Ranking Potential Customers Based on Group-Ensemble

2011 ◽  
pp. 355-365
Author(s):  
Zhang Zhi-Zhuo ◽  
Chen Qiong ◽  
Ke Shang-Fu ◽  
Wu Yi-Jun ◽  
Qi Fei
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Regression Tree ◽  
Marketing Strategies ◽  
Decision Makers ◽  
Missing Value ◽  
Data Imbalance ◽  
Ranking Model ◽  
Potential Customers

Ranking potential customers has become an effective tool for company decision makers to design marketing strategies. The task of PAKDD competition 2007 is a cross-selling problem between credit card and home loan, which can also be treated as a ranking potential customers problem. This article proposes a 3-level ranking model, namely Group-Ensemble, to handle such kinds of problems. In our model, Bagging, RankBoost and Expending Regression Tree are applied to solve crucial data mining problems like data imbalance, missing value and time-variant distribution. The article verifies the model with data provided by PAKDD Competition 2007 and shows that Group-Ensemble can make selling strategy much more efficient.

Ranking Potential Customers Based on Group-Ensemble

2010 ◽  
pp. 776-786
Author(s):  
Zhi-Zhuo Zhang ◽  
Qiong Chen ◽  
Shang-Fu Ke ◽  
Yi-Jun Wu ◽  
Fei Qi ◽  
...  
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Regression Tree ◽  
Marketing Strategies ◽  
Decision Makers ◽  
Missing Value ◽  
Data Imbalance ◽  
Ranking Model ◽  
Potential Customers

Ranking potential customers has become an effective tool for company decision makers to design marketing strategies. The task of PAKDD competition 2007 is a cross-selling problem between credit card and home loan, which can also be treated as a ranking potential customers problem. This article proposes a 3-level ranking model, namely Group-Ensemble, to handle such kinds of problems. In our model, Bagging, RankBoost and Expending Regression Tree are applied to solve crucial data mining problems like data imbalance, missing value and time-variant distribution. The article verifies the model with data provided by PAKDD Competition 2007 and shows that Group-Ensemble can make selling strategy much more efficient.

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

Influence of social media marketing towards purchase of residential property

2019 ◽  
Vol 118 (6) ◽  
pp. 145-149
Author(s):  
A. Ekanthalingam ◽  
Dr. A. Gopinath
Keyword(s):  
Social Media ◽  
High Speed ◽  
Word Of Mouth ◽  
Marketing Strategies ◽  
Social Media Marketing ◽  
Purchase Decision ◽  
Residential Property ◽  
Customer Delight ◽  
The Right ◽  
Potential Customers

‘Marketing’ is not just an activity. It is a process, a philosophy and a phenomenon. The evolution of marketing has produced tremendous benefits to business and end consumers. The innovation in this field has been steady and yet at high speed. From ‘word of mouth advertising’ which was the only option earlier we are now at the mercy of what consumers are sharing about their experience on the internet. Social Media has become more powerful than what we think and this article shows how we can leverage this to benefit the top-line and customer delight. We dive deep to understand the influence Social Media can create towards purchase of residential property. As much complex it is to make the purchase decision of a property, it is equally difficult for marketers to send the right message to their target audience. Through this article, we are trying to see how marketers have transformed their traditional marketing strategies to address the needs of the millennial population, who are the most potential customers for property purchase.

Transformation from Traditional to Digital Marketing: A Case on Facebook Marketing for Micro Foodservice Brands

2020 ◽  
Vol 2 (2) ◽  
pp. 166-187
Keyword(s):  
Marketing Strategies ◽  
Restaurant Industry ◽  
Digital Marketing ◽  
Customer Engagement ◽  
Per Se ◽  
Potential Customers ◽  
Inexpensive Technique ◽  
Facebook Marketing

This study analyses the transformation of marketing strategies from traditional to 'digital' and finds out the efficacy of 'Facebook' marketing for small or micro-enterprise brands; in the case of the restaurant industry. The study looks for the answers to questions such as How ‘Facebook' has changed the means of marketing in the case of small restaurant brands. Two restaurant brands were considered for the study. A qualitative case approach was adopted for the study. Major findings of the study have revealed that Facebook marketing is the direct, easiest and economical mode of communication with current and potential customers through 'Facebook page', to disseminate information regarding brand and services through 'Facebook posts', 'Comments' and 'chats'; besides, to maintain rapid feedback service to customers' queries. In addition, through 'Facebook' activities i.e., an instance of 'Likes', 'Reviews', 'Check-ins', and' Share' customers, themselves ensue as a source for promoting the brands. Nevertheless, for these small or microbrands' Facebook Marketing' per se, it is an inexpensive technique for effective marketing; additionally, it fosters mutual relationships and increases the level of customer engagement.

scholarly journals Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

2018 ◽  
Vol 8 (8) ◽  
pp. 1369 ◽  
Author(s):  
Alireza Arabameri ◽  
Biswajeet Pradhan ◽  
Hamid Reza Pourghasemi ◽  
Khalil Rezaei ◽  
Norman Kerle
Keyword(s):  
Data Mining ◽  
Spatial Relationship ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Drainage Density ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Boosted Regression Tree ◽  
Area Index

Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.

scholarly journals Leveraging Social Media for Development in Organisations

2017 ◽  
Vol 13 (4) ◽  
pp. 201
Author(s):  
Ajith Sundaram
Keyword(s):  
Social Media ◽  
Social Influence ◽  
Detailed Analysis ◽  
Computer Systems ◽  
Marketing Strategies ◽  
Online Marketing ◽  
Potential Customers ◽  
Marketing Approach

Marketers must never underestimate an online buyer. If they are assuming that mere marketing efforts from their side will influence a buyer, then they are being uncalculated and ignorant. Potential customers are not isolated people who are glued to their computer systems confused as to what product to buy. Instead they are tech-savvy, socially active, smart buyers who make a detailed analysis about the product they intend to buy online. Therefore, marketers need to re-design their online marketing strategies to suit to the needs of the buyer. The marketing approach should focus on social influence marketing as well.

scholarly journals Groundwater Augmentation through the Site Selection of Floodwater Spreading Using a Data Mining Approach (Case study: Mashhad Plain, Iran)

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1405 ◽  
Author(s):  
Seyed Naghibi ◽  
Mehdi Vafakhah ◽  
Hossein Hashemi ◽  
Biswajeet Pradhan ◽  
Seyed Alavi
Keyword(s):  
Data Mining ◽  
Regression Tree ◽  
Roc Curves ◽  
Flood Damage ◽  
Boosted Regression Trees ◽  
Classification And Regression Tree ◽  
Operating Characteristics ◽  
National Guidelines ◽  
Conditioning Factors ◽  
Suitability Maps

It is a well-known fact that sustainable development goals are difficult to achieve without a proper water resources management strategy. This study tries to implement some state-of-the-art statistical and data mining models i.e., weights-of-evidence (WoE), boosted regression trees (BRT), and classification and regression tree (CART) to identify suitable areas for artificial recharge through floodwater spreading (FWS). At first, suitable areas for the FWS project were identified in a basin in north-eastern Iran based on the national guidelines and a literature survey. Using the same methodology, an identical number of FWS unsuitable areas were also determined. Afterward, a set of different FWS conditioning factors were selected for modeling FWS suitability. The models were applied using 70% of the suitable and unsuitable locations and validated with the rest of the input data (i.e., 30%). Finally, a receiver operating characteristics (ROC) curve was plotted to compare the produced FWS suitability maps. The findings depicted acceptable performance of the BRT, CART, and WoE for FWS suitability mapping with an area under the ROC curves of 92, 87.5, and 81.6%, respectively. Among the considered variables, transmissivity, distance from rivers, aquifer thickness, and electrical conductivity were determined as the most important contributors in the modeling. FWS suitability maps produced by the proposed method in this study could be used as a guideline for water resource managers to control flood damage and obtain new sources of groundwater. This methodology could be easily replicated to produce FWS suitability maps in other regions with similar hydrogeological conditions.

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