Customer choice prediction based on transfer learning

Choice behaviour prediction is valuable for developing suitable customer segmentation and finding target customers in marketing management. Constructing good choice models for choice behaviour prediction usually requires a sufficient amount of customer data. However, there is only a small amount of data in many marketing applications due to resource constraints. In this paper, we focus on choice behaviour prediction with a small sample size by introducing the idea of transfer learning and present a method that is applicable to choice prediction. The new model called transfer bagging extracts information from similar customers from different areas to improve the performance of the choice model for customers of interest. We illustrate an application of the new model for customer mode choice analysis in the long-distance communication market and compare it with other benchmark methods without information transfer. The results show that the new model can provide significant improvements in choice prediction.     

A MCMC approach for modeling customer lifetime behavior using the COM‐Poisson distribution

One of the major challenges associated with the measurement of customer lifetime value is selecting an appropriate model for predicting customer future transactions. Among such models, the Pareto/negative binomial distribution (Pareto/NBD) is the most prevalent in noncontractual relationships characterized by latent customer defections; ie, defections are not observed by the firm when they happen. However, this model and its applications have some shortcomings. Firstly, a methodological shortcoming is that the Pareto/NBD, like all lifetime transaction models based on statistical distributions, assumes that the number of transactions by a customer follows a Poisson distribution. However, many applications have an empirical distribution that does not fit a Poisson model. Secondly, a computational concern is that the implementation of Pareto/NBD model presents some estimation challenges specifically related to the numerous evaluation of the Gaussian hypergeometric function. Finally, the model provides 4 parameters as output, which is insufficient to link the individual purchasing behavior to socio‐demographic information and to predict the behavior of new customers. In this paper, we model a customer's lifetime transactions using the Conway‐Maxwell‐Poisson distribution, which is a generalization of the Poisson distribution, offering more flexibility and a better fit to real‐world discrete data. To estimate parameters, we propose a Markov chain Monte Carlo algorithm, which is easy to implement. Use of this Bayesian paradigm provides individual customer estimates, which help link purchase behavior to socio‐demographic characteristics and an opportunity to target individual customers.     

A consumer purchasing model with learning and departure behaviour

The purpose of this paper is to extend the model of negative binominal distribution used in consumer purchasing models so as to incorporate the consumer's learning and departure behaviours. The regularity of interpurchase time and its unobserved heterogeneity are also included. Due to these extensions, this model can be used to determine during a given period how many purchases are made by an experienced or an inexperienced customer. This model also allows the determination of the probability that a customer with a given pattern of purchasing behaviour still remains, or has departed, at any time after k≥1 purchases are made. An illustration of the approach is conducted using consumer purchase data for tea. As assessed by comparing results with Theil's U, the integrated model developed gives the best results and shows that learning and departure are important factors which influence consumer's purchase behaviour, especially, when evaluating the behaviour of inexperienced customers.     

基于计划行为理论的顾客与服务商购买意向达成的实证研究

基于计划行为理论研究顾客与服务商之间购买意向达成的影响因素及关系。通过分析顾客的”心理反映”以及商家“责任或义务”的承诺对购买意向达成的影响,提出了顾客与商家购买意向达成的模型及假设;采取情景实验及过程跟踪方法,以服务关系为背景验证模型及假设。研究结果表明:顾客的行为控制认知、价值观/态度、主观规范、知识及经验、商家承诺及传递对顾客的购买意向及达成均呈现不同程度的影响。结论为服务商有侧重地采取措施与顾客达成购买意向及行为具有一定参考作用,并为服务商与顾客之间建立长期稳定的关系奠定基础。     

Predicting online-purchasing behaviour

This empirical study investigates the contribution of different types of predictors to the purchasing behaviour at an online store. We use logit modelling to predict whether or not a purchase is made during the next visit to the website using both forward and backward variable-selection techniques, as well as Furnival and Wilson's global score search algorithm to find the best subset of predictors. We contribute to the literature by using variables from four different categories in predicting online-purchasing behaviour: (1) general clickstream behaviour at the level of the visit, (2) more detailed clickstream information, (3) customer demographics, and (4) historical purchase behaviour. The results show that predictors from all four categories are retained in the final (best subset) solution indicating that clickstream behaviour is important when determining the tendency to buy. We clearly indicate the contribution in predictive power of variables that were never used before in online purchasing studies. Detailed clickstream variables are the most important ones in classifying customers according to their online purchase behaviour. Though our dataset is limited in size, we are able to highlight the advantage of e-commerce retailers of being able to capture an elaborate list of customer information.     

A rational random behaviour model of choice

In this paper, a multinomial-Dirichlet-geometric model of consumer brand choice is developed. This individual-level stochastic choice model is derived as an extension of Theil's theory of rational random behaviour. These behavioural assumptions permit modelling of changes in likelihood of purchase as consumers are confronted with environmental factors whose occurrence and exact nature could not be anticipated at the planning stage of a shopping trip. Moreover, the model allows for uncertainties about future events which might affect actual choice to be built into the choice process alongside a traditional choice model which reflects preferences and/or utilities (and potential uncertainties surrounding them). Empirical results using consumer diary purchase panel data indicate a strong superiority of the model developed compared with previous models which assume stationary preference vectors.     

Mathematical programming models for revenue management under customer choice

We study a network airline revenue management problem with discrete customer choice behavior. We discuss a choice model based on the concept of preference orders, in which customers can be grouped according to a list of options in decreasing order of preference. If a customer’s preferred option is not available, the customer moves to the next choice on the list with some probability. If that option is not available, the customer moves to the third choice on the list with some probability, and so forth until either the customer has no other choice but to leave or his/her request is accepted. Using this choice model as an input, we propose some mathematical programs to determine seat allocations. We also propose a post-optimization heuristic to refine the allocation suggested by the optimization model. Simulation results are presented to illustrate the effectiveness of our method, including comparisons with other models.     

Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models

In the past, several authors have found evidence for the existence of a priority pattern of acquisition for durable goods, as well as for financial services. Its usefulness lies in the fact that if the position of a particular customer in this acquisition sequence is known, one can predict what service will be acquired next by that customer. In this paper, we analyse purchase sequences of financial services to identify cross-buying patterns, which might be used to discover cross-selling opportunities as part of customer relationship management (CRM). Hereby, special attention is paid to transitions, which might encourage bank-only or insurance-only customers to become financial-services customers. We introduce the mixture transition distribution (MTD) model as a parsimonious alternative to the Markov model for use in the analysis of marketing problems. An interesting extension on the MTD model is the MTDg model, which is able to represent situations where the relationship between each lag and the current state differs. We illustrate the MTD and MTDg model on acquisition sequences of customers of a major financial-services company and compare the fit of these models with that of the corresponding Markov model. Our results are in favor of the MTD and MTDg models. Therefore, the MTD as well as the MTDg transition matrices are investigated to reveal cross-buying patterns. The results are valuable to product managers as they clarify the customer flows among product groups. In some cases, the lag-specific transition matrices of the MTDg model give better insight into the acquisition patterns than the general transition matrix of the MTD model.     

Drawing inferences from logit models for panel data

Logit models have been widely used in marketing to predict brand choice and to make inference about the impact of marketing mix variables on these choices. Most researchers have followed the pioneering example of Guadagni and Little, building choice models and drawing inference conditional on the assumption that the logit model is the correct specification for household purchase behaviour. To the extent that logit models fail to adequately describe household purchase behaviour, statistical inferences from them may be flawed. More importantly, marketing decisions based on these models may be incorrect. This research applies White's robust inference method to logit brand choice models. The method does not impose the restrictive assumption that the assumed logit model specification be true. A sandwich estimator of the covariance ‘corrected’ for possible mis‐specification is the basis for inference about logit model parameters. An important feature of this method is that it yields correct standard errors for the marketing mix parameter estimates even if the assumed logit model specification is not correct. Empirical examples include using household panel data sets from three different product categories to estimate logit models of brand choice. The standard errors obtained using traditional methods are compared with those obtained by White's robust method. The findings illustrate that incorrectly assuming the logit model to be true typically yields standard errors which are biased downward by 10–40 per cent. Conditions under which the bias is particularly severe are explored. Under these conditions, the robust approach is recommended. Copyright © 2000 John Wiley & Sons, Ltd.     

A framework for solving network revenue management problems with customer choice behavior

Revenue management is the process of understanding, anticipating and influencing consumer behavior in order to maximize revenue. Network revenue management models attempt to maximize revenue when customers buy bundles of multiple resources. The dependence among the resources in such cases is created by customer demand. Network revenue management can be formulated as a stochastic dynamic programming problem whose exact solution is computationally intractable. Solutions are based on approximations of various types. Customer choice behavior modeling has been gaining increasing attention in the revenue management. A framework for solving network revenue management problems with customer choice behavior is proposed. The modeling and solving framework is composed from three inter-related network structures: basic network model, Petri net, and neural net.     

Incentive mechanism to control customer buy-down behaviour

Airlines have successfully practiced revenue management over the past four decades and enhanced their revenue. Most of the traditional models that are applied assume that customers buying a high-fare class ticket will not purchase a low-fare class ticket even if it is available. This is not a very realistic assumption and has led to revenue leakage due to customers exhibiting buy-down behaviour. This paper aims at devising a suitable incentive mechanism that would incite the customer to reveal his nature. This helps in reducing revenue leakage. We show that the proposed incentive mechanism is profitable to both the buyer and seller and hence ensures the buyers participation in the mechanism.     

Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences

Currently, in order to remain competitive companies are adopting customer centered strategies and consequently customer relationship management is gaining increasing importance. In this context, customer retention deserves particular attention. This paper proposes a model for partial churn detection in the retail grocery sector that includes as a predictor the similarity of the products?? first purchase sequence with churner and non-churner sequences. The sequence of first purchase events is modeled using Markov for discrimination. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of approximately 95,000 new customers is analyzed taken from the data warehouse of a European retailing company. The empirical results reveal the relevance of the inclusion of a products?? sequence likelihood in partial churn prediction models, as well as the supremacy of logistic regression when compared with random forests.     

Stochastic modelling of household behaviour before,during and after a consumer price promotion

A stochastic model is developed to study household behaviour with regard to purchase quantity, brand choice and purchase timing before, during and after a price change and a price promotion such as price-offs and price-cuts. The basic assumption of the model is that price promotion and levels of consumer inventory influence a household's purchase-timing and brand-switching decisions. The model incorporates market segments and brand switching on aggregated demand for the brands by the use of multivariate Markov processes. A transient stochastic model is employed to analyse the dynamic process of household behaviour before, during, and after a price promotion. The interpurchase time that is derived from the model does not require any assumptions and is not independently, identically distributed. An empirical analysis using the Information Resources Incorporated cracker data indicated that (1) price promotion does affect household purchase of the brand and (2) households with larger family size tend to purchase promoted items. We conjecture that households with larger family size take advantage of the lower price of the promoted brands while smaller households tend to remain loyal to one brand.     

Credit scoring for profitability objectives

In consumer credit markets lending decisions are usually represented as a set of classification problems. The objective is to predict the likelihood of customers ending up in one of a finite number of states, such as good/bad payer, responder/non-responder and transactor/non-transactor. Decision rules are then applied on the basis of the resulting model estimates. However, this represents a misspecification of the true objectives of commercial lenders, which are better described in terms of continuous financial measures such as bad debt, revenue and profit contribution. In this paper, an empirical study is undertaken to compare predictive models of continuous financial behaviour with binary models of customer default. The results show models of continuous financial behaviour to outperform classification approaches. They also demonstrate that scoring functions developed to specifically optimize profit contribution, using genetic algorithms, outperform scoring functions derived from optimizing more general functions such as sum of squared error.     

住宅房地产顾客感知价值评估

不同的顾客对同一住宅房地产的感知价值是不同的,购房决策主要取决于对住宅房地产的顾客感知价值.提出了住宅房地产顾客感知价值评估的质量功能配置逆过程法,在已知住宅房地产工程特性的条件下,确定顾客需求值.利用该方法,给出了评估住宅房地产的顾客感知价值的过程和数学模型,案例研究表明,方法能有效评估住宅房地产的顾客感知价值,帮助购房者选择住宅房地产项目.     

Assortment and inventory decisions with multiple quality levels

We consider the assortment and inventory decisions of a retailer under a locational consumer choice model where products can be differentiated both horizontally (e.g., color of a product) and vertically (e.g., quality of a product). The assortment and quantity decisions affect customer choice and, hence, the demand and sales for each product. In this paper, we investigate two different environments where product availability and assortment affect consumer choice and demand in different ways: make-to-order (MTO) and make-to-stock (MTS). In the MTO environment, customers order and purchase their most preferred product; that is, stockouts do not occur. In the MTS model, customers buy their most preferred product if it is in stock or do not buy if it is out of stock. In both environments we find conditions under which it is optimal to carry assortments of only a single quality level. In the MTS case, we show that an assortment of mixed quality levels can be optimal only within a narrow range of parameters.     

Testing robustness in calibration of stochastic volatility models

Many numerical aspects are involved in parameter estimation of stochastic volatility models. We investigate a model for stochastic volatility suggested by Hobson and Rogers [Complete models with stochastic volatility, Mathematical Finance 8 (1998) 27] and we focus on its calibration performance with respect to numerical methodology.In recent financial literature there are many papers dealing with stochastic volatility models and their capability in capturing European option prices; in Figà-Talamanca and Guerra [Towards a coherent volatility pricing model: An empirical comparison, Financial Modelling, Phisyca-Verlag, 2000] a comparison between some of the most significant models is done. The model proposed by Hobson and Rogers seems to describe quite well the dynamics of volatility.In Figà-Talamanca and Guerra [Fitting the smile by a complete model, submitted] a deep investigation of the Hobson and Rogers model was put forward, introducing different ways of parameters' estimation. In this paper we test the robustness of the numerical procedures involved in calibration: the quadrature formula to compute the integral in the definition of some state variables, called offsets, that represent the weight of the historical log-returns, the discretization schemes adopted to solve the stochastic differential equation for volatility and the number of simulations in the Monte Carlo procedure introduced to obtain the option price.The main results can be summarized as follows. The choice of a high order of convergence scheme is not fully justified because the option prices computed via calibration method are not sensitive to the use of a scheme with 2.0 order of convergence or greater. The refining of the approximation rule for the integral, on the contrary, allows to compute option prices that are often closer to market prices. In conclusion, a number of 10 000 simulations seems to be sufficient to compute the option price and a higher number can only slow down the numerical procedure.     

双寡头垄断下全渠道竞争优化策略

引入包含线上购买线下取货(buy online and pick up in store, BOPS)的全渠道策略,在随机和确定性需求下分别构建双寡头Nash均衡博弈模型,得出在线下-线上和BOPS-线上等不同渠道下双寡头企业的最优库存策略。通过分析不同的渠道,进一步探讨了双寡头企业产品定价的差异对最优线下库存的影响。结果表明,BOPS作为一种新的零售模式,通过提供库存的可用性信息,显著降低了顾客承担的缺货风险,既可以增加顾客的访问量,还可以增加交叉收益;同时,零售商是否提供BOPS,将显著影响顾客的渠道选择。