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| Title: | 利用RFM模型與購物籃分析進行電子商務顧客分群與銷售策略之研究
A Research On e-commerce seller `s sales strategy using RFM Model and Market Basket Analysis |
| Authors: | 陳一慈
Chen, I-Tzu |
| Contributors: | 鄭宇庭
陳一慈
Chen, I-Tzu |
| Keywords: | 顧客關係管理
顧客分群
RFM分析
購物籃分析
關聯規則
Customer relationship management
Customer segmentation
RFM analysis
Marketing basket anaylsis
Association rules |
| Date: | 2021 |
| Issue Date: | 2021-08-04 16:40:05 (UTC+8) |
| Abstract: | 隨著電子商務零售市場成長，為因應競爭加劇的環境，無論在強化顧客關係管理或擬定產品銷售策略皆是業者需要考量的重點項目，RFM模型是在顧客分群最廣泛應用的方法之一，而購物籃分析可協助商家找出常被消費者購買的產品組合。
本研究以一間販售美妝用品的電子商務業者為例，藉由分析顧客的購買日期、金額、消費頻率建立RFM模型，從原始資料中找出一群回購率高、且為研究對象帶來高銷售額的常貴客，並利用購物籃分析從40,772件商品中找出1,049組有機率被消費者購買的產品組合。
除了RFM模型與購物籃分析，本研究亦配合敘述性統計使用，發現研究對象存在顧客流失率高、產品銷售效率低的問題，因此針對顧客關係管理、產品銷售兩面向提出建立在顧客分群結果為主的顧客忠誠計畫，與產品銷售策略上的建議作法。
As the e-commerce retail market grows, in order to respond to the increasingly competitive environment, both strengthening customer relationship management or product sales strategies are the key items that every business owner needs to consider.
The RFM model is one of the most widely used methods for customer segmentation. Marketing basket analysis can assist merchants in identifying product combinations that are frequently purchased by consumers.
The study tried to find people who have created high revenue and repurchased more frequently by analyzing every consumers’ recency, frequency, monetary and combinations of items that occur together frequency in transactions in an e-commerce cosmetic shop.
Besides the analysis of above, the study also did some research about the target shop’s operating performance and found the fact that the shop suffered from lack of selling efficiency and high customer churn rate. Therefore, the study offered some advices about customer relationship management and product sales strategies. |
| Reference: | 一、中文文獻
1.Michael J.A. Berry、Gordon S. Linoff，(2001)，資料採礦-顧客關係與管理計電行銷之應用。台北市：數博網資訊股份有限公司。
2.Michael J.A. Berry、Gordon S. Linoff，(2001)，資料採礦的理論與實務-顧客關係管理的技巧與科學。台北市：維科圖書有限公司。
3.謝邦昌、鄭宇庭，(2016)，零售業資料採礦：R及Excel的應用。台北市：新陸書局股份有限公司。
4.呂惠聰、強南囡、王微微，(2018)，客戶關係管理。台北市:財經前線文化出版
5.經濟部統計處，(2019、2020)，零售業網路銷售額統計調查。
6.行銷資料科學，常貴客？新客？ 讓RFM模型簡簡單單解釋一切！（附實現程式碼），2021年3月14日，檢自https://reurl.cc/qgmZ9y
7.行銷資料科學，購物籃分析 — Python實戰：如何找出商品搭配的總體策略？(附Python程式碼)，2021年3月17日，檢自https://reurl.cc/dGV7zy
8.行銷資料科學，你怎麼處理顧客交易資訊？Apriori演算法，2021年4月7日，檢自https://reurl.cc/ZGQXnW
9.行銷資料科學，來為RFM 客製化，打造自己的CRM說書人-【附Python程式碼】，2021年4月18日，檢自https://reurl.cc/O0XVby
二、英文文獻
1.Association rules and the Apriori algorithm: A Tutorial, Retrieved May 02,2021, from:https://www.kdnuggets.com/2016/04/association-rules-apriori-algorithm-tutorial.html
2.Distribution of retail website visits and orders worldwide as of 1st quarter 2019, by device, Retrieved March 28 2021, from: https://www.statista.com/statistics/568684/e-commerce-website-visit-and-orders-by-device/
3.Statista, US ecommerce grows 44.0% in 2020, Retrieved March 28 2021, from: https://www.digitalcommerce360.com/article/us-ecommerce-sales/
4.Statista, Global retail e-commerce sales 2014-2024, Retrieved March 30 2021, from: https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/
5.Chelsea Haynes, True Cost of Beauty: Survey Reveals Where Americans Spend Most, Retrieved March 30 2021, from: https://www.groupon.com/merchant/trends-insights/market-research/true-cost-beauty-americans-spend-most-survey
6.Leonard L. Berry, (1983). "Relationship Marketing," in Emerging Perspectives, Journal of the Academy of Marketing Science, no.23, pp236-245.
7.Rakesh Agrawal, (1994). Fast Algorithms for Mining Association Rules, pp1-6
8.Ron Kohavi, Rajesh Parekh, (2004). Visualizing RFM Segmentation, pp1-5 |
| Description: | 碩士
國立政治大學
企業管理研究所(MBA學位學程)
108363104 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108363104 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202100799 |
| Appears in Collections: | [企業管理研究所（MBA學位學程）] 學位論文
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