Current Applications of Machine Learning Techniques in CRM: A Literature Review and Practical Implications

Chagas, Beatriz Nery Rodrigues; Viana, Julio; Reinhold, Olaf; Lobato, Fábio Manoel França; Jacob, Antônio F. L.; Alt, Rainer

doi:10.1109/wi.2018.00-53

Cited by 24 publications

(7 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Pada ranah bisnis, analisis sentimen memainkan peran penting untuk mendapatkan insight dari feedback pelanggan untuk meningkatkan strategi dan produk mereka. Saat ini budaya bisnis menggunakan prinsip 'customer-based' ini artinya pelanggan dijadikan sebuah mercusuar untuk role-model bisnis mereka, oleh karena itu memahami pelanggan adalah hal yang terpenting [2].…”

Section: Pendahuluanunclassified

Bidirectional GRU dengan Attention Mechanism pada Analisis Sentimen PLN Mobile

Rohman

Suhartono

Chamidy

2023

View full text Add to dashboard Cite

PLN Mobile adalah aplikasi ponsel customer self-service yang terintegrasi dengan Aplikasi Pengaduan dan Keluhan Pelanggan (APKT) dan Aplikasi Pelayanan Pelanggan Terpusat (AP2T). Mulai awal tahun 2021 sampai sekarang PLN menggencarkan sosialisasi PLN Mobile pada masyarakat sehingga jumlah ulasan PLN Mobile pada google playstore meningkat drastis. Untuk mengetahui kepuasan pelanggan tidak bisa hanya dengan melihat dan menganalisis dari kolom ulasan PLN Mobile di google playstore, hal ini dikarenakan data ulasan berbentuk tidak terstruktur. Untuk mengatasi masalah ini dibutuhkan teknik khusus yaitu analisis sentimen. Penelitian ini bertujuan untuk mengusulkan arsitektur analisis sentimen untuk mengatasi ketidakmampuan algoritma deep learning seperti LSTM dan GRU dalam menangkap informasi penting. Arsitektur yang diusulkan yaitu mengkombinasikan Bidirectional GRU (BiGRU) dengan attention mechanism menggunakan word2vec sebagai word embedding. Attention mechanism digunakan untuk menangkap kata yang penting sehingga arsitektur tersebut dapat memahami informasi yang penting. Kemudian, arsitektur yang diusulkan dilakukan perbandingan dengan metode CNN, CNN-GRU, CNN-LSTM, CNN-BiGRU, CNN-BiLSTM dengan menggunakan data ulasan PLN Mobile. Hasil eksperimen menunjukkan bahwa arsitektur analisis sentimen yang diusulkan memiliki akurasi dan f1-score yang lebih tinggi.

show abstract

Section: Pendahuluanunclassified

Bidirectional GRU dengan Attention Mechanism pada Analisis Sentimen PLN Mobile

Rohman

Suhartono

Chamidy

2023

View full text Add to dashboard Cite

show abstract

“…Os sistemas de CRM têm evoluindo notoriamente, tanto na aplicação de métodos de análise mais rebuscados como a aplicação de inteligência computacional [Rodrigues Chagas et al 2018], quanto na utilização de diferentes fontes de dados para enriquecimento das análises, como no caso do Social CRM. Por exemplo, [Duan et al 2008, Liu et al 2017] que demonstraram os efeitos doeWoM em uma compra e também mudanças de atitude frenteà uma marca.É importante destacar que eWoM não ocorre exclusivamente em plataformas de propósito específico.…”

Section: Trabalhos Correlatosunclassified

Melhorando Sistemas de Social CRM por meio de Eletronic Word-of-Mouth

Netto

Almeida

Lobato

et al. 2019

REIC

View full text Add to dashboard Cite

A Web 2.0 modificou radicalmente a forma pela qual os consumidores tomam decisões de compra, necessitando de uma nova abordagem de gerenciamento de dados de redes sociais, conhecida por Social Customer Relationship Management. Além disso, nesses novos ambientes surgiu o boca-a-boca eletrônico, sendo a capacidade de disponibilizar opiniões sobre produtos e serviços por meio de redes sociais com qualquer outro usuário. Consequentemente, dentro dessas redes residem dados gerados por usuários em forma de comentários e reclamações que, sob análise, podem trazer em vantagens competitivas para empresas. Os resultados deste trabalho confirmam que a ferramenta é uma inovação tecnológica, que permiti uma melhor interação empresa-consumidor, gerando maior confiabilidade nos processos decisórios de vendas e de atendimento aos clientes.

show abstract

“…SA is critical in the business sphere since it allows companies to develop strategies and acquire knowledge of client's opinion for their products. Acknowledging the client is becoming extremely important in today's modern consumerbased company culture for increasing their business [5].…”

Section: Introductionmentioning

confidence: 99%

Aspect-Based Sentiment Analysis using Machine Learning and Deep Learning Approaches

Kumar¹,

Gupta

et al. 2023

IJRITCC

View full text Add to dashboard Cite

Sentiment analysis (SA) is also known as opinion mining, it is the process of gathering and analyzing people's opinions about a particular service, good, or company on websites like Twitter, Facebook, Instagram, LinkedIn, and blogs, among other places. This article covers a thorough analysis of SA and its levels. This manuscript's main focus is on aspect-based SA, which helps manufacturing organizations make better decisions by examining consumers' viewpoints and opinions of their products. The many approaches and methods used in aspect-based sentiment analysis are covered in this review study (ABSA). The features associated with the aspects were manually drawn out in traditional methods, which made it a time-consuming and error-prone operation. Nevertheless, these restrictions may be overcome as artificial intelligence develops. Therefore, to increase the effectiveness of ABSA, researchers are increasingly using AI-based machine learning (ML) and deep learning (DL) techniques. Additionally, certain recently released ABSA approaches based on ML and DL are examined, contrasted, and based on this research, gaps in both methodologies are discovered. At the conclusion of this study, the difficulties that current ABSA models encounter are also emphasized, along with suggestions that can be made to improve the efficacy and precision of ABSA systems.

show abstract

Current Applications of Machine Learning Techniques in CRM: A Literature Review and Practical Implications

Cited by 24 publications

References 76 publications

Bidirectional GRU dengan Attention Mechanism pada Analisis Sentimen PLN Mobile

Bidirectional GRU dengan Attention Mechanism pada Analisis Sentimen PLN Mobile

Melhorando Sistemas de Social CRM por meio de Eletronic Word-of-Mouth

Aspect-Based Sentiment Analysis using Machine Learning and Deep Learning Approaches

Contact Info

Product

Resources

About