Extracting Customer Traces from CRMS: From Software to Process Models

Osman, Cristina-Claudia; Ghiran, Ana-Maria

doi:10.1016/j.promfg.2019.02.261

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Customer journey mapping (CJM) is a key marketing process for online companies. The formerly difficult task of monitoring every customer step and collecting as much information as possible is very frequently solved with the help of customer relationship management software (CRMS) or process mining algorithms [18]. CRMS is considered a crucial instrument to achieve organizational agility, which Dyer et al [19] defines as a company's ability to collect and interpret information about its target market, respond to market fluctuations and needs, and develop a system of organizational learning.…”

Section: Mapping the Online Customer Journeymentioning

confidence: 99%

Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting

Semerádová

Weinlich

2019

IEEE Access

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The advertising systems and the algorithms they use are constantly evolving and expanding the possibilities for reaching potential customers. Hyper-targeting (also called microtargeting) is the use of detailed customer data and marketing automation to deliver highly targeted and personalized messages across a large number of channels. These campaigns are designed to appeal to specific people or small groups of customers. By using the ability to process large amounts of data through innovations, such as predictive analytics, marketers can gain a deeper understanding of their audiences, focusing on specific accounts and not on the entire segments. This reportedly allows B2B brands to target customers directly and provide unique personal and highly relevant experiences. However, the scientific evidence to support this claim is missing. Some previous studies even suggest a negative impact of highly personalized advertising content on user reactiveness and purchase behavior. In this article, we test the effects of different levels of personalized advertisements using the advanced campaign targeting tool called Facebook Lookalike Audiences. Facebook Lookalike Audiences works on the basis of the estimation of customer similarity based on the characteristics of a custom audience, as defined by the advertiser. We examine the performance of various targeting settings using data from 840 Facebook ads with different personalization levels. These advertisements are compared in terms of reach, number of reactions, frequency of impressions, number of clicks, average time spent on a website, number of viewed pages, number of conversions, and profitability. We believe that the findings presented in this article help clarify the factors that influence user reactiveness toward personalized online advertising using evidence from actual Facebook ad sets.

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Section: Mapping the Online Customer Journeymentioning

confidence: 99%

Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting

Semerádová

Weinlich

2019

IEEE Access

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“…Analyzing the dataset is curial one. Various machine learning algorithms and data mining algorithms helps to find out insights in the data [4].Machine Learning (ML) algorithms are efficient one in analyzing the datasets. Based upon the dataset characteristics, the ML algorithm can be split into three category supervised learning, reinforcement learning and unsupervised learning.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Naive Bayes Classifier with and without Filter Based Feature Selection

Prabha¹,

Subramanian²,

Balakrishnan³

et al. 2019

IJITEE

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Customer Relationship Ma agement tends to analyze datasets to find insights about data which in turn helps to frame the business strategy for improvement of enterprises. Analyzing data in CRM requires high intensive models. Machine Learning (ML) algorithms help in analyzing such large dimensional datasets. In most real time datasets, the strong independence assumption of Naive Bayes (NB) between the attributes are violated and due to other various drawbacks in datasets like irrelevant data, partially irrelevant data and redundant data, it leads to poor performance of prediction. Feature selection is a preprocessing method applied, to enhance the predication of the NB model. Further, empirical experiments are conducted based on NB with Feature selection and NB without feature selection. In this paper, a empirical study of attribute selection is experimented for five dissimilar filter based feature selection such as Relief-F, Pearson correlation (PCC), Symmetrical Uncertainty (SU), Gain Ratio (GR) and Information Gain (IG).

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