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。