Improving Lost/Won Classification in CRM Systems Using Sentiment Analysis

Rotovei, Doru; Negru, Viorel

doi:10.1109/synasc.2017.00038

Cited by 4 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our hypothesis is that won deals and lost deals can be clustered together, in other words, there is a distinctive pattern of activities performed for won deals and for lost deals. This hypothesis, if true, could help us improve the B2B sales prediction models that we previously studied in [17,16].…”

Section: Introductionmentioning

confidence: 83%

Opportunity activity sequence investigations in B2B CRM systems

Rotovei

2020

Acta Universitatis Sapientiae, Informatica

Self Cite

View full text Add to dashboard Cite

Closing a deal in a business to business environment implies a series of orchestrated actions that the sales representatives are taking to take a prospective buyer from first contact to a closed sale. The actions, such as meetings, emails, phone calls happen in succession and in different points in time relative to the first interaction.Time-series are ordered sequences of discrete-time data. In this work, we are examining the relationship between the actions as time series and the final win outcome for each deal. To assess whether the behavior of the salespeople have a direct influence on the final outcome of the current deal, we used histogram analysis, dynamic time warping and string edit distance on a real-world Customer Relationship Management System data set. The results are discussed and included in this paper.

show abstract

Section: Introductionmentioning

confidence: 83%

Opportunity activity sequence investigations in B2B CRM systems

Rotovei

2020

Acta Universitatis Sapientiae, Informatica

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [13] the discussion is based on the sentiment analysis for the customer relation management where the idea of have a customer loyal for a longer run after their first purchase is very important for a company and their products. The proposed concept in [14] is to use multiaspect level sentiment analysis (MALSA) model in a CRM which not only helps find the sentiment but also has a recommendation approach to recommend the customers throughout the purchase cycle. The model was built on four discussions namely, Frequency based detection, Syntax based detection, supervised and unsupervised learning and finally hybrid models for analyzing the sentiments.…”

Section: Introductionmentioning

confidence: 99%

“…Sentiment analysis is not only limited to finding the sentiment straight forward from a textual sentence but can also be used to find the lost-won classification of complex deals [14]. The model is built upon using "Frequency, lexicon based and syntax-based detection", "machine learning based approach" and "hybrid methods".…”

Section: Introductionmentioning

confidence: 99%

Sentimental analysis of audio based customer reviews without textual conversion

Maradithaya,

Katti

2024

IJECE

View full text Add to dashboard Cite

The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.

show abstract

Pipeline for Pre-processing of Audio Data

Katti

Sumana

2022

IOT With Smart Systems

View full text Add to dashboard Cite

Improving Lost/Won Classification in CRM Systems Using Sentiment Analysis

Cited by 4 publications

References 22 publications

Opportunity activity sequence investigations in B2B CRM systems

Opportunity activity sequence investigations in B2B CRM systems

Sentimental analysis of audio based customer reviews without textual conversion

Pipeline for Pre-processing of Audio Data

Contact Info

Product

Resources

About