Skip to main content
Top

2024 | OriginalPaper | Chapter

Harnessing Machine Learning to Optimize Customer Relations: A Data-Driven Approach

Authors : Santosh Kumar, Priti Verma, Dhaarna Singh Rathore, Richa Pandey, Gunjan Chhabra

Published in: Micro-Electronics and Telecommunication Engineering

Publisher: Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In today’s competitive business landscape, optimizing customer relations is paramount for sustained success. Harnessing the power of machine learning, this research presents a data-driven approach to achieve this objective. By leveraging three prominent algorithms, namely Linear Regression (LR), decision tree (DT), and support vector machine (SVM), customer behavior patterns are identified and analyzed. Through the systematic examination of vast datasets, this study attains an impressive accuracy of 95%. The findings showcase the potential of machine learning in enhancing customer relations, enabling businesses to make more informed decisions, tailor personalized experiences, and foster long-lasting customer loyalty. This data-driven approach promises to revolutionize CRM strategies, propelling enterprises toward unparalleled growth and success.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

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!

Literature
1.
go back to reference Chatterjee S, Ghosh SK, Chaudhuri R, Nguyen B (2019) Are CRM systems ready for AI integration? A conceptual framework of organizational readiness for effective AI-CRM integration. Bottom Line 32(2):144–157CrossRef Chatterjee S, Ghosh SK, Chaudhuri R, Nguyen B (2019) Are CRM systems ready for AI integration? A conceptual framework of organizational readiness for effective AI-CRM integration. Bottom Line 32(2):144–157CrossRef
2.
go back to reference Chatterjee S, Rana NP, Tamilmani K, Sharma A (2021) The effect of AI-based CRM on organization performance and competitive advantage: an empirical analysis in the B2B context. Indus Market Manage 1(97):205–219CrossRef Chatterjee S, Rana NP, Tamilmani K, Sharma A (2021) The effect of AI-based CRM on organization performance and competitive advantage: an empirical analysis in the B2B context. Indus Market Manage 1(97):205–219CrossRef
3.
go back to reference Chatterjee S, Chaudhuri R, Vrontis D (2022) AI and digitalization in relationship management: impact of adopting AI-embedded CRM system. J Bus Res 1(150):437–450CrossRef Chatterjee S, Chaudhuri R, Vrontis D (2022) AI and digitalization in relationship management: impact of adopting AI-embedded CRM system. J Bus Res 1(150):437–450CrossRef
4.
go back to reference Smith AD (2009) The impact of e-procurement systems on customer relationship management: a multiple case study. Int J Procurement Manage 2(3):314–338CrossRef Smith AD (2009) The impact of e-procurement systems on customer relationship management: a multiple case study. Int J Procurement Manage 2(3):314–338CrossRef
5.
go back to reference Ames CP, Smith JS, Pellisé F, Kelly M, Alanay A, Acaroglu E, Pérez-Grueso FJ, Kleinstück F, Obeid I, Vila-Casademunt A, Shaffrey Jr CI (2019) Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value. Spine 44(13):915–926 Ames CP, Smith JS, Pellisé F, Kelly M, Alanay A, Acaroglu E, Pérez-Grueso FJ, Kleinstück F, Obeid I, Vila-Casademunt A, Shaffrey Jr CI (2019) Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value. Spine 44(13):915–926
6.
go back to reference Subramani MK, Muruganantharaj MG. EnhancedTree+: a novel approach for improving decision tree classifiers Subramani MK, Muruganantharaj MG. EnhancedTree+: a novel approach for improving decision tree classifiers
7.
go back to reference Srifi M, Oussous A, Ait Lahcen A, Mouline S (2020) Recommender systems based on collaborative filtering using review texts-a survey. Information 11(6):317CrossRef Srifi M, Oussous A, Ait Lahcen A, Mouline S (2020) Recommender systems based on collaborative filtering using review texts-a survey. Information 11(6):317CrossRef
8.
go back to reference Kim M, Yun J, Cho Y, Shin K, Jang R, Bae HJ, Kim N (2019) Deep learning in medical imaging. Neurospine 16(4):657 Kim M, Yun J, Cho Y, Shin K, Jang R, Bae HJ, Kim N (2019) Deep learning in medical imaging. Neurospine 16(4):657
9.
go back to reference Zhang Z, Mo L, Huang C, Xu P (2019) Binary logistic regression modeling with TensorFlow\(^{\text{TM}}\). Ann Trans Med 7(20) Zhang Z, Mo L, Huang C, Xu P (2019) Binary logistic regression modeling with TensorFlow\(^{\text{TM}}\). Ann Trans Med 7(20)
10.
go back to reference Li Q, Wen Z, He B (2020) Practical federated gradient boosting decision trees. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, No 04, pp 4642–4649 Li Q, Wen Z, He B (2020) Practical federated gradient boosting decision trees. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, No 04, pp 4642–4649
11.
go back to reference Chandrasekaran G, Nguyen TN, Hemanth DJ (2021) Multimodal sentimental analysis for social media applications: a comprehensive review. Wiley Interdiscipl Rev Data Min Knowl Discov 11(5):e1415CrossRef Chandrasekaran G, Nguyen TN, Hemanth DJ (2021) Multimodal sentimental analysis for social media applications: a comprehensive review. Wiley Interdiscipl Rev Data Min Knowl Discov 11(5):e1415CrossRef
12.
go back to reference Hannigan TR, Haans RF, Vakili K, Tchalian H, Glaser VL, Wang MS, Kaplan S, Jennings PD (2019) Topic modeling in management research: rendering new theory from textual data. Acad Manage Ann 13(2):586–632CrossRef Hannigan TR, Haans RF, Vakili K, Tchalian H, Glaser VL, Wang MS, Kaplan S, Jennings PD (2019) Topic modeling in management research: rendering new theory from textual data. Acad Manage Ann 13(2):586–632CrossRef
13.
go back to reference Mane DT, Sangve S, Upadhye G, Kandhare S, Mohole S, Sonar S, Tupare S (2022) Detection of anomaly using machine learning: a comprehensive survey. Int J Emerg Technol Adv Eng 12(11):134–152CrossRef Mane DT, Sangve S, Upadhye G, Kandhare S, Mohole S, Sonar S, Tupare S (2022) Detection of anomaly using machine learning: a comprehensive survey. Int J Emerg Technol Adv Eng 12(11):134–152CrossRef
14.
go back to reference Guerroum M, Zegrari M, Masmoudi M, Berquedich M, Elmahjoub AA (2022) Machine learning technics for remaining useful life prediction using diagnosis data: a case study of a Jaw Crusher. Int J Emerg Technol Adv Eng 12(10):122–135CrossRef Guerroum M, Zegrari M, Masmoudi M, Berquedich M, Elmahjoub AA (2022) Machine learning technics for remaining useful life prediction using diagnosis data: a case study of a Jaw Crusher. Int J Emerg Technol Adv Eng 12(10):122–135CrossRef
15.
go back to reference Clarin JA (2022) Comparison of the performance of several regression algorithms in predicting the quality of white wine in WEKA. Int J Emerg Technol Adv Eng 12(7):20–26CrossRef Clarin JA (2022) Comparison of the performance of several regression algorithms in predicting the quality of white wine in WEKA. Int J Emerg Technol Adv Eng 12(7):20–26CrossRef
16.
go back to reference Baharun N, Razi NFM, Masrom S, Yusri NAM, Rahman ASA (2022) Auto modelling for machine learning: a comparison implementation between rapid miner and python. Int J Emerg Technol Adv Eng 12(5):15–27CrossRef Baharun N, Razi NFM, Masrom S, Yusri NAM, Rahman ASA (2022) Auto modelling for machine learning: a comparison implementation between rapid miner and python. Int J Emerg Technol Adv Eng 12(5):15–27CrossRef
17.
go back to reference Malvin DC, Rangkuti AH (2022) WhatsApp Chatbot customer service using natural language processing and support vector machine. Int J Emerg Technol Adv Eng 12(3):130–136CrossRef Malvin DC, Rangkuti AH (2022) WhatsApp Chatbot customer service using natural language processing and support vector machine. Int J Emerg Technol Adv Eng 12(3):130–136CrossRef
18.
go back to reference Masrom S, Baharun N, Razi NFM, Rahman RA, Abd Rahman AS (2022) Particle swarm optimization in machine learning prediction of Airbnb hospitality price prediction. Int J Emerg Technol Adv Eng 12(1):146–151CrossRef Masrom S, Baharun N, Razi NFM, Rahman RA, Abd Rahman AS (2022) Particle swarm optimization in machine learning prediction of Airbnb hospitality price prediction. Int J Emerg Technol Adv Eng 12(1):146–151CrossRef
19.
go back to reference Lam NT (2021) Developing a framework for detecting phishing URLs using machine learning. Int J Emerg Technol Adv Eng 11(11):61–67CrossRef Lam NT (2021) Developing a framework for detecting phishing URLs using machine learning. Int J Emerg Technol Adv Eng 11(11):61–67CrossRef
20.
go back to reference Michael C, Utama DN (2021) Social media based decision support model to solve Indonesian waste management problem: an improved version. Int J Emerg Technol Adv Eng 11(10):1–12CrossRef Michael C, Utama DN (2021) Social media based decision support model to solve Indonesian waste management problem: an improved version. Int J Emerg Technol Adv Eng 11(10):1–12CrossRef
21.
go back to reference Rahman RA, Masrom S, Zakaria NB, Halid S (2021) Auditor choice prediction model using corporate governance and ownership attributes: Machine learning approach. Int J Emerg Technol Adv Eng 11(7):87–94CrossRef Rahman RA, Masrom S, Zakaria NB, Halid S (2021) Auditor choice prediction model using corporate governance and ownership attributes: Machine learning approach. Int J Emerg Technol Adv Eng 11(7):87–94CrossRef
22.
go back to reference Rahman ASA, Masrom S, Rahman RA, Ibrahim R (2021) Rapid software framework for the implementation of machine learning classification models. Int J Emerg Technol Adv Eng 11(8):8–18CrossRef Rahman ASA, Masrom S, Rahman RA, Ibrahim R (2021) Rapid software framework for the implementation of machine learning classification models. Int J Emerg Technol Adv Eng 11(8):8–18CrossRef
23.
go back to reference Rahman RA, Masrom S, Zakaria NB, Nurdin E, Abd Rahman AS (2021) Prediction of earnings manipulation on Malaysian listed firms: a comparison between linear and tree-based machine learning. Int J Emerg Technol Adv Eng 11(8):111–120CrossRef Rahman RA, Masrom S, Zakaria NB, Nurdin E, Abd Rahman AS (2021) Prediction of earnings manipulation on Malaysian listed firms: a comparison between linear and tree-based machine learning. Int J Emerg Technol Adv Eng 11(8):111–120CrossRef
24.
go back to reference Al-Thani MG, Yang D (2021) Machine learning for the prediction of returned checks closing status. Int J Emerg Technol Adv Eng 11(6):19–26CrossRef Al-Thani MG, Yang D (2021) Machine learning for the prediction of returned checks closing status. Int J Emerg Technol Adv Eng 11(6):19–26CrossRef
25.
go back to reference Vijayalakshmi K (2020) Comparitive approach of data mining for diabetes prediction and classification. Int J Emerg Technol Adv Eng 10(2):19–26MathSciNet Vijayalakshmi K (2020) Comparitive approach of data mining for diabetes prediction and classification. Int J Emerg Technol Adv Eng 10(2):19–26MathSciNet
26.
go back to reference Muqodas AU, Kusuma GP (2021) Promotion scenario based sales prediction on E-retail groceries using data mining. Int J Emerg Technol Adv Eng 11(6):9–18CrossRef Muqodas AU, Kusuma GP (2021) Promotion scenario based sales prediction on E-retail groceries using data mining. Int J Emerg Technol Adv Eng 11(6):9–18CrossRef
27.
go back to reference Saritha B, Mohan Reddy AR (2020) Mining association rules from distributed databases with privacy preserving by using the randomization and cryptographic techniques. Int J Emerg Technol Adv Eng 10(11):70–73 Saritha B, Mohan Reddy AR (2020) Mining association rules from distributed databases with privacy preserving by using the randomization and cryptographic techniques. Int J Emerg Technol Adv Eng 10(11):70–73
28.
go back to reference Dubey R, Agrawal D (2015) Bearing fault classification using ANN-based Hilbert footprint analysis. IET Sci Measure Technol 9(8):1016–1022CrossRef Dubey R, Agrawal D (2015) Bearing fault classification using ANN-based Hilbert footprint analysis. IET Sci Measure Technol 9(8):1016–1022CrossRef
29.
go back to reference Rajpoot V, Dubey R, Mannepalli PK, Kalyani P, Maheshwari S, Dixit A, Saxena A (2022) Mango plant disease detection system using hybrid BBHE and CNN approach. Traitement du Signal 39(3) Rajpoot V, Dubey R, Mannepalli PK, Kalyani P, Maheshwari S, Dixit A, Saxena A (2022) Mango plant disease detection system using hybrid BBHE and CNN approach. Traitement du Signal 39(3)
30.
go back to reference Dubey R, Sharma RR, Upadhyay A, Pachori RB (2023) Automated variational non-linear chirp mode decomposition for bearing fault diagnosis. IEEE Trans Indus Inform Dubey R, Sharma RR, Upadhyay A, Pachori RB (2023) Automated variational non-linear chirp mode decomposition for bearing fault diagnosis. IEEE Trans Indus Inform
31.
go back to reference Uduweriya RMBPM, Napagoda NA. Clustering online retail data set. In: Research symposium, p 106 Uduweriya RMBPM, Napagoda NA. Clustering online retail data set. In: Research symposium, p 106
32.
go back to reference Joshi K, Kumar M, Memoria M, Bhardwaj P, Chhabra G, Baloni D (2022) Big data F5 load balancer with Chatbots framework. In: Rising threats in expert applications and solutions, pp 709–717 Joshi K, Kumar M, Memoria M, Bhardwaj P, Chhabra G, Baloni D (2022) Big data F5 load balancer with Chatbots framework. In: Rising threats in expert applications and solutions, pp 709–717
33.
go back to reference Hasan M, Venkatanarayan A, Mohan I, Singh N, Chhabra G (2020) Comparison of various DOS algorithm. Int J Inform Secu Priv 14(1):27–43CrossRef Hasan M, Venkatanarayan A, Mohan I, Singh N, Chhabra G (2020) Comparison of various DOS algorithm. Int J Inform Secu Priv 14(1):27–43CrossRef
34.
go back to reference Thakral M, Singh RR, Jain A, Chhabra G (2021) Rigid wrap ATM debit card fraud detection using multistage detection. In: 2021 6th international conference on signal processing, computing and control (ISPCC) Thakral M, Singh RR, Jain A, Chhabra G (2021) Rigid wrap ATM debit card fraud detection using multistage detection. In: 2021 6th international conference on signal processing, computing and control (ISPCC)
Metadata
Title
Harnessing Machine Learning to Optimize Customer Relations: A Data-Driven Approach
Authors
Santosh Kumar
Priti Verma
Dhaarna Singh Rathore
Richa Pandey
Gunjan Chhabra
Copyright Year
2024
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-99-9562-2_36