Combining visual customer segmentation and response modeling

Yao, Zheng; Sarlin, Peter; Eklund, Tomas; Back, Barbro

doi:10.1007/s00521-013-1454-3

Cited by 23 publications

(15 citation statements)

References 54 publications

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“…Keramati et al [15] conducted an assessment of weaknesses and risks in Customer Relationship Management implementations when considering the return in terms of LTV. Customer analysis and segmentation may help to enhance future targeting in terms of customer responses to marketing campaigns [26], increasing LTV. Still, it is remarkably difficult to predict future behavior of customers with effective accuracy [19].…”

Section: Introductionmentioning

confidence: 99%

Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns

Moro

Cortez

Rita

2014

Neural Comput & Applic

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Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell longterm deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, under a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve predictive performance without even having to ask for more information to the companies they serve.

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Section: Introductionmentioning

confidence: 99%

Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns

Moro

Cortez

Rita

2014

Neural Comput & Applic

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“…Useful results can be gained by combining data mining techniques and business expertise. The ability of data clustering techniques to discover and reveal natural groupings of data makes it a widely used approach for customer segmentation and analysis (Yao, Sarlin, Eklund and Back, 2014). This paper adopts clustering and visualization techniques for efficient investigation of customer segmentation and target customer analysis.…”

Section: Data Mining Tasks and Crmmentioning

confidence: 99%

“…The Silhouette value for each data point is a measure of how similar a cluster point is to other points in its own cluster (when compared to data points in different clusters). The Silhouette coefficient ( ) of the ith data point is defined in equation 3 (Yao, Sarlin, Eklund and Back, 2014)…”

Section: Customer Segmentationmentioning

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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“…Among the four CRM dimensions, customer development (19 out of 51 articles, 37.3 %) is the most common dimension for which data analytics is used to support decision making. [18], [27], [40], [46] , [47] , [50], [55], [67] Customer Attraction 16 31 % [19], [20], [29], [34], [37], [44], [45], [49], [52], [53], [57], [59], [61], [65], [66], [68] Customer Retention 7 14 % [17], [21], [24], [26], [28], [35], [64] Customer Development 19 37 % [3], [22], [23], [25], [30], [31], [32], [33], [36], [38], [42], [43], [48], [51], [56], …”

Section: Classification Of the Articlesmentioning

confidence: 99%

“…Among the seven data mining techniques, clustering (7 out of 51 articles, 14 %) is the most common data mining technique for which data analytics is used to support decision making. [20], [22], [23], [32], [36], [38] [35], [42], [51], [55], [59] Full list of reviewed publications with classification is available at https://drive.google.com/open?id=0Bwp9RlyV--pwcFg1dC1kSzlMNG8 VI. CONCLUSION Application of data analytics in CRM is an emerging trend in the industry.…”

Section: Classification Of the Articlesmentioning

confidence: 99%

Data Analytics in CRM Processes: A Literature Review

Gončarovs

2017

Information Technology and Management Science

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-Nowadays, the data scarcity problem has been supplanted by the data deluge problem. Marketers and Customer Relationship Management (CRM) specialists have access to rich data on consumer behaviour. The current challenge is effective utilisation of these data in CRM processes and selection of appropriate data analytics techniques. Data analytics techniques help find hidden patterns in data. The present paper explores the characteristics of data analytics as the integrated tool in CRM for sales managers. The paper aims at analysing some of the different analytics methods and tools which can be used for continuous improvement of CRM processes. A systematic literature has been conducted to achieve this goal. The results of the review highlight the most frequently considered CRM processes in the context of data analytics.

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Combining visual customer segmentation and response modeling

Abstract: Customer Relationship Management (CRM) is a central part of Business

Cited by 23 publications

References 54 publications

Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns

Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns

Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification

Data Analytics in CRM Processes: A Literature Review

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