LRFMP model for customer segmentation in the grocery retail industry: a case study

2017 ◽  
Vol 35 (4) ◽  
pp. 544-559 ◽  
Author(s):  
Serhat Peker ◽  
Altan Kocyigit ◽  
P. Erhan Eren
Keyword(s):  
Real Life ◽  
Customer Segmentation ◽  
Marketing Strategies ◽  
Point Of View ◽  
Customer Relationships ◽  
Retail Industry ◽  
Content Type ◽  
Rfm Model ◽  
Customer Profiles ◽  
Loyal Customers

Purpose The purpose of this paper is to propose a new RFM model called length, recency, frequency, monetary and periodicity (LRFMP) for classifying customers in the grocery retail industry; and to identify different customer segments in this industry based on the proposed model. Design/methodology/approach This study combines the LRFMP model and clustering for customer segmentation. Real-life data from a grocery chain operating in Turkey is used. Three cluster validation indices are used for optimizing the number of groups of customers and K-means algorithm is employed to cluster customers. First, attributes of the LRFMP model are extracted for each customer, and then based on LRFMP model features, customers are segmented into different customer groups. Finally, identified customer segments are profiled based on LRFMP characteristics and for each customer profile, unique CRM and marketing strategies are recommended. Findings The results show that there are five different customer groups and based on LRFMP characteristics, they are profiled as: “high-contribution loyal customers,” “low-contribution loyal customers,” “uncertain customers,” “high-spending lost customers” and “low-spending lost customers.” Practical implications This research may provide researchers and practitioners with a systematic guideline for effectively identifying different customer profiles based on the LRFMP model, give grocery companies useful insights about different customer profiles, and assist decision makers in developing effective customer relationships and unique marketing strategies, and further allocating resources efficiently. Originality/value This study contributes to prior literature by proposing a new RFM model, called LRFMP for the customer segmentation and providing useful insights about behaviors of different customer types in the Turkish grocery industry. It is also precious from the point of view that it is one of the first attempts in the literature which investigates the customer segmentation in the grocery retail industry.

scholarly journals SEGMENTASI PELANGGAN BISNIS DENGAN MULTI KRITERIA MENGGUNAKAN K-MEANS

2019 ◽  
Vol 1 (2) ◽  
pp. 45
Author(s):  
Yanuar Wicaksono
Keyword(s):  
Customer Loyalty ◽  
Research Method ◽  
Customer Segmentation ◽  
Marketing Strategies ◽  
Customer Knowledge ◽  
Rfm Model ◽  
Knowledge Discovery In Database ◽  
Clustering Data ◽  
Loyal Customers ◽  
Better Than

Customer knowledge is an important asset, in gathering, and managing from sharing customer knowledge into valuable capital for the company. This causes the company to continue to innovate in producing products and serving according to customer needs. To find out the needs of each customer, the company needs to make customer segmentation. Customer segmentation is defined as the division into different groups with similar characteristics to develop marketing strategies that are tailored to customer characteristics. The easiest, simplest, well-known and commonly used model of customer characteristics is the model of the recency, frequency, monetary (RFM) criteria. The RFM model still has weaknesses in low customer segmentation capacity and does not provide information on the continuity of customer transactions in understanding customer loyalty. The research method used is the Knowledge Discovery in Database (KDD) method. The data is transformed into another format that suits the needs of analysis and then the customer is segmented using clustering data mining techniques with the K-Means algorithm. From the experiments, the RFM model guesses loyal customers when reviews, frequency and monetary are high. In reality, the recency only provides information on the customer making the last transaction and the high number of transaction frequencies can be done without the customer's stability in making transactions each period. Implementing multi-criteria in customer segmentation can be better than just RFM criteria. So it will not be wrong to treat customers according to the groups that have been formed.

scholarly journals The role of industries’ environmental reputation and competitive intensity on sustainability marketing strategy

2019 ◽  
Vol 23 (1) ◽  
pp. 3-24
Author(s):  
Mohammad Taherdangkoo ◽  
Beikpour Mona ◽  
Kamran Ghasemi
Keyword(s):  
Marketing Strategy ◽  
Structural Equation ◽  
Environmental Concern ◽  
Customer Segmentation ◽  
Influential Factors ◽  
Marketing Strategies ◽  
Competitive Intensity ◽  
Content Type ◽  
Adoption And Implementation ◽  
The Impact

Purpose This paper aims to highlight a model of industry drivers (industries’ environmental reputation and competitive intensity) that affect the sustainability marketing strategy segmentation, targeting and positioning based on customers’ environmental concern and explore the circumstances under which such a strategy affects performance. Design/methodology/approach The authors examined 64 Iranian export companies, which adopted sustainability marketing strategies across seven different industries. Achieved data are analyzed using a structural equation model methodology. Findings The results indicate that industries’ environmental reputation is positively related to the sustainability marketing strategies based on customers’ environmental concern and leads to superior financial and market performance. They also posit that competitive intensity has no significant effect on sustainability marketing strategies. Research limitations/implications This study specifically examines the impact of industry drivers on sustainability marketing strategy and performance. Logically, there might be other factors affecting the sustainability or other value dimensions that are not addressed in this study. Practical implications This paper provides some understanding of how organizations strength their sustainability marketing strategy, and they have to consider what factors to adopt such strategy. This paper also facilitates a better understanding of the customers’ needs and concern as a factor influencing sustainability marketing strategy adoption and implementation. Identifying the customer segmentation and market targeting based on the industry’s environmental can lead to the business will normally tailor the marketing mix (4Ps) with the needs and expectations of the target in mind. Originality/value This paper strengthens the effect of environmental concern of customer to understand what influences the success of the sustainability marketing adoption and implementation by investigating the most influential factors such as industries’ environmental reputation and competitive intensity.

Integrating multi-criteria decision making and clustering for business customer segmentation

2015 ◽  
Vol 115 (6) ◽  
pp. 1022-1040 ◽  
Author(s):  
Hülya Güçdemir ◽  
Hasan Selim
Keyword(s):  
Decision Making ◽  
Clustering Algorithms ◽  
Customer Segmentation ◽  
Database Management System ◽  
Multi Criteria Decision Making ◽  
Business Markets ◽  
Content Type ◽  
Partitional Clustering ◽  
Rfm Model ◽  
Business Customers

Purpose – The purpose of this paper is to develop a systematic approach for business customer segmentation. Design/methodology/approach – This study proposes an approach for business customer segmentation that integrates clustering and multi-criteria decision making (MCDM). First, proper segmentation variables are identified and then customers are grouped by using hierarchical and partitional clustering algorithms. The approach extended the recency-frequency-monetary (RFM) model by proposing five novel segmentation variables for business markets. To confirm the viability of the proposed approach, a real-world application is presented. Three agglomerative hierarchical clustering algorithms namely “Ward’s method,” “single linkage” and “complete linkage,” and a partitional clustering algorithm, “k-means,” are used in segmentation. In the implementation, fuzzy analytic hierarchy process is employed to determine the importance of the segments. Findings – Business customers of an international original equipment manufacturer (OEM) are segmented in the application. In this regard, 317 business customers of the OEM are segmented as “best,” “valuable,” “average,” “potential valuable” and “potential invaluable” according to the cluster ranks obtained in this study. The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. Research limitations/implications – The success of the proposed approach relies on the availability and quality of customers’ data. Therefore, design of an extensive customer database management system is the foundation for any successful customer relationship management (CRM) solution offered by the proposed approach. Such a database management system may entail a noteworthy level of investment. Practical implications – The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. By making customer segmentation decisions, the proposed approach can provides firms a basis for the development of effective loyalty programs and design of customized strategies for their customers. Social implications – The proposed segmentation approach may contribute firms to gaining sustainable competitive advantage in the market by increasing the effectiveness of CRM strategies. Originality/value – This study proposes an integrated approach for business customer segmentation. The proposed approach differentiates itself from its counterparts by combining MCDM and clustering in business customer segmentation. In addition, it extends the traditional RFM model by including five novel segmentation variables for business markets.

The value of prime customer relationships

2011 ◽  
Vol 32 (6) ◽  
pp. 50-51 ◽  
Author(s):  
Stuart E. Jackson
Keyword(s):  
Market Share ◽  
Customer Relationships ◽  
Market Position ◽  
Content Type ◽  
Case Examples ◽  
Customer Preference ◽  
Future Performance ◽  
The Individual ◽  
Strategic Market ◽  
Loyal Customers

PurposeThe author has previously written about the concept of “strategic market position.” Simply stated, SMP is a strategic discipline which ties together the principles of customer preference, producer economics, and corporate finance and helps companies understand when and how increased market share leads to stronger competitive position and higher profitability. This enables businesses to make smart decisions about where to expand and go after increased market share – and, conversely, where not to dig in deeper. Customer Market Position (CMP) carries SMP down to the individual customer level. It keys off the extra value inherent in what the author calls “prime customer relationships.” This paper aims to address these issues.Design/methodology/approachThe author discusses the benefits that accrue to businesses that have a high CMP with customers that account for the majority of their business. He cites a number of case examples of businesses that have reinforced their commercial success through closely managing their relationships and share of wallet with key customers. Examples of businesses in the article include medical products, e‐commerce and traditional retailing. The author then draws lessons that can be applied broadly by any business.FindingsThe author proposes four key applications for businesses wishing to apply the concept of CMP: benchmarking CMP against competitors to understand strengths and weaknesses; assessing profitability for customers in different tiers of CMP to stop subsidizing less loyal customers; identifying untapped potential to build further business with high‐CMP customers; using CMP as a leading indicator of future performance when making investment decisions.Originality/valueThis article sheds light on the economics and value of nurturing a business's most loyal customers and introduces a new metric to manage and monitor this.

The context of choice as boundary condition for gender differences in brand choice considerations

2018 ◽  
Vol 52 (5/6) ◽  
pp. 1280-1304 ◽  
Author(s):  
Enav Friedmann ◽  
Oded Lowengart
Keyword(s):  
Gender Differences ◽  
Gender Stereotypes ◽  
Latent Variables ◽  
Design Methodology ◽  
Real Life ◽  
Brand Choice ◽  
Marketing Strategies ◽  
Men And Women ◽  
Content Type ◽  
Practical Implications

Purpose Marketers often assume that functional, hedonic and socially conspicuous utilities in choosing a brand differ for men and women, thus different marketing strategies are required for each gender. To date, most of the research studies have used self-reported measures when shopping in general or in regard to a single product. The purpose of this research is to examine this question using two different contexts of brand choice: single choice evaluation (SCE) and brand selection context (BSC). This assessment will clarify whether male and female utilities when choosing a brand are indeed inherent and consistent. Design/methodology/approach Data were collected using surveys in three studies (N = 923). Conjoint analysis and ICLV (integrated choice and latent variables) models were examined. Findings BSC analysis that more closely mimics real-life contexts revealed that the consideration of these utilities is generally similar for men and women, while the SCE analysis showed significant gender differences. Practical implications In the context of choosing between brands, stereotypical gender targeting may be ineffective and might not be the best allocation of resources for marketers. Social implications Gender stereotypes in advertising seem to reconstruct differences that are not significant in a realistic brand selection context. Originality/value The context of choice was found to be a condition boundary for gender differences in brand choice considerations. Gender differences are not evolutionary or inherent.

Customer Segmentation Using RFM Model

2020 ◽  
Vol 8 (2) ◽  
pp. 153
Author(s):  
Made Chandra Hendrawan ◽  
I Putu Gede Hendra Suputra
Keyword(s):  
Market Competition ◽  
Customer Segmentation ◽  
Customer Relationships ◽  
Economic Community ◽  
Rfm Model ◽  
Asean Economic Community ◽  
Different Characteristics

At the time of the ASEAN Economic Community (MEA), Indonesia was selected by several companies from other countries to sell its products, including overseas paint companies. Therefore, the increasingly fierce market competition business is unlikely to focus solely on products sold, but it is also important to pay attention to the process of managing customer relationships with retailers. Segmentation is an early process that knows which customers can be sustained. In segmentation, customers who have certain similarities will be grouped into one. Customer segmentation is a model built in grouping customers according to certain standards to be used as a variable grouping. Customers will be the same group if they have certain similarities, while different groups or segments are customers who have different characteristics.

scholarly journals Facilitation fundamentals: redesigning an online course using adult learning principles and trifecta of student engagement framework

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Heather J. Leslie
Keyword(s):  
Student Engagement ◽  
Adult Learning ◽  
Teaching Strategies ◽  
Real Life ◽  
Point Of View ◽  
The Body ◽  
Online Course ◽  
Content Type ◽  
Adult Learning Principles ◽  
Learning Principles

PurposeThe purpose was to describe the redesigning of an online course that utilized adult learning principles and a framework to engage students.Design/methodology/approachThe methodology used is a first person account from the instructor point of view.FindingsFindings indicate that the teaching strategies used encouraged student engagement in the course.Research limitations/implicationsThe research is limited to one course with less than 20 students.Practical implicationsOther online instructors can utilize teaching strategies used that promote engagement among students.Social implicationsThis course is an example of a highly engaging online course. This shows that online courses can be engaging and satisfying for students.Originality/valueThis paper adds to the body of literature on what teaching strategies encourage students to engage online. It connects theories with real life examples that others teaching online can implement.

An attractiveness index based approach for warranty optimization

2015 ◽  
Vol 32 (4) ◽  
pp. 415-431 ◽  
Author(s):  
Prashant M. Ambad ◽  
Makarand S. Kulkarni
Keyword(s):  
Cost Model ◽  
Real Life ◽  
Point Of View ◽  
Design Stage ◽  
Optimization Approach ◽  
Content Type ◽  
Mean Time Between Failures ◽  
Component Selection ◽  
Decision Variables ◽  
Quantitative Manner

Purpose – The purpose of this paper is to develop an attractiveness index-based warranty cost model considering decision variables as design alternatives, warranty duration and support level. Design/methodology/approach – A warranty optimization approach is illustrated using a real life example of an automobile engine with Mean Time Between Failures and Warranty Attractiveness Index as constraints. Findings – It will help to improve the customer satisfaction by giving a more attractive warranty compared to that being offered by the competitors. Practical implications – Approaches that consider the effect of decision variables on attractiveness of a warranty policy in a quantitative manner have received relatively less attention. The paper attempts to capture the attractiveness of warranty from the manufacturer as well as customer point of view. Originality/value – The proposed approach will help manufacturers to take appropriate decisions related to warranty parameters and component selection at the design stage.

Impact of e-CRM on Website Loyalty of a Public Organization’s Customers

2013 ◽  
pp. 109-123
Author(s):  
Wen-Jang Jih
Keyword(s):  
Business Strategy ◽  
Information Technologies ◽  
Marketing Strategies ◽  
Point Of View ◽  
Customer Relationships ◽  
Customer Relationship ◽  
Strategic Flexibility ◽  
Disciplinary Field ◽  
Conflicting Goals

Web-enabled customer relationship management, or e-CRM, is able to simultaneously achieve the conflicting goals of strategic flexibility and operational efficiency in developing and executing innovative e-commerce marketing strategies. A multi-disciplinary field, involving such areas as marketing, economics, business strategy, information systems, social psychology and consumer behavior, e-CRM strengthens customer relationships via a portfolio of trust-building practices with the support of powerful information technologies. While most literature in e-CRM has examined how firms formulate and implement e-CRM initiatives, there is little information on the overall quality of an e-commerce firm’s e-CRM practices from the consumer point of view. This research proposes such a consumer-oriented concept, e-CRM value, based on existing e-CRM research, and examines the effect of e-CRM value on website loyalty. An empirical study is conducted to validate the theoretical model. Customers’ perceptions of e-CRM value have positive causal effects on their website loyalty. Valuable implications can be derived from this finding for public organizations in managing their customer relationships.

An integrated strategy for fleet maintenance planning

2017 ◽  
Vol 23 (4) ◽  
pp. 457-478 ◽  
Author(s):  
Manish Rawat ◽  
Bhupesh Kumar Lad
Keyword(s):  
Failure Rate ◽  
Real Life ◽  
Complex Problem ◽  
General Assumption ◽  
Point Of View ◽  
System Structure ◽  
Imperfect Maintenance ◽  
Content Type ◽  
Integrated Strategy

Purpose Conventionally, fleet maintenance decisions are made based on the level of repair (LOR) analysis. A general assumption made during LOR analysis is the consideration of the lifetime distribution with constant failure rate (CFR). However, industries do use preventive maintenance (PM) to extend the life of such components, which in turn may affect the LOR decisions such as repair/move/discard. The CFR assumption does not allow the consideration of effect of PM in LOR analysis. The purpose of this paper is to develop a more practical LOR analysis approach, considering the time-dependent failure rate (TDFR) of components and the effect of PM. Design/methodology/approach In the proposed methodology, first, a detailed life cycle model considering the effect of various parameters related to LOR and PM is developed. A simulation-based genetic algorithm approach is then used to obtain an integrated solution for LOR and PM schedule decisions. The model is also evaluated for the various cases of quality of maintenance measured in terms of degree of restoration. Findings The results, from the illustrative example for a multi-indenture and multi-echelon fleet maintenance network, show that the proposed integrated strategy leads to better LCC performance compare to the conventional approach. Additionally, it is identified that the degree of restoration also affects the PM schedule as well as LOR decisions of the fleet system. Therefore, consideration of TDFR is important to truly optimize the LOR decisions. The proposed approach can be applied to fleet of any equipment. Research limitations/implications The approach is illustrated using a hypothetical example of an industrial system. A more complex system structure in terms of number of machines, types of machines (identical vs non-identical), number of echelons, possible repair actions at various echelons, etc. may be present for a particular industrial case. However, the approach presented is generic and can be extended to any system. Moreover, the aim of the paper is to highlight the importance of the considering PM and quality of maintenance in LOR decision making. Originality/value To the best of the authors’ knowledge, this is the first work which considers the effect of PM and quality of maintenance on LOR analysis. Consideration of TDFR and imperfect maintenance while optimizing LOR decisions is a complex problem. Thus, the work is of high significance from the research point of view. Also, most of the real life fleet systems use PM to extend the life of the equipment. Thus, present paper is a more practical approach for LOR analysis of such systems.

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