A methodology for improving complex sales success in CRM Systems

Rotovei, Doru; Negru, Viorel

doi:10.1109/inista.2017.8001179

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…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

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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.

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Section: Introductionmentioning

confidence: 83%

Opportunity activity sequence investigations in B2B CRM systems

Rotovei

2020

Acta Universitatis Sapientiae, Informatica

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Bayesian Classifiers in Intrusion Detection Systems

Mardini-Bovea

De-la-Hoz-Franco

Molina-Estren

et al. 2020

Machine Learning for Networking

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To be able to identify computer attacks, detection systems that are based on faults are not dependent on data base upgrades unlike the ones based on misuse. The first type of systems mentioned generate a knowledge pattern from which the usual and unusual traffic is distinguished. Within computer networks, different classification traffic techniques have been implemented in intruder detection systems based on abnormalities. These try to improve the measurement that assess the performance quality of classifiers and reduce computational cost. In this research work, a comparative analysis of the obtained results is carried out after implementing different selection techniques such as Info.Gain, Gain ratio and Relief as well as Bayesian (Naïve Bayes and Bayesians Networks). Hence, 97.6% of right answers were got with 13 features. Likewise, through the implementation of both load balanced methods and attributes normalization and choice, it was also possible to diminish the number of features used in the ID classification process. Also, a reduced computational expense was achieved.

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Improving Lost/Won Classification in CRM Systems Using Sentiment Analysis

Rotovei

Negru

2017

2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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A methodology for improving complex sales success in CRM Systems

Cited by 5 publications

References 25 publications

Opportunity activity sequence investigations in B2B CRM systems

Opportunity activity sequence investigations in B2B CRM systems

Bayesian Classifiers in Intrusion Detection Systems

Improving Lost/Won Classification in CRM Systems Using Sentiment Analysis

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