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ВИКОРИСТАННЯ RFM-АНАЛІЗУ В СЕГМЕНТАЦІЇ КЛІЄНТІВ
І. В. Мірошниченко, В. Д. Алексєєва

Назад

DOI: 10.32702/2306-6806.2022.1.114

УДК: 330.4, 519.2

І. В. Мірошниченко, В. Д. Алексєєва

ВИКОРИСТАННЯ RFM-АНАЛІЗУ В СЕГМЕНТАЦІЇ КЛІЄНТІВ

Анотація

Статтю присвячено застосуванню RFM-сегментації та алгоритму Apriori Аpriori для виявлення сегментів клієнтів та асоціативних правил у споживчих кошиках. У результаті дослідження було вивчено поняття RFM-аналізу, визначено його переваги, етапи побудови моделі, способи застосування на практиці. Надано рекомендації щодо підготовки даних для виконання аналізу, визначено обов'язкові дані. Також ознайомлено з алгоритмом Аpriori для виявлення асоціативних правил у споживчих кошиках. Було визначено сфери застосування, його сильні та слабкі сторони, запропоновано загальний алгоритм його побудови та вивчено, як серед множини результатів знайти ключові правила. Після теоретичного ознайомлення було побудовано RFM-модель та використано алгоритм Apriori. Отримані результати проаналізовано, запропоновано подальші кроки дій для компаній, а саме: розробка комунікаційної стратегії для збільшення обсягів продажів завдяки email-ретаргетингу та таргетингу в соціальних мережах. Запропоновано, як можна проваджувати отримані результати для створення спеціальних пропозицій та рекламних оголошень.

Ключові слова: сегментація клієнтів; комунікаційна стратегія; RFM-аналіз; алгоритм Apriori; споживчий кошик; асоціативні правила; таргетована реклама.

Література

1. Ahmad Heru Mujianto, Chamdan Mashuri, Anita Andriani. Consumer customs analysis using the association rule and apriori algorithm for determining sales strategies in retail central. E3S W eb of Conferences — ICENIS. 2019. No. 125.
2. Andrew Aziz. Customer segmentation based on behavioural data in e-marketplace. 2017. 42 p. URL: https://uu.diva-portal.org/smash/get/diva2:1145508/FULLTEXT01.pdf (дата звернення: 14.01.2022).
3. Anish Nair. RFM analysis for successful customer segmentation. URL: https://www.putler.com/rfm-analysis/#What_is_Recency_Frequency_and_Monetary_analysis (дата звернення: 14.01.2022).
4. Arules: Mining Association Rules and Frequent Itemsets. URL: https://cran.r-project.org/web/packages/arules/index.html (дата звернення: 14.01.2022).
5. ArulesViz: Visualizing Association Rules and Frequent Itemsets. URL: https://cran.r-project.org/web/packages/arulesViz/index.html (дата звернення: 14.01.2022).
6. Data Visualization in R with ggplot2. URL: https://ssc.wisc.edu/sscc/pubs/dvr/index.html (дата звернення: 14.01.2022).
7. Derya Birant. Data mining using RFM analysis. URL: https://cdn.intechopen.com/pdfs/13162/InTech-Data_mining_using_rfm_analysis.pdf (дата звернення: 14.01.2022).
8. DoBy: Groupwise Statistics, LSmeans, Linear Contrasts, Utilities. URL: https://cran.r-project.org/web/packages/doBy/index.html (дата звернення: 14.01.2022).
9. Dplyr: A Grammar of Data Manipulation. URL: https://cran.r-project.org/web/packages/dplyr/index.html (дата звернення: 14.01.2022).
10. Fachrul Kurniawan, Binti Umayah, Jihad Hammad. Market basket analysis to identify customer behaviors by way of transaction data. Knowledge engineering and data science. 2018. Т. 1, № 1. С. 20—25.
11. Forcats: Tools for Working with Categorical Variables (Factors). URL: https://cran.r-project.org/web/packages/forcats/index.html (дата звернення: 14.01.2022).
12. Gert Stulp. R visualization workshop. URL: https://stulp.gmw.rug.nl/ggplotworkshop/index.html (дата звернення: 14.01.2022).
13. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. URL: https://cran.r-project.org/web/packages/ggplot2/index.html (дата звернення: 14.01.2022).
14. Ggpubr: 'ggplot2' Based Publication Ready Plots. URL: https://cran.r-project.org/web/packages/ggpubr/index.html (дата звернення: 14.01.2022).
15. Habil Gadirli. Segmenting Bank Customers via RFM Model and Unsupervised Machine Learning. URL: https://arxiv.org/ftp/arxiv/papers/2008/2008.08662.pdf (дата звернення: 14.01.2022).
16. Hadi Roshan, Masoumeh Afsharinezhad. The new approach in market segmentation by using RFM model. Journal of applied research on industrial engineering. 2017. Т. 4 (17). С. 259—267.
17. Hafsa Jabeen. Market basket analysis using R. URL: https://www.datacamp.com/community/tutorials/market-basket-analysis-r (дата звернення: 14.01.2022).
18. Holly Bastow-Shoop, Dale Zetocha, Gregory Passewitz. Visual merchandising. A guide for small retailers. Iowa State University, 1991. 68 p.
19. Jo-Ting Wei, Shih-Yen Lin, Hsin-Hung Wu. A review of the application of RFM model. African journal of business management. 2010. December Special Review, no. 4 (19). Pр. 4199—4206.
20. Michael Hahsler. Visualizing association rules: introduction to the r-extension package arulesviz. 2015. 24 p.
21. Mining frequent items bought together using Apriori Algorithm. URL: https://www.analyticsvidhya.com/blog/2017/08/mining-frequent-items-using-apriori-algorithm.com (дата звернення: 14.01.2022).
22. Mohamad Abdul Kadir, Adrian Achyar. Customer segmentation on online retail using RFM analysis: big data case of bukku.id. ICEASD. 2019. Vol. 4, no. 1. Pр. 1—11.
23. Mohammadreza Tavakoli, Mohammadreza Molavi, Vahid Masoumi. Customer segmentation and strategy developmentbased on user behavior analysis, RFM model anddata mining techniques: a case study. URL: https://www.researchgate.net/publication/330027350_Customer_Segmentation_and_Strategy_Development_Based_on_User_Behavior_Analysis_RFM_Model_and_Data_Mining_Techniques_A_Case_Study (дата звернення: 14.01.2022).
24. Nagesh Singh Chauhan. Association Rule Mining. URL: https://www.kdnuggets.com/2019/12/market-basket-analysis.html (дата звернення: 14.01.2022).
25. Onur Dogan, Ejder Aycion, Zeki Atil BULUT. Customer segmentation by using rfm model and clustering methods: a case study in retail industry. International journal of contemporary economics and administrative sciences. 2018. Vol. 8, no. 1. Pр. 1—19.
26. Package 'formattable'. URL: https://cran.r-project.org/web/packages/formattable/formattable.pdf (дата звернення: 14.01.2022).
27. Plot method to visualize association rules and itemsets. URL: https://rdrr.io/rforge/arulesViz/man/plot.html (дата звернення: 14.01.2022).
28. Ponlacha Rojlertjanya. Customer segmentation based on the rfm analysis model using k-means clustering technique: a case of it solution and service provider in thailand. Bangkok University, 2019. 103 p. URL: http://dspace.bu.ac.th/bitstream/123456789/4003/1/ponlacha_rojl.pdf (дата звернення: 14.01.2022).
29. Predictive segments using RFM analysis: an in-depth guide. URL: https://www.moengage.com/blog/rfm-analysis-using-predictive-segments/ (дата звернення: 14.01.2022).
30. Pushpa Makhija. RFM analysis for customer segmentation. URL: https://clevertap.com/blog/rfm-analysis/ (дата звернення: 14.01.2022).
31. RColorBrewer: ColorBrewer Palettes. URL: https://cran.r-project.org/web/packages/RColorBrewer/index.html (дата звернення: 14.01.2022).
32. Readxl: Read Excel Files URL: https://cran.r-project.org/web/packages/readxl/index.html (дата звернення: 14.01.2022).
33. Recency, Frequency, Monetary Model with Python — and how Sephora uses it to optimize their Google and Facebook Ads. URL: https://towardsdatascience.com (дата звернення: 14.01.2022).
34. Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin. RFM analysis. URL: https://link.springer.com/chapter/10.1007/978-0-387-72579-6_12 (дата звернення: 14.01.2022).
35. Roy Wollen. A modern approach to RFM segmentation. URL: https://cdn2.hubspot.net/hub/184373/file-41856256-pdf/docs/modern-approach-to-rfm-segmentation-ebook.pdf (дата звернення: 14.01.2022).
36. Scales: Scale Functions for Visualization URL: https://cran.r-project.org/web/packages/scales/index.html (дата звернення: 14.01.2022).
37. Selva Prabhakaran. The complete ggplot2 tutorial — part 2 | how to customize ggplot2. URL: http://r-statistics.co/Complete-Ggplot2-Tutorial-Part2-Customizing-Theme-With-R-Code.html (дата звернення: 14.01.2022).
38. Sohaib Zafar Ansari. Market basket analysis: Dissertation. 2019. 56 p. URL: https://run.unl.pt/bitstream/10362/80955/1/TEGI0458.pdf (дата звернення: 14.01.2022).
39. Vasilis Aggelis, Dimitris Christodoulakis. Customer Clustering using RFM analysis. URL: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.128.7091&rep=rep1&type=pdf (дата звернення: 14.01.2022).
40. Vijaykumar Ummadisetty. Online retail data set. URL: https://www.kaggle.com/vijayuv/onlineretail.com (дата звернення: 14.01.2022).
41. Прокофьева Д. Как улучшить рассылку: советы от маркетинговых платформ и экспертов. URL: https://www.cossa.ru/trends/276162 (дата звернення: 14.01.2022).
42. Ланц Бретт. Машинное обучение на R: экспертные техники для прогностического анализа. Питер, 2020. 463 с.
43. Разделяем клиентов по лояльности с помощью RFM-анализа. URL: https://emailsoldiers.ru/blog/rfm-analysis (дата звернення: 14.01.2022).
44. Таргетована реклама: що це, для чого потрібна, як ефективно використовувати. URL: https://bestmarketing.com.ua/ua/tarhetovana-reklama-shcho-tse-i-dlya-choho-vona-potribna (дата звернення: 14.01.2022).
45. Что такое RFM-анализ и как его применить в рассылке интернет-магазина. Dacademy. URL: https://digital-academy.ru (дата звернення: 14.01.2022).

I. Miroshnychenko, V. Aleksieieva

USE OF RFM ANALYSIS IN CUSTOMER SEGMENTATION

Summary

Digital technologies cease to seem complex and immensity and begin to necessitate their use in business. This is not about IT startups, but about the digitalization of internal business processes. One of the tools of a successful company is a well-developed marketing company that meets modern market conditions. Moreover, during quarantine restrictions, more and more companies are forced to go online and face the need to improve or only implement digital marketing technologies.
The article is devoted to the application of RFM-segmentation and the Apriori Apriori algorithm to identify customer segments and associative rules in consumer baskets. As a result of research the concept of RFM-analysis was studied, its advantages, stages of construction of model, ways of application in practice were defined. Recommendations for preparing data for analysis are provided, mandatory data are defined. Also acquainted with the Apriori algorithm for identifying associative rules in consumer baskets. The areas of application, its strengths and weaknesses were identified, a general algorithm for its construction was proposed, and how to find key rules among the many results was studied. After theoretical acquaintance, the RFM model was built and the Apriori algorithm was used. The obtained results are analyzed, further steps for companies are proposed, namely the development of a communication strategy to increase sales through email retargeting and targeting on social networks. Suggest how you can implement the results to create special offers and advertisements.
Search and development of effective algorithms and their implementation in customer relationship management systems allows to optimize work with customers in all directions and, above all, to conduct sound marketing activities designed, according to the identified benefits, directly for target segments of the customer base, ie to send advertising information is not chaotic, but purposeful for customer segments that are interested in this information and will most likely have a response.
The work is of practical importance for all companies that have a database of transactions (sales) with the specified customer ID, because the paper presents a step-by-step algorithm for data analysis and provides recommendations for further work with the obtained segments. The study also has universal theoretical and methodological significance: the method of combining RFM-analysis and analysis of consumer baskets, providing recommendations for the use of email-retargeting and targeted advertising on social networks can contribute to further in-depth study and improvement of algorithms, not only in marketing but, web-programming, medicine.

Keywords: customer segmentation; communication strategy; RFM-analysis; Apriori algorithm; consumer basket; associative rules; targeted advertising.

References

1. Mujianto, A.H. Chamdan, M.A. and Andriani, A.C. (2019), "Consumer customs analysis using the association rule and apriori algorithm for determining sales strategies in retail central", E3S Web of Conferences, vol. 125.
2. Aziz, A. (2017), "Customer segmentation based on behavioural data in e-marketplace", [Online], available at: https://uu.diva-portal.org/smash/get/diva2:1145508/FULLTEXT01.pdf (Accessed 14 January 2022).
3. Nair, M. (2015), "RFM analysis for successful customer segmentation", [Online], available at: https://www.putler.com/rfm-analysis (Accessed 14 January 2022).
4. R documantation (2021), "Arules: Mining Association Rules and Frequent Itemsets", [Online], available at: https://cran.r-project.org/web/packages/arules/index.html (Accessed 14 January 2022).
5. R documantation (2021), "ArulesViz: Visualizing Association Rules and Frequent Itemsets", [Online], available at: https://cran.r-project.org/web/packages/arulesViz/index.html (Accessed 14 January 2022).
6. R documantation (2021), "Data Visualization in R with ggplot2", [Online], available at: https://ssc.wisc.edu/sscc/pubs/dvr/index.html (Accessed 14 January 2022).
7. Birant, D. (2021), "Data mining using RFM analysis", [Online], available at: https://cdn.intechopen.com/pdfs/13162/InTech-Data_mining_using_rfm_analysis.pdf (Accessed 14 January 2022).
8. R documantation (2021), "DoBy: Groupwise Statistics, LSmeans, Linear Contrasts, Utilities", [Online], available at: URL: https://cran.r-project.org/web/packages/doBy/index.html (Accessed 14 January 2022).
9. R documantation (2021), "Dplyr: A Grammar of Data Manipulation", [Online], available at: https://cran.r-project.org/web/packages/dplyr/index.html (Accessed 14 January 2022).
10. Kurniawan, B.U. and Hammad, J. (2018), "Market basket analysis to identify customer behaviors by way of transaction data", Knowledge engineering and data science, vol. 1.
11. R documantation (2021), "Forcats: Tools for Working with Categorical Variables (Factors)", [Online], available at: https://cran.r-project.org/web/packages/forcats/index.html (Accessed 14 January 2022).
12. R documantation (2021), "R visualization workshop", [Online], available at: https://stulp.gmw.rug.nl/ggplotworkshop/index.html (Accessed 14 January 2022).
13. R documantation (2021), "Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics", [Online], available at: https://cran.r-project.org/web/packages/ggplot2/index.html (Accessed 14 January 2022).
14. R documantation (2021), "Ggpubr: "ggplot2" Based Publication Ready Plots", [Online], available at: https://cran.r-project.org/web/packages/ggpubr/index.html (Accessed 14 January 2022).
15. Gadirli, H. (2020), "Segmenting Bank Customers via RFM Model and Unsupervised Machine Learning", [Online], available at: https://arxiv.org/ftp/arxiv/papers/2008/2008.08662.pdf (Accessed 14 January 2022).
16. Roshan, H. and Afsharinezhad, M. (2017), "The new approach in market segmentation by using RFM model", Journal of applied research on industrial engineering, vol. 17.
17. Jabeen, H. (2021), "Market basket analysis using R", [Online], available at: https://www.datacamp.com/community/tutorials/market-basket-analysis-r (дата звернення: Accessed 14 January 2022).
18. Holly, B.S. Zetocha, D.A. and Passewitz, G.C. (1991), "Visual merchandising. A guide for small retailers", pp. 67—70.
19. Wei, J.T. Lin, S.Y. and Wu, H.H. (2010), "A review of the application of RFM model", African journal of business management, vol. 19.
20. Hahsler, M. (2015), "Visualizing association rules: introduction to the r-extension package arulesviz", pp. 24—26.
21. R documentation (2021), "Mining frequent items bought together using Apriori Algorithm", [Online], available at: https://www.analyticsvidhya.com/blog/2017/08/mining-frequent-items-using-apriori-algorithm.com (Accessed 14 January 2022).
22. Kadir, M. A. and Achyar, A. (2019), "Customer segmentation on online retail using RFM analysis", vol. 4, no. 1, pp. 1—11.
23. Tavakoli, M. Molavi, M. and Masoumi, V. (2019), "Customer segmentation and strategy developmentbased on user behavior analysis, RFM model anddata mining techniques: a case study", [Online], available at: https://www.researchgate.net/publication/330027350_Customer_Segmentation_and_Strategy_Development_Based_on_User_Behavior_Analysis_RFM_Model_and_Data_Mining_Techniques_A_Case_Study (Accessed 14 January 2022).
24. Chauhan, N.S. (2019), "Association Rule Mining", [Online], available at: https://www.kdnuggets.com/2019/12/market-basket-analysis.html (Accessed 14 January 2022).
25. Dogan, D. Ayciоn, A. and Atil, Z. (2018), "Customer segmentation by using rfm model and clustering methods: a case study in retail industry", International journal of contemporary economics and administrative sciences, vol. 8, no. 1, pp. 1—19.
26. R documantation (2021), "Package "formattable"", [Online], available at: https://cran.r-project.org/web/packages/formattable/formattable.pdf (Accessed 14 January 2022).
27. R documentation (2021), "Plot method to visualize association rules and itemsets", [Online], available at: https://rdrr.io/rforge/arulesViz/man/plot.html (Accessed 14 January 2022).
28. Rojlertjanya, P. (2019), "Customer segmentation based on the rfm analysis model using k-means clustering technique: a case of it solution and service provider in thailand", Bangkok University, pp. 103—105.
29. Moengage blog (2021), "Predictive segments using RFM analysis: an in-depth guide", [Online], available at: https://www.moengage.com/blog/rfm-analysis-using-predictive-segments/ (Accessed 14 January 2022).
30. Makhija, P. (2020), "RFM analysis for customer segmentation", [Online], available at:https://clevertap.com/blog/rfm-analysis/ (Accessed 14 January 2022).
31. R documantation (2021), "RColorBrewer: ColorBrewer Palettes", [Online], available at: https://cran.r-project.org/web/packages/RColorBrewer/index.html (Accessed 14 January 2022).
32. R documantation (2021), "Readxl: Read Excel Files", [Online], available at: https://cran.r-project.org/web/packages/readxl/index.html (Accessed 14 January 2022).
33. Data science articles (2019), "Recency, Frequency, Monetary Model with Python — and how Sephora uses it to optimize their Google and Facebook Ads", [Online], available at: https://towardsdatascience.com (Accessed 14 January 2022).
34. Blattberg, B. Kim, D. and Neslin, M. (2019), "RFM analysis", [Online], available at: https://link.springer.com/chapter/10.1007/978-0-387-72579-6_12 (Accessed 14 January 2022).
35. Wollen, R. (2019), "A modern approach to RFM segmentation", [Online], available at: https://cdn2.hubspot.net/hub/184373/file-41856256-pdf/docs/modern-approach-to-rfm-segmentation-ebook.pdf (Accessed 14 January 2022).
36. R documantation (2021), "Scales: Scale Functions for Visualization", [Online], available at: https://cran.r-project.org/web/packages/scales/index.html (Accessed 14 January 2022).
37. Prabhakaran, S. (2019), "The complete ggplot2 tutorial, how to customize ggplot2", [Online], available at: http://r-statistics.co/Complete-Ggplot2-Tutorial-Part2-Customizing-Theme-With-R-Code.html (Accessed 14 January 2022).
38. Ansari, S.Z. (2019), "Market basket analysis", Dissertation, pp. 55—57, [Online], available at: https://run.unl.pt/bitstream/10362/80955/1/TEGI0458.pdf (дата звернення: Accessed 14 January 2022).
39. Aggelis, V. and Christodoulakis, D. (2019), "Customer Clustering using RFM analysis", [Online], available at: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.128.7091&rep=rep1&type=pdf (Accessed 14 January 2022).
40. Ummadisetty, V. (2020), "Online retail data set", [Online], available at: https://www.kaggle.com/vijayuv/onlineretail.com (Accessed 14 January 2022).
41. Prokof'eva, D. (2019), "How to Improve Your Newsletter: Tips from Marketing Platforms and Experts", [Online], available at: https://www.cossa.ru/trends/276162 (Accessed 14 January 2022).
42. Brett, L. (2020), "Machine Learning in R: Expert Techniques for Predictive Analysis", Piter, pp. 460—465.
43. Email soldiers blog (2019), "Separate customers by loyalty using RFM analysis", [Online], available at: https://emailsoldiers.ru/blog/rfm-analysis (Accessed 14 January 2022).
44. Best Marketing Blog (2020), "Targeted advertising: what it is, what it is for, how to use it effectively", [Online], available at: https://bestmarketing.com.ua/ua/tarhetovana-reklama-shcho-tse-i-dlya-choho-vona-potribna (Accessed 14 January 2022).
45. Digital Academy (2021), "What is RFM analysis and how to apply it in the mailing list of an online store", [Online], available at: https://digital-academy.ru (Accessed 14 January 2022).

№ 1 2022, стор. 114 - 122

Дата публікації: 2022-02-02

Кількість переглядів: 737

Відомості про авторів

І. В. Мірошниченко

к. е. н., доцент кафедри математичного моделювання та статистики, Київський національний економічний університет імені Вадима Гетьмана

I. Miroshnychenko

PhD in Economics, Associate Professor of the Department of Mathematical Modeling and Statistics, Kyiv National Economic University named after Vadym Hetman

ORCID:

0000-0002-1307-7889


В. Д. Алексєєва

магістрант спеціальності "Економічна кібернетика та Дата Сайанс", Київський національний економічний університет імені Вадима Гетьмана

V. Aleksieieva

Master's student of "Economic cybernetics and Data science", Kyiv National Economic University named after Vadym Hetman

ORCID:

0000-0002-1001-6933

Як цитувати статтю

Мірошниченко І. В., Алексєєва В. Д. Використання rfm-аналізу в сегментації клієнтів. Економіка та держава. 2022. № 1. С. 114–122. DOI: 10.32702/2306-6806.2022.1.114

Miroshnychenko, I. and Aleksieieva, V. (2022), “Use of rfm analysis in customer segmentation”, Ekonomika ta derzhava, vol. 1, pp. 114–122. DOI: 10.32702/2306-6806.2022.1.114

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