Evaluating discounts as a dimension of customer behavior analysis

haghighatnia, shohre; Abdolvand, Neda; Harandi, Saeedeh Rajaee

doi:10.1080/13527266.2017.1410210

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…decision tree and SVM-Lib support vector machine in order to analyze the portfolio and customer classification. Each of these algorithms has the properties shown in the tables (6)(7)(8). Table 4 shows the details of the deep neural networks algorithm for basket analysis and customer classification.…”

Section: Evaluation Resultsmentioning

confidence: 99%

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An Analytics Model for TelecoVAS Customers’ Basket Clustering using Ensemble Learning Approach

Etebarian

Azmi

Dastjerdi

2020

Preprint

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Value Added Services at Mobile Communications Company provide customers with a variety of services. Value added services generate significant revenue annually for telecommunications companies. Providing solutions that can provide customers of a communications company with relevant and engaging services has become a major challenge in this field. Numerous methods have been proposed so far to analyze customers' carts and provide related services. Despite the many applications that these methods have, they still face difficulties in improving the accuracy of bids. This paper combines the X-Means algorithm, the ensemble learning system, and the N-List structure to analyze the customer portfolio of a mobile communications company and provide value-added services. The X-Means algorithm is used to determine the optimal number of clusters and clustering of customers in a mobile communications company. The ensemble learning algorithm is also used to assign categories to new Elder customers, and finally to the N-List structure for customer basket analysis. By simulating the proposed method and comparing it with other methods including KNN, SVM, and deep neural networks, it has improved the accuracy of about 7%.

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Section: Evaluation Resultsmentioning

confidence: 99%

“…Using this information, it can be used to display the right product to attract customers. One of the most important uses of these transactions is data analysis and transaction and customer basket [7].…”

Section: Introductionmentioning

confidence: 99%

An Analytics Model for TelecoVAS Customers’ Basket Clustering using Ensemble Learning Approach

Etebarian

Azmi

Dastjerdi

2020

Preprint

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“…The concept has limited use in earlier research in the user behavior domains. Concerning user product expectations, entropy was used to isolate the importance of discounts (Haghighatnia, Abdolvand, & Rajaee Harandi, 2018). Yang, Shan, Jiang, Yang, and Yao (2018) used entropy to align new products and consumer needs, with an aim, similar to ours, of managing complexity.…”

Section: Prior Literaturementioning

confidence: 99%

Making Meaningful User Segments from Datasets Using Product Dissemination and Product Impact

Jansen¹,

Salminen²,

Jung³

2020

Data and Information Management

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Online companies face large user populations, making segmentation a daunting exercise. Demonstrating an approach that facilitates user segmentation, this research leverages product dissemination and product impact metrics with normalized Shannon entropy. Using 4,653 products from an international news and media organization with 134,364,449 user-product engagements, we isolate the key products with the widest product dissemination and the least product impact using entropy-based measures, effectively capturing the engagement levels. We demonstrate that a small percentage (0.33% in our dataset) of products are so widely disseminated that they are non-discriminatory, and a large percentage of products (17.02%) are discriminatory but have so little dissemination that their impact is negligible. Our approach reduces the product dataset by 17.35% and the number of user segments by 8.18%. Implications are that organizations can isolate impactful products useful for user segmentation to enhance the user focus.

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“…Thanks to the new communication technology of Web 2.0, consumption behaviors have generated a huge amount of data online [3,4]. Digging deep into the big data on consumption behaviors, enterprises can reasonably predict consumption intentions, pinpoint potential consumers timely, and organize pertinent marketing activities [5][6][7][8]. To improve marketing efficiency and conversion rate, enterprises need to effectively explore the consumption demand, and build a complete hierarchical strategy for consumer management [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…To evaluate consumer credit, Monalisa et al [23] established a backpropagation neural network (BPNN), and demonstrated the high accuracy of the network in handling massive data and predicting potential purchase intentions of consumers. Haghighatnia et al [5] applied the optimized fuzzy neural network (FNN) to evaluate e-commerce consumers and predict their purchase intentions, and proved that the optimized FNN is a scientific and accurate network with good nonlinearity, self-learning ability, self-organization.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation Strategy for Commercial Precision Marketing Based on Artificial Neural Network

Cheng¹

2020

RIA

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With the proliferation of the Internet and smart mobile terminals, great progress has been made in the precision placement and benefit-sharing mechanism of commercial advertisements. Meanwhile, media marketing has become increasingly in-depth and precise. So far, mature theories have been proposed on consumer value and precision marketing. But further research is needed to mine the value from the big data on commercial precision marketing. To improve the accuracy of commercial precision marketing, this paper presents an evaluation index system (EIS) for commercial precision marketing based on improved attention-interest-desire-memory-action (ADIMA) model, and determines the principal evaluation indices through principal component analysis (PCA). Next, an artificial neural network (ANN) was established to evaluate commercial precision marketing, and optimized through k-means clustering (KMC). Finally, the optimized model was realized on MATLAB. The proposed EIS and ANN were proved scientific and effectiveness through simulations. The research results provide a reference for the application of the ANN in other fields of marketing.

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Evaluating discounts as a dimension of customer behavior analysis

Cited by 10 publications

References 20 publications

An Analytics Model for TelecoVAS Customers’ Basket Clustering using Ensemble Learning Approach

An Analytics Model for TelecoVAS Customers’ Basket Clustering using Ensemble Learning Approach

Making Meaningful User Segments from Datasets Using Product Dissemination and Product Impact

An Evaluation Strategy for Commercial Precision Marketing Based on Artificial Neural Network

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