Modelling Customer Relationships as Hidden Markov Chains

Models in behavioural relationship marketing suggest that relations between the customer and the company change over time as a result of the continuous encounter. Some theoretical models have been put forward concerning relationship marketing, both from the standpoints of consumer behaviour and empirical modelling. In addition to these, this study proposes the hidden Markov model (HMM) as a potential tool for assessing customer relationships. Specifically, the HMM is submitted via the framework of a Markov chain model to classify customers relationship dynamics of a telecommunication service company by using an experimental data set. We develop and estimate an HMM to relate the unobservable relationship states to the observed buying behaviour of the customers giving an appropriate classification of the customers into the relationship states. By merely accounting for the functional and unobserved heterogeneity with a two-state hidden Markov model and taking estimation into account via an optimal estimation method, the empirical results not only demonstrate the value of the proposed model in assessing the dynamics of a customer relationship over time but also gives the optimal marketing-mixed strategies in different customer state

Modelling Customers Lifetime Value For Non-Contractual Business

Due to the increasing importance placed on customer equity in today's business environment, many companies are focusing on the notion of customer loyalty and profitability to increase market share. Building a successful Customer Relationship Management (CRM), a company starts from identifying true value and customer loyalty because customer value can provide basic information to spread more targeted and personalized marketing. In this paper, customer lifetime value (CLV) is used for customer segmentation in non-contracted businesses. The results obtained from this study are very acceptable. CLV has successfully analyzed and produced a fairly strong assumption about the value possessed by each customer whether they will make a return transaction or not

The value of a "free" customer

We study the problem of a firm that faces asymmetric information about the productivity of its potential workers. In our framework, a worker’s productivity is either assigned by nature at birth, or determined by an unobservable initial action of the worker that has persistent effects over time. We provide a characterization of the optimal dynamic compensation scheme that attracts only high productivity workers: consumption –regardless of time period– is ranked according to likelihood ratios of output histories, and the inverse of the marginal utility of consumption satisfies the martingale property derived in Rogerson (1985). However, in the case of i.i.d. output and square root utility we show that, contrary to the features of the optimal contract for a repeated moral hazard problem, the level and the variance of consumption are negatively correlated, due to the influence of early luck into future compensation. Moreover, in this example long-term inequality is lower under persistent private informationCustomer lifetime value, CRM, Dynamic programming, GMM Estimation

Supplier Selection and Relationship Management: An Application of Machine Learning Techniques

Managing supply chains is an extremely challenging task due to globalization, short product life cycle, and recent advancements in information technology. These changes result in the increasing importance of managing the relationship with suppliers. However, the supplier selection literature mainly focuses on selecting suppliers based on previous performance, environmental and social criteria and ignores supplier relationship management. Moreover, although the explosion of data and the capabilities of machine learning techniques in handling dynamic and fast changing environment show promising results in customer relationship management, especially in customer lifetime value, this area has been untouched in the upstream side of supply chains. This research is an attempt to address this gap by proposing a framework to predict supplier future value, by incorporating the contract history data, relationship value, and supply network properties. The proposed model is empirically tested for suppliers of public works and government services Canada. Methodology wise, this thesis demonstrates the application of machine learning techniques for supplier selection and developing effective strategies for managing relationships. Practically, the proposed framework equips supply chain managers with a proactive and forward-looking approach for managing supplier relationship

Determination of optimal pricing and warranty policies

An important problem facing manufacturers in today\u27s competitive market is the determination of the selling price of a product and its warranty period. A longer warranty may serve as a signal of product reliability; however, it may also lead to an increase in cost and hence reduce the profit if the product reliability is low. A burn-in test may be used to improve the reliability of products prior to their shipment.;This research presented integrated models for maximizing the expected profit for products that are subjected to a burn-in test and sold with warranty. The burn-in time, warranty period, and price were chosen as three decision variables in these models. The price and warranty period were treated as marketing variables and a simple multiplicative form was used to model their effect on sales. Solution procedures were developed for several warranty policies. These procedures are applicable for any failure time distribution. Three failure time distributions were further investigated and formulas for optimal solutions were derived. Finally, two sets of data were used to illustrate the application of the models. Two computer programs were developed to solve the models both parametrically and nonparametically

Demand Estimation at Manufacturer-Retailer Duo: A Macro-Micro Approach

This dissertation is divided into two phases. The main objective of this phase is to use Bayesian MCMC technique, to attain (1) estimates, (2) predictions and (3) posterior probability of sales greater than certain amount for sampled regions and any random region selected from the population or sample. These regions are served by a single product manufacturer who is considered to be similar to newsvendor. The optimal estimates, predictions and posterior probabilities are obtained in presence of advertising expenditure set by the manufacturer, past historical sales data that contains both censored and exact observations and finally stochastic regional effects that cannot be quantified but are believed to strongly influence future demand. Knowledge of these optimal values is useful in eliminating stock-out and excess inventory holding situations while increasing the profitability across the entire supply chain. Subsequently, the second phase, examines the impact of Cournot and Stackelberg games in a supply-chain on shelf space allocation and pricing decisions. In particular, we consider two scenarios: (1) two manufacturers competing for shelf space allocation at a single retailer, and (2) two manufacturers competing for shelf space allocation at two competing retailers, whose pricing decisions influence their demand which in turn influences their shelf-space allocation. We obtain the optimal pricing and shelf-space allocation in these two scenarios by optimizing the profit functions for each of the players in the game. Our numerical results indicate that (1) Cournot games to be the most profitable along the whole supply chain whereas Stackelberg games and mixed games turn out to be least profitable, and (2) higher the shelf space elasticity, lower the wholesale price of the product; conversely, lower the retail price of the product, greater the shelf space allocated for that product

PERANCANGAN MODEL SISTEM INTELIJENSIA BISNIS UNTUK MENGANALISIS PEMASARAN PRODUK ROTI DI PABRIK ROTI MENGGUNAKAN METODE DATA MINING DAN CUBE

Business intelligence systems participate to deliveran accurate and useful information to decision makers in marketing division of bakeries manufacture. The purpose of this study was to design business intelligence model to analyze the marketing product, design the data mining model,  measure and analyze the marketing process of the product they sell. The methodology of this research wasto analyze system requirements, design unified modeling language, make process extract, transform, and load, designdata warehouse, and data mining that integrated with the on line analytical process cube webbased. The business intelligence model produced was a marketing data mining model and on line analytical process cube. The result from on line analytical process cube was the data warehouse of transaction in R Bakery. In designing the data mining, K-means clustering method was used. The results from data mining k-means clustering were there were 83% cluster 1 and 17% cluster 2. Cluster 1 wasthecategorize for low leftover breads and cluster 2 was the categorize for high leftover breads. The model cube recency, frequency, and monetary and customer lifetime value resulted ranked out of the most amount of sales in R Bakery. Keywords: business intelligence system, data mining, extract transform load, on line analitical process cub

PERANCANGAN MODEL SISTEM INTELIJENSIA BISNIS UNTUK MENGANALISIS PEMASARAN PRODUK ROTI DI PABRIK ROTI MENGGUNAKAN METODE DATA MINING DAN CUBE

Business intelligence systems participate to deliveran accurate and useful information to decision makers in marketing division of bakeries manufacture. The purpose of this study was to design business intelligence model to analyze the marketing product, design the data mining model, measure and analyze the marketing process of the product they sell. The methodology of this research wasto analyze system requirements, design unified modeling language, make process extract, transform, and load, designdata warehouse, and data mining that integrated with the on line analytical process cube webbased. The business intelligence model produced was a marketing data mining model and on line analytical process cube. The result from on line analytical process cube was the data warehouse of transaction in R Bakery. In designing the data mining, K-means clustering method was used. The results from data mining k-means clustering were there were 83% cluster 1 and 17% cluster 2. Cluster 1 wasthecategorize for low leftover breads and cluster 2 was the categorize for high leftover breads. The model cube recency, frequency, and monetary and customer lifetime value resulted ranked out of the most amount of sales in R Bakery