Using RFM approach with PBL to course design

Based on Problem-Based Learning (PBL) concept, this study creates an advertising script by conducting RFM (recency, frequency, monetary) approach from the data analysis in customer relationship management (CRM) course. In PBL process, the students themselves should use RFM method to make segment from the customer database of service industries and find out the characteristics of target segment based on the demographic variables and according to these traits to form their advertising scripts. Through finding the highest value of segment as the target customer, they should focus on the characteristic of their choice target and create an advertising script to attract this segment by their traits. These processes of steps can provide the course design of CRM or advertising project to make creation from customer targeting

Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach

Background: Decision makers use the process of determining the best course of action by processing, analysing & interpreting the data to gain insights, known as Business Intelligence. Some decision support systems use sales figures to predict future expansion, but few consider the effect of customer data. Objectives: The main objective of this study is to build a model that will give a forecast based on fine-tuned sales numbers using some customer-centric features. Methods/Approach: We first use the RFM model to segment the customers into distinct segments based on customer buying characteristics and then discard the segments that are irrelevant to the business. Then we use the ARIMA model to do the sales forecasting for the remainder of the data. Results: Using this model, we were able to achieve a better fitment of the data for the prediction model and achieved a better accuracy when used after RFM analysis. Conclusions: We tried to merge two different concepts to do a cross-functional analysis for better decision-making. We were able to present the RFM-ARIMA model as a better metric or approach to fine-tune the sales analysis

A multi layer recency frequency monetary method for customer priority segmentation in online transaction

Customer segmentation is a critical step toward appropriately differentiating services to different customers. One common way of segmenting customers is by using what is called Recency, Frequency, Monetary (RFM) approach, where customers are classified based on the recency of their transactions as well as how often they purchase goods and services and how much money they spent. However, this approach is not able to fairly differentiate customers especially when it comes to the cases where old customers have decreased or stopped their purchases and the new customers just started buying. In order to overcome this, we proposed what is called Multi Layer Recency, Frequency, and Monetary (MLRFM) approach. In this approach, we divide time periods into multiple layers and the recency, frequency, and monetary values are analyzed considering these different segments. Our numerical examples show that this multi layer approach can provide a good alternative for the companies that sell products online and customers are behaving very dynamically

Guest editorial: Globalization and the convergence of creativity, innovation and entrepreneurship

This Special Issue of the Management Research Review comprises a selection of the best papers presented at the 2014 annual conference of the International Management Research Academy (IMRA) co-organized with the Global Business School at the Kean University Union campus in New Jersey, USA. The theme of the conference centered on “Globalization and the convergence of creativity, innovation and entrepreneurship”, with the aim of bringing together a diverse and multi-disciplinary group of scholars and practitioners from across emerged, emerging and frontier markets. The conference received 108 extended abstracts involving 239 authors from 31 countries, of which 31 proposals were rejected in the first round, leaving a total of 76 submissions to be invited for presentation. The conference attracted a number of leading academics and practitioners, including the keynote addresses by Dr. Raj Shaj, Founder, President and Chief Executive Officer of Telemed Ventures; Joseph Sheridan, President and Chief Operating Officer of Wakefern Food Corporation; Dr. Dawood Farahi, President of Kean University; and Dr. Michael Cooper, Dean of the Global Business School at Kean University. While the keynote speakers uniformly highlighted the need for forward-looking and entrepreneurial leaders with global and multicultural perspectives, the selected conference presentations provided valuable examples and demonstrated ways in which the convergence of creativity, innovation and entrepreneurship could offer significant competitive advantage to any business in our increasingly globalized environment.info:eu-repo/semantics/acceptedVersio

Segmentation of life insurance customers based on their profile using fuzzy clustering

In the current competitive environment, companies will be able to adjust business strategies, they use market segmentation based on practical ways rather than using traditional approaches or incomplete and impractical mass marketing. In recent years, mining has gained attention and popularity in the business world. The goal of data mining projects is to convert the raw data into useful information. Clustering can also be used to explore differences in attitudes and intentions of the clients. In this study, we used fuzzy clustering on 1071 life insurance customers during March to October 2014. Results show that the optimal number of clusters was 2 which were named as "investment" and "life safety". Some suggestions are presented to improve the performance of the insurance company

Analysis and development of customer segmentation criteria and tools for SMEs

In order to use the limited resources of sales and marketing optimally, and to provide customers with the best services, effective customer segmentation is of prime importance. This thesis deals with methods for analysing and comparing the individual values of customers for SMEs (Small Medium Enterprises), because not all customers bring the same value to the company and not every customer can be treated in the same way. The different segmentation models are judged by different criteria. Which segmentation method allows a company to treat customers in the best possible way based on their value for the company? To answer this question first requires the SME company to determine whether they know the monetary or non-monetary value of their customers. The researcher examined if the size of the company influences the choice of segmentation criteria and method. To determine this, it is necessary to address which companies are SMEs. The main methods are reviewed extensively likewise available software models were evaluated and included in the research, and the advantages and disadvantages are compared. For this research topic, a mixed-method design was chosen. The researcher carried out one-to-one semi-structured expert interviews and, parallel to the qualitative research, quantitative data from a technical retailing company’s database was analysed. The company has data from more than 10,000 customers in the business warehouse and CRM system. The results of this research provide new thoughts to reflect on whether the segmentation methods of the existing literature are useful for SMEs in the B2B business and provide the basis for further research and development in this field. The new segmentation method, identified and confirmed through follow-up interviews in this research, will be of immense value to practitioners. Especially for sales and marketing managers working in this field

Churn prediction using customers' implicit behavioral patterns and deep learning

The processes of market globalization are rapidly changing the competitive conditions of the business and financial sectors. With the emergence of new competitors and increasing investments in the banking services, an environment of closer customer relationships is the demand of today’s economics. In such a scenario, the concept of customer’s willingness to change the service provider – i.e. churn, has become a competitive domain for organizations to work on. In the banking sector, the task to retain the valuable customers has forced management to preemptively work on customers data and devise strategies to engage the customers and thereby reducing the churn rate. Valuable information can be extracted and implicit behavior patterns can be derived from the customers’ transaction and demographic data. Our prediction model, which is jointly using the time and location based sequence features has shown significant improvement in the customer churn prediction. Various supervised models had been developed in the past to predict churning customers; our model is using the features which are derived jointly from location and time stamped data. These sequenced based feature vectors are then used in the neural network for the churn prediction. In this study, we have found that time sequenced data used in a recurrent neural network based Long Short Term Memory (LSTM) model can predict with better precision and recall values when compared with baseline model. The feature vector output of our LSTM model combined with other demographic and computed behavioral features of customers gave better prediction results. We have also iv proposed and developed a model to find out whether connection between the customers can assist in the churn prediction using Graph convolutional networks (GCN); which incorporate customer network connections defined over three dimension

Essays on multichannel marketing

Multichannel marketing is the practice of simultaneously offering information, goods, services, and support to customers through two or more synchronized channels. In this dissertation, I develop an integrated framework of multichannel marketing and develop models to assist managers in their marketing resource allocation decisions. In the first essay of the dissertation, I investigate the factors that drive customers multichannel shopping behavior and identify its consequences for retailers. In the second essay, I build on this work and develop a model that enables firms to optimize their allocation of marketing resources across different customer-channel segments. In the first essay, I develop a framework comprising the factors that drive consumers’ channel choice, the consequences of channel choice, and their implications for managing channel equity. The results show that customer-channel choice is driven in a nonlinear fashion by a customer demographic variable such as age and is also influenced by consumer shopping traits such as number of categories bought and the duration of relationship with a retailer. I show that by controlling for the moderating effects of channel-category associations, the influence of customers’ demographics and shopping traits on their channel choices can vary significantly across product categories. Importantly, the results show that multichannel shoppers buy more often, buy more items, and spend considerably more than single channel shoppers. The channel equity of multichannel customers is nearly twice that of the closest single channel customers (online or offline). In the second essay, I propose a model for optimal allocation of marketing efforts across multiple customer-channel segments. I first develop a set of models for consumer response to marketing efforts for each channel-customer segment. This set comprises four models, the first for purchase frequency, the second for purchase quantity, the third for product return behavior, and the fourth for contribution margin of purchase. The results show that customers’ responses to firm marketing efforts vary significantly across the customer-channel segments. They also suggest that marketing efforts influence purchase frequency, purchase quantity and monetary value in different ways. The resource allocation results show that profits can be substantially improved by reallocating marketing efforts across the different customer-channel segments

The perfect dose can be the right choice

Since founded a subsidiary in 1989 Henkel Ibérica continues to be one of the most innovative companies in the detergents industry in Portugal, being number one in a lot of country in the world. Persil, the most challenging brand of the company since 1907, has having a huge evolution since quality, sales and portfolio, being the brand number two in the detergents industry in Portugal. In 2015 one of the innovations of this brand came to challenge the market with a new package, believing to be a revolution to the end of waste. After few months since the launch of the product, the sales started to be lower than the expectations of the company, seeing itself obligated to create a revitalization plan. Due to huge external changes and a lack of support of the company in this plan, the product was leaving its presence in the marketplace. The process of an innovative product that turns to not be a success to the company, as the case of Persil Perfect Dose, is an interesting subject of discussion. It includes themes like the influence of the external environment, the product’s positioning and marketing mix. In this sense, it may be analyzed by any marketing or strategy areas, supported by the theoretical concepts proposed in the literature review. The proposed reflection focuses essentially on the development of a revitalization plan to Persil Perfect Dose through extensive analyses of the case.Desde 1989, em que foi fundada uma subsidiária em Portugal, Henkel Ibérica continua a ser uma das mais inovadoras empresas na indústria dos detergentes em Portugal, sendo a número um em vários países do mundo. Persil, a marca mais desafiadora da companhia desde 1907, tem tido uma grande evolução a nível de qualidade, vendas e de portfólio, sendo a marca número dois de detergentes em Portugal. Em 2015, uma das inovações desta marca veio desafiar o mercado com uma nova embalagem, acreditando que seria uma revolução para o fim do desperdício. No entanto, passados alguns meses desde o lançamento do produto, o valor das vendas começou a ficar abaixo das expectativas da companhia, sendo a mesma obrigada a criar um plano de revitalização. Devido a grandes mudanças externas e a uma falta de apoio da parte da companhia neste plano de revitalização, o produto foi deixando de ter presença no mercado. O facto do lançamento de um produto inovador no mercado que não resultou num sucesso de vendas para a companhia, como o caso do Persil Perfect Dose, é um tema interessante de discussão pois inclui temas como a influência do ambiente externo, posicionamento do produto e marketing mix. Neste sentido, este tema pode ser analisado pelas áreas de marketing ou de estratégia, sendo apoiado por conceitos teóricos propostos na revisão de literatura. A reflexão proposta foca-se essencialmente no desenvolvimento de um plano de revitalização para o produto Persil Perfect Dose através de uma análise extensiva do caso