Optimal selection of potential customer range through the union sequential pattern by using a response model

Chen, Wen-Chin; Hsu, Cheng–Che; Hsu, Jing-Ning

doi:10.1016/j.eswa.2010.12.078

Cited by 14 publications

(8 citation statements)

References 17 publications

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“…These techniques can be very powerful, but each algorithm also make several stringent, but different, assumptions on the underlying distribution between the independent variables and the dependent variable. To counter this, more advanced datamining algorithms have been proposed for discriminating between responders and non-responders, such as artificial neural networks (Baesens et , Chen et al 2011, Curry and Moutinho 1993, Zahavi and Levin 1997, decision tree-generating techniques (Buckinx et al 2004, Chen et al 2012, Haughton and Oulabi 1997, McCarty and Hastak 2007, Rada 2005) and k-NN learners (Govindarajan andChandrasekaran 2010, Kang et al 2012). …”

Section: Classification Algorithmsmentioning

confidence: 98%

Improving direct mail targeting through customer response modeling

Coussement

Harrigan

Benoit

2015

Expert Systems with Applications

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Section: Classification Algorithmsmentioning

confidence: 98%

Improving direct mail targeting through customer response modeling

Coussement

Harrigan

Benoit

2015

Expert Systems with Applications

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“…In contrast, the second group of customers, who hold positive attitude, is confident in the prospects of WeChat business and they are our current customers. However, these customers only represent 0.3% of the total consumers compared with current customer cluster which registers 29% of the total [7] . From above analysis, we know that there is ample room for WeChat business and lots of potential customers remain there.…”

Section: How To Single Out Customer Shopping Interests Based On Analymentioning

confidence: 98%

Analysis on Customer Behavior and interest in mining on WeChat Business

Zhang¹

2018

PBES

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Abstract:In recent years, with the increasing number of Wechat users, a new shopping mode has emerged in the social platform of Wechat, which is what we call Wechat business. It can share goods and publicly display goods information to social friends. Since Wechat's origin is social rather than marketing tools, Wechat business can find user groups and interconnected big data more than traditional e-commerce. However, with the advent of the era of big data, Wechat business has generated huge customer behavior data, and with the strong demand of micro-business dealers to convert massive amounts of data into useful information, how to dig out users' shopping in huge behavioral data. Interest, and thus the personalized recommendation service in the micro-business model is particularly urgent.

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“…In commercial applications of data-mining algorithms that involve the marketing of consumer products, a lift chart analysis is often used to predict the increase in response rate that would be achieved by targeting a specific demographic group, as opposed to blanket marketing of a given product to the entire population of consumers (Shin and Cho, 2006;Chen et al, 2011;Sheng et al, 2014). In the context of reproductive management of dairy herds, a lift chart analysis can be used to predict the gain in conception rate that would be achieved by inseminating a selected subset of cows with high probability of conception, as opposed to inseminating all eligible cows in the herd.…”

Section: Lift Chart Analysismentioning

confidence: 99%

Optimization of reproductive management programs using lift chart analysis and cost-sensitive evaluation of classification errors

Shahinfar

Guenther

Page

et al. 2015

Journal of Dairy Science

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The common practice on most commercial dairy farms is to inseminate all cows that are eligible for breeding, while ignoring (or absorbing) the costs associated with semen and labor directed toward low-fertility cows that are unlikely to conceive. Modern analytical methods, such as machine learning algorithms, can be applied to cow-specific explanatory variables for the purpose of computing probabilities of success or failure associated with upcoming insemination events. Lift chart analysis can identify subsets of high fertility cows that are likely to conceive and are therefore appropriate targets for insemination (e.g., with conventional artificial insemination semen or expensive sex-enhanced semen), as well as subsets of low-fertility cows that are unlikely to conceive and should therefore be passed over at that point in time. Although such a strategy might be economically viable, the management, environmental, and financial conditions on one farm might differ widely from conditions on the next, and hence the reproductive management recommendations derived from such a tool may be suboptimal for specific farms. When coupled with cost-sensitive evaluation of misclassified and correctly classified insemination events, the strategy can be a potentially powerful tool for optimizing the reproductive management of individual farms. In the present study, lift chart analysis and cost-sensitive evaluation were applied to a data set consisting of 54,806 insemination events of primiparous Holstein cows on 26 Wisconsin farms, as well as a data set with 17,197 insemination events of primiparous Holstein cows on 3 Wisconsin farms, where the latter had more detailed information regarding health events of individual cows. In the first data set, the gains in profit associated with limiting inseminations to subsets of 79 to 97% of the most fertile eligible cows ranged from $0.44 to $2.18 per eligible cow in a monthly breeding period, depending on days in milk at breeding and milk yield relative to contemporaries. In the second data set, the strategy of inseminating only a subset consisting of 59% of the most fertile cows conferred a gain in profit of $5.21 per eligible cow in a monthly breeding period. These results suggest that, when used with a powerful classification algorithm, lift chart analysis and cost-sensitive evaluation of correctly classified and misclassified insemination events can enhance the performance and profitability of reproductive management programs on commercial dairy farms.

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Optimal selection of potential customer range through the union sequential pattern by using a response model

Cited by 14 publications

References 17 publications

Improving direct mail targeting through customer response modeling

Improving direct mail targeting through customer response modeling

Analysis on Customer Behavior and interest in mining on WeChat Business

Optimization of reproductive management programs using lift chart analysis and cost-sensitive evaluation of classification errors

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