nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

CRM and Marketing Analytics

verfasst von : Sultan Ceren Oner, Yusuf Isik, Abdullah Emin Kazdaloglu, Mirac Murat, Tolga Ahmet Kalayci, Kubra Cetin Yildiz, Aycan Pekpazar, Mahmut Sami Sivri, Nevcihan Toraman, Basar Oztaysi, Umut Asan, Cigdem Altin Gumussoy

Erschienen in: Business Analytics for Professionals

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Customer relationship management (CRM) and marketing analytics is a combination of techniques, technologies, and strategies that serve to create and deliver value to profitable customers. It involves internal business processes and functions (such as marketing, sales) as well as external influences (such as competitors). CRM systems collect, analyze and model information about customers using data science methods at all stages of their life cycle to establish and maintain long-term profitable relationships and create loyal customers.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Supply Chain Analytics

Nächstes Kapitel Financial Analytics

Supply Chain Management (2021) SCM—pricing & revenue. TutorialsPoint, https://www.tutorialspoint.com/supply_chain_management/supply_chain_management_pricing_and_revenue.htm. Access: 06 Sep 2021

Cross RG, Higbie JA, Cross ZN (2011) Milestones in the application of analytical pricing and revenue management. J Revenue Pricing Manag 10(1):8–18CrossRef

Rockton Software (2021) How to efficiently manage pricing and revenue in a supply chain. Rockton software, https://www.rocktonsoftware.com/how-to-efficiently-manage-pricing-and-revenue-in-a-supply-chain/. Access: 6 Sep 2021

Black J (2019) Revenue management: definition and dynamic pricing. Prisync, https://prisync.com/blog/revenue-management-dynamic-pricing/. Access: 6 Sep 2021

Chiang WC, Chen JC, Xu X (2007) An overview of research on revenue management: current issues and future research. Int J Revenue Manag 1(1):97–128CrossRef

Chen IJ, Popovich K (2003) Understanding customer relationship management (CRM): people, process and technology. Bus Process Manag J 9:672–688CrossRef

Buttle F (2008) Customer relationship management. Cust Relatsh Manag Second Ed 1–500. https://doi.org/10.4324/9780080949611

Aslan D, Asan U (2020) Churn prediction in the payment services industry: an application at token financial technologies for IoT devices. Içinde: industrial engineering in the internet-of-things world: selected papers from the virtual global joint conference on industrial engineering and its application areas, GJCIE 2020, August 14–15. 2020. p 317

Kumar V, Petersen JA (2012) Statistical methods in customer relationship management. Stat Methods Cust Relatsh Manag

10.

Ang L, Buttle F (2006) Managing for successful customer acquisition: an exploration. J Mark Manag 22:295–317CrossRef

11.

Gallo A (2014) The value of keeping the right customers, harvard business review, October 29

12.

Vafeiadis T, Diamantaras KI, Sarigiannidis G, Chatzisavvas KC (2015) A comparison of machine learning techniques for customer churn prediction. Simul Model Pract Theory Complete: 1–9. https://doi.org/10.1016/J.SIMPAT.2015.03.003

13.

Gordini N, Veglio V (2017) Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry. Ind Mark Manag 62:100–107CrossRef

14.

Yang LS, Chiu C (2006) Knowledge discovery on customer churn prediction. Içinde: procedings of the 10th WSEAS international conference on applied mathematics, Dallas, Texas, USA, November 1–3, 2006, pp 523

15.

Amin A, Anwar S, Adnan A, Nawaz M, Alawfi K, Hussain A, Huang K (2017) Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing 237:242–254. https://doi.org/10.1016/J.NEUCOM.2016.12.009CrossRef

16.

Bing-Zhang H, Yue W, Li-Ming Z, Dong-Lai Z, Ao-Ran X (2018) Customer churn prediction in Chinese traditional broadcasting industry: a positive analysis. Int Conf Manag Sci Eng—Annu Conf Proc 2017-August. 596–605. https://doi.org/10.1109/ICMSE.2017.8574436

17.

Manďák J, Hančlová J (2019) Use of logistic regression for understanding and prediction of customer churn in telecommunications

18.

Khamlichi FI, Zaim D, Khalifa K (2019) A new model based on global hybridization of machine learning techniques for “customer churn prediction”. 2019 3rd Int Conf Intell Comput Data Sci ICDS 2019. https://doi.org/10.1109/ICDS47004.2019.8942240

19.

Karvana KGM, Yazid S, Syalim A, Mursanto P (2019) Customer churn analysis and prediction using data mining models in banking industry. 2019 Int Work Big Data Inf Secur IWBIS 2019:33–38. https://doi.org/10.1109/IWBIS.2019.8935884CrossRef

20.

Ahmad AK, Jafar A, Aljoumaa K (2019) Customer churn prediction in telecom using machine learning in big data platform. J Big Data 61(6):1–24. https://doi.org/10.1186/S40537-019-0191-6CrossRef

21.

Amin A, Al-Obeidat F, Shah B, Adnan A, Loo J, Anwar S (2019) Customer churn prediction in telecommunication industry using data certainty. J Bus Res 94:290–301. https://doi.org/10.1016/J.JBUSRES.2018.03.003CrossRef

22.

Pamina J, Raja B, SathyaBama S, Sruthi MS, Vj A (2019) An effective classifier for predicting churn in telecommunication. Jour Adv Res Dyn Control Syst 11:221–229

23.

Amin A, Al-Obeidat F, Shah B, Al Tae M, Khan C, Durrani HUR, Anwar S (2017) Just-in-time customer churn prediction in the telecommunication sector. J Supercomput 766(76):3924–3948. https://doi.org/10.1007/S11227-017-2149-9CrossRef

24.

De Caigny A, Coussement K, De Bock KW, Lessmann S (2020) Incorporating textual information in customer churn prediction models based on a convolutional neural network. Int J Forecast 36:1563–1578. https://doi.org/10.1016/J.IJFORECAST.2019.03.029CrossRef

25.

What Is the Telecommunications Sector? https://www.investopedia.com/ask/answers/070815/what-telecommunications-sector.asp. Accessed 1 Aug 2021

26.

Reasons for customer churn in telecoms [Survey results]|TechSee. https://techsee.me/resources/surveys/2019-telecom-churn-survey/. Accessed 1 Aug 2021

27.

Telecom Churn Dataset|Kaggle. https://www.kaggle.com/mnassrib/telecom-churn-datasets. Accessed 1 Aug 2021

28.

Metrics and scoring: quantifying the quality of predictions—scikit-learn 0.24.2 documentation. https://scikit-learn.org/stable/modules/model_evaluation.html#accuracy-score. Accessed 17 Aug 2021

29.

Classification: precision and recall|machine learning crash course. https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall. Accessed 14 Aug 2021

30.

Internet World Stats (2021) History and growth of the internet from 1995 till today. Retrieved July 14, 2021, from https://www.internetworldstats.com/emarketing.htm

31.

Statista (15 July 2020) Number of social network users worldwide from 2017 to 2025 (in billions) [Graph]. In Statista. Retrieved July 14, 2021, from https://0-www-statista-com.seyhan.library.boun.edu.tr/statistics/278414/number-of-worldwide-social-network-users/

32.

McKinsey (2012) The social economy: unlocking value and productivity through social technologies. Retrieved July 14, 2021, from www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-social-economy

33.

Edosomwan S, Prakasan SK, Kouame D, Watson J, Seymour T (2011) The history of social media and its impact on business. J Appl Manag Entrep 16(3):79–91

34.

Paridon T, Carraher SM (2009) Entrepreneurial marketing: customer shopping value and patronage behavior. J Appl Manag Entrep 14(2):3–28

35.

Burby J, Brown A, Standards Committee WAA (2007) Web analytics definitions. Web Analytics Association, Washington DC

36.

Jansen BJ (2009) Understanding user-web interactions via web analytics. Synth Lectureson Inf Concepts, Retrieval, Serv 1(1):1–102

37.

Kotler P, Kelle KL (2006) A Framework for marketing management, 6th edn. Pearson Prentice Hall

38.

Lovelock C H, Wirtz J (2011) Services marketing-people, technology, and strategy. 7th edn. Pearson Prentice Hall

39.

Kim J, Lim C (2021) Customer complaints monitoring with customer review data analytics: an integrated method of sentiment and statistical process control analyses. Adv Eng Inf 49:101304CrossRef

40.

Kang D, Park Y (2014) Review-based measurement of customer satisfaction in mobile service: sentiment analysis and VIKOR approach. Expert Syst Appl 41(4):1041–1050CrossRef

41.

Hwangbo H, Kim YS, Cha KJ (2018) Recommendation system development for fashion retail e-commerce. Electron Commer Res Appl 28:94–101CrossRef

42.

Jivani AG (2011) A comparative study of stemming algorithms. Int J Comp Tech Appl 2(6):1930–1938

43.

Scikit Learn (2021) Sklearn feature extraction, Count vectorizer. https://scikitlearn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html

44.

Machine Learning (2021) Text classification, Step 3: prepare your data. https://developers.google.com/machine-learning/guides/text-classification/step-3

45.

Goutte C, Gaussier E (2005) A probabilistic interpretation of precision, recall and F-Score, with implication for evaluation. In: Losada DE, Fernández-Luna JM (eds) Advances in information retrieval. ECIR 2005. Lecture notes in computer science, vol 3408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31865-1_25

46.

Nick Brooks (2018) Women’s e-commerce clothing reviews. https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews

47.

Herlocker J, Konstan J (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53. Retrieved from http://portal.acm.org/citation.cfm?doid=963770.963772, http://dl.acm.org/citation.cfm?id=963772. https://doi.org/10.1145/963770.9637722

48.

Agrawal R, Srikant R (1994) Fast algorithms for mining association rules, proceedings of the 20th VLDB conference. Santiago, Chile

49.

Hegland M (2003) Algorithms for association rules, lecture notes in computer science (2600)

50.

Jooa JH, Bangb SW, Parka GD (2016) Implementation of a recommendation system using association rules and collaborative filtering. Procedia Comput Sci (91):944–952

51.

Viktoratos I, Tsadiras A, Bassiliades N (2018) Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems. Expert Syst Appl 101:78–90CrossRef

52.

Chee CH, Jaafar J, Aziz IA, Hasan MH, Yeoh W (2019) Algorithms for frequent itemset mining: a literature review. Artif Intell Rev 52:2603–2621CrossRef

53.

Yazgan P, Kusakci I (2016) A literature survey on association rule mining algorithms. Southeast Eur J Soft Comput 5(1). https://doi.org/10.21533/scjournal.v5i1.102

54.

Kaggle (2021) https://www.kaggle.com/c/movielens-100k. Accessed 18 Oct 2021

55.

Lin W, Alvarez S, Ruiz C (2001) Efficient adaptive support association rule mining for recommender systems. Kluwer Academic Publishers pp 1–20

56.

Smyth B, McCarthy K, Reilly J, O’Sullivan D, McGinty L, Wilson DC (27–30 June 2005) Case-studies in association rule mining for recommender systems, conference: proceedings of the 2005 international conference on artificial intelligence, ICAI 2005. Las Vegas, Nevada, USA

57.

Mican D, Tomai N (2010) Association-rules-based recommender system for personalization in adaptive web-based applications. In: Daniel F, Facca FM (eds) Current trends in web engineering. ICWE 2010. Lecture notes in computer science, vol 6385. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16985-4_8

58.

Kumara Swamy M, Krishna Reddy P (2015) Improving diversity performance of association rule based recommender systems. In: Chen Q, Hameurlain A, Toumani F, Wagner R, Decker H (eds) Database and expert systems applications. Globe 2015, DEXA 2015. Lecture notes in computer science, vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_34

59.

García MNM, Lucas JP, Batista VFL, Vicente MDM (2010) Semantic based web mining for recommender systems In: de Leon F, de Carvalho AP, Rodríguez-González S, De Paz Santana JF, Rodríguez JMC (eds) Distributed computing and artificial intelligence. Advances in intelligent and soft computing, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14883-5_3

60.

Paranjape-Voditel P, Deshpande U (2011) An association rule mining based stock market recommender system. Second Int Conf Emerg Appl Inf Technol 2011:21–24. https://doi.org/10.1109/EAIT.2011.90CrossRef

61.

Sandvig JJ, Mobasher B, Burke R (2007) Robustness of collaborative recommendation based on association rule mining. In: Proceedings of the 2007 ACM conference on recommender systems (RecSys ‘07): 105–112. Association for computing machinery, New York, NY, USA. https://doi.org/10.1145/1297231.1297249

62.

Sobhanam H, Mariappan AK (2013) Addressing cold start problem in recommender systems using association rules and clustering technique. Int Conf Comput Commun Inf 2013:1–5. https://doi.org/10.1109/ICCCI.2013.6466121CrossRef

63.

Ali Z, Khusro S, Ullah I (2016) A hybrid book recommender system based on table of contents (ToC) and association rule mining conference: the 10th international conference on informatics and systems (INFOS ‘16) At: Giza, Egypt Volume: ACM/ICPS

64.

Chen M, Lin C (2008) A data mining approach to product assortment and shelf space allocation. Expert Syst Appl 32(2007):976–998

Titel: CRM and Marketing Analytics
verfasst von: Sultan Ceren Oner
Yusuf Isik
Abdullah Emin Kazdaloglu
Mirac Murat
Tolga Ahmet Kalayci
Kubra Cetin Yildiz
Aycan Pekpazar
Mahmut Sami Sivri
Nevcihan Toraman
Basar Oztaysi
Umut Asan
Cigdem Altin Gumussoy
Verlag: Springer International Publishing
Buch: Business Analytics for Professionals
Print ISBN: 978-3-030-93822-2

Electronic ISBN: 978-3-030-93823-9

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-93823-9_12

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"