户的基本信息（如年龄、教育水平、婚姻状况等）、购买行为（如各类产品支出、购买渠道等）以及对营销活动的响应情况。数据集的主要目标是训练预测模型，以预测客户对营销活动的响应，从而优化营销策略。