A data mining approach for turning potential customers into real ones in basket purchase analysis

Kazemi, Abolfazl; Babaei, Mohammad Esmaeil; Javad, Mahsa Oroojeni Mohammad

doi:10.1504/ijbis.2015.069427

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…Decision tree: aims to identify and classify the factors for turning potential customers into“real”customers [ 31 ]. Specifically, decision trees can deal with both continuous and discrete attributes for extracting valuable hidden knowledge from leads data.…”

Section: State Of Lead Scoring Modelsmentioning

confidence: 99%

“…Cost reductions/monetary savings stand for money that can be saved per lead conversion. Kazemi et al [ 31 ] proposed a predictive lead scoring model considering effective identification factors to increase sales and customer satisfaction by using a decision tree and a basket purchase technique to analyze which potential customers were the“real” ones. The suggested model was tested on data from a furniture producer.…”

Section: State Of Lead Scoring Modelsmentioning

confidence: 99%

“…The suggested model was tested on data from a furniture producer. In the post-performance evaluation, administrative costs were reduced by 8%, and the profit was increased by 15% [ 31 ]. These results indicated that the proposed model positively affects sales in terms of cost reduction and profit growth.…”

Section: State Of Lead Scoring Modelsmentioning

confidence: 99%

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The state of lead scoring models and their impact on sales performance

Andreev

Benyoucef

2023

Inf Technol Manag

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Although lead scoring is an essential component of lead management, there is a lack of a comprehensive literature review and a classification framework dedicated to it. Lead scoring is an effective and efficient way of measuring the quality of leads. In addition, as a critical Information Technology tool, a proper lead scoring model acts as an alleviator to weaken the conflicts between sales and marketing functions. Yet, little is known regarding lead scoring models and their impact on sales performance. Lead scoring models are commonly categorized into two classes: traditional and predictive. While the former primarily relies on the experience and knowledge of salespeople and marketers, the latter utilizes data mining models and machine learning algorithms to support the scoring process. This study aims to review and analyze the existing literature on lead scoring models and their impact on sales performance. A systematic literature review was conducted to examine lead scoring models. A total of 44 studies have met the criteria and were included for analysis. Fourteen metrics were identified to measure the impact of lead scoring models on sales performance. With the increased use of data mining and machine learning techniques in the fourth industrial revolution, predictive lead scoring models are expected to replace traditional lead scoring models as they positively impact sales performance. Despite the relative cost of implementing and maintaining predictive lead scoring models, it is still beneficial to supersede traditional lead scoring models, given the higher effectiveness and efficiency of predictive lead scoring models. This study reveals that classification is the most popular data mining model, while decision tree and logistic regression are the most applied algorithms among all the predictive lead scoring models. This study contributes by systematizing and recommending which machine learning method (i.e., supervised and/or unsupervised) shall be used to build predictive lead scoring models based on the integrity of different types of data sources. Additionally, this study offers both theoretical and practical research directions in the lead scoring field.

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Section: State Of Lead Scoring Modelsmentioning

confidence: 99%

Section: State Of Lead Scoring Modelsmentioning

confidence: 99%

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The state of lead scoring models and their impact on sales performance

Andreev

Benyoucef

2023

Inf Technol Manag

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“…They achieve these by exploiting the four major dimensions of CRM: identification, attraction, retention and satisfaction (Kazemi, Babaei and Javad, 2015). Investing in customers satisfactiondeveloping skills and methodologies to identify the needs and expectations of customers, and strengthening customers relationship -by providing them with more and more benefits, are key indicators to gaining competitive advantage in the market.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification

Inyang¹,

Obot²,

Ekpenyong³

et al. 2017

MAS

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Maintaining healthy organization-customers relationship has positive influence on customers' behavioral tendencies as regards preference to products and services, buying behavior, loyalty, satisfaction, and so on. To achieve this, an in-depth analysis of customers' characteristics and purchasing behavioral trend is required. This paper proposes a hybrid unsupervised learning framework consisting of k-means algorithm and self-organizing maps (SOMs) for customer segmentation and behavior analysis. K-means algorithm was used to partition the entire input space of customers' transaction dataset into 3 and 4 disjoint segments based on customers' frequency (F) and monetary value (MV). SOM provided visualization of the underlying clusters and discovered customers' relationships in the dataset. Interaction of F and MV clusters resulted in 12 sub-clusters. An in-depth analysis of each sub-cluster was also performed and appropriate customer relationship management (CRM) strategies established for each sub-cluster. Discovered knowledge will guide effective allocation of resources to each customer cluster and other organizational decision support functions much required by CRM systems.

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