Purchase History Of Customers Research Articles

Modeling the generative mechanism of personalized preferences from latent groups: A hierarchical nonparametric Bayesian method

Personalization is among the most prominent marketing strategies for facing online and offline retailers. The basis for personalization is learning the customer’s personalized preferences, but the sparsity of customer behavioral data makes it difficult to calculate personalized preferences and predict behaviors. Assuming that customer preferences are influenced by the latent group, we propose a Hierarchical Nonparametric Bayesian method (HNB) to model the generative mechanism of personalized preferences from latent groups. The proposed HNB employs a hierarchical and nonparametric structure based on the Dirichlet process, and constructs a combined model to analyze personalized preferences and latent groups. In the context of online shopping, HNB not only mitigates data sparsity with information from the latent groups, but also identifies the meaning of preference in customer purchase history and discovers latent groups accurately. Our experiments also show the number of preferences and latent groups is calculated automatically from the data, understand the generative mechanism of personalized preference from latent groups, and predict personalized purchases based on preferences.

Bayesian dynamic learning and pricing with strategic customers

We consider a seller who repeatedly sells a nondurable product to a single customer whose valuations of the product are drawn from a certain distribution. The seller, who initially does not know the valuation distribution, may use the customer's purchase history to learn and wishes to choose a pricing policy that maximizes her long‐run revenue. Such a problem is at the core of personalized revenue management where the seller can access each customer's individual purchase history and offer personalized prices. In this paper, we study such a learning problem when the customer is aware of the seller's policy and thus may behave strategically when making a purchase decision. By using a Bayesian setting with a binary prior, we first show that a popular policy in this setting—the myopic Bayesian policy (MBP)—may lead to incomplete learning of the seller, namely, the seller may never be able to ascertain the true type of the customer and the regret may grow linearly over time. The failure of the MBP is due to the strategic action taken by the customer. To address the strategic behavior of the customers, we first analyze a Stackelberg game under a two‐period model. We derive the optimal policy of the seller in the two‐period model and show that the regret can be significantly reduced by using the optimal policy rather than the myopic policy. However, such a game is hard to analyze in general. Nevertheless, based on the idea used in the two‐period model, we propose a randomized Bayesian policy (RBP), which updates the posterior belief of the customer in each period with a certain probability, as well as a deterministic Bayesian policy (DBP), in which the seller updates the posterior belief periodically and always defers her update to the next cycle. For both the RBP and DBP, we show that the seller can learn the customer type exponentially fast even if the customer is strategic, and the regret is bounded by a constant. We also propose policies that achieve asymptotically optimal regrets when only a finite number of price changes are allowed.

Customer Purchase History Analysis by Data Mining: A Case of an Online Shopping Store

Customer Purchase History Analysis by Data Mining: A Case of an Online Shopping Store

Investigating online social media users’ behaviors for social commerce recommendations

Online social media create virtual communities and network platforms that people use to create, share, and exchange opinions, views and experiences. With social networks, social commerce not only relies on commerce, but online social media can also promote the sale of goods or services online. Many online operators have begun to use recommendation systems to analyze customer purchase history and identify individual products that customers may purchase. This enables the company to send product information to consumers to attract their attention. In addition, consumers have a higher purchase rate for recommended products based on consumer data. Based on a survey in Taiwan society, this study uses the questionnaire survey method to collect data on a relational database. This study investigates Taiwan online social media users’ behaviors using data mining methods, including clustering analysis and association rules. Clustering analysis is to investigate possible profiles of users and association rules are to find knowledge patterns and rules of user profiles, online social media usage motivation/preferences and social commerce behavior in order to generate social commerce recommendations in terms of social technology development in the modern society.

Potential Customer Prediction

Major industries today are dealing with large amount of data even small shops are no oblivion for the huge pile of customer data. To gain profit in competitive marketing is imperative that the useful information shall be extracted out of this data. The processing of this huge pile of data becomes monotonous task and the different types of software and algorithms are developed to process and acquire result out of this data. This project deals with same kind of problem of dealing with data. Taking customer purchase history as an input our system using "Apriori Algoritm" classifies these customers as potential and non-potential customers. Customers who are potential are more likely to buy more from the store and help enhance business.

Evaluating the effect of individually-targeted food incentives on grocery purchases: The smart cart study protocol for a randomized controlled cross-over trial

Background/AimsHealth stakeholders are interested in the promise of healthy food incentives to improve dietary quality. The Smart Cart Study tested whether targeting healthful food incentives based on customer preferences and purchase history was effective for improving grocery purchase quality. DesignRandomized controlled crossover design of 224 adults who shopped at an independent supermarket for ≥50% of their groceries, participated in the store's loyalty program, and completed validated diet and sociodemographic/behavioral questionnaires. Participants were randomized using 1:1 blocked randomization; all participants received a 5% discount on their purchases with their loyalty card. For the first 13-weeks, the intervention group received individually-targeted weekly coupons (valued up to $10) with brief nutrition education to improve grocery purchase quality. The study team developed healthy food coupons, and the study algorithm allocated targeted coupons to participants' loyalty cards using purchase history, dietary preferences/allergies, and baseline diet quality. Control participants received weekly untargeted nutrition education and occasional generic coupons. Following a 2–4 week washout period, the two groups crossed over. The primary study outcomes were purchases of targeted products and grocery purchase quality measured using the validated Grocery Purchase Quality Index-2016; the study was powered to detect a minimum 3% difference in purchase quality. ConclusionsThe Smart Cart Study tested a novel application of automated individually-targeted marketing using customer purchase history, dietary quality, and preferences to identify and deliver targeted incentives to improve grocery purchase quality. Future research could scale this program through collaboration between multiple stakeholders, including supermarkets, workplace wellness initiatives and insurance companies.

Online Decision Making with High-Dimensional Covariates

Decision-makers increasingly have access to rich customer-specific data, providing an opportunity to make better, personalized service decisions. For example, in healthcare, doctors can personalize interventions based on a patient’s clinical history; in marketing, companies can target ads based on customer purchase history. However, the increased variety of potentially relevant customer data implies that an individual’s covariates may be high dimensional, which, in turn, poses statistical challenges for learning personalized decision-making policies. In “Online Decision-Making with High-Dimensional Covariates,” H. Bastani and M. Bayati introduce the LASSO Bandit, an adaptive decision-making algorithm that efficiently leverages high-dimensional user covariates by learning sparse models of decision rewards. The authors illustrate the practical relevance of such an approach by evaluating it against a personalized medication dosing problem, finding that the LASSO Bandit outperforms existing bandit methods and physicians in correctly dosing a majority of patients.

Model-Based Purchase Predictions for Large Assortments

An accurate prediction of what a customer will purchase next is of paramount importance to successful online retailing. In practice, customer purchase history data is readily available to make such predictions, sometimes complemented with customer characteristics. Given the large product assortments maintained by online retailers, scalability of the prediction method is just as important as its accuracy. We study two classes of models that use such data to predict what a customer will buy next, i.e., a novel approach that uses latent Dirichlet allocation (LDA), and mixtures of Dirichlet-Multinomials (MDM). A key benefit of a model-based approach is the potential to accommodate observed customer heterogeneity through the inclusion of predictor variables. We show that LDA can be extended in this direction while retaining its scalability. We apply the models to purchase data from an online retailer and contrast their predictive performance with that of a collaborative filter and a discrete choice model. Both LDA and MDM outperform the other methods. Moreover, LDA attains performance similar to that of MDM while being far more scalable, rendering it a promising approach to purchase prediction in large product assortments. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0985 .

Bayesian Dynamic Learning and Pricing with Strategic Customers

We consider a seller who repeatedly sells a nondurable product to a single customer whose valuations of the product are drawn from a certain distribution. The seller, who initially does not know the valuation distribution, may use the customer's purchase history to learn, and wishes to choose a pricing policy that maximizes her long-run revenue. Such a problem is at the core of personalized revenue management where the seller can access each customer's individual purchase history and offer personalized prices.In this paper, we study such a learning problem when the customer is aware of the seller's policy, and thus may behave strategically when making a purchase decision. By using a Bayesian setting with a binary prior, we first show that a naive myopic Bayesian policy (MBP) by the seller may lead to incomplete learning -- the seller may never be able to ascertain the true type of the customer and the regret may grow linearly in time. The failure of the MBP is due to the strategic action taken by the customer. To resolve this issue, we propose a randomized Bayesian policy (RBP), which updates the posterior belief of the customer in each period with a certain probability. We show that the seller can learn the customer type exponentially fast with the RBP even if the customer is strategic, and the regret is bounded by a constant. We also propose policies that achieve asymptotically optimal regrets when only a finite number of price changes is allowed.

Product Recommendations Based on Latent Purchase Motivations

Being able to accurately predict what a customer will purchase next is of paramount importance to successful online retailing. In practice, customer purchase history data is readily available to make such predictions, sometimes complemented with customer characteristics. Given the large assortments maintained by online retailers, scalability of the prediction method is just as important as its accuracy. We study two classes of models that use such data to predict what a customer will buy next: A novel approach that uses latent Dirichlet allocation (LDA), and mixtures of Dirichlet-Multinomials (MDM). A key benefit of a model-based approach is the potential to accommodate observed customer heterogeneity through the inclusion of predictor variables. We show that LDA can be extended in this direction while retaining its scalability. We apply the models to purchase data from an online retailer and contrast their predictive performance with that of a collaborative filter and a discrete choice model. Both LDA and MDM outperform the other methods. Moreover, LDA attains performance similar to that of MDM while being far more scalable, rendering it a promising approach to purchase prediction in large assortments.

Recommender Systems as a Mobile Marketing Service

Recommender systems (RSs) have become a familiar artifact in cyberspace as a vehicle for increasing revenues while deepening customer loyalty and satisfaction. Typically RS are developed in house by companies with a large product line and customer base. However, the structure of RS is often straightforward, and effective systems can be developed at relatively low cost, and thus offered as a marketing service. We discuss the anatomy of a specific recommender system designed for a telecommunication carrier which uses predictive analytics in the form of collaborative filtering techniques to recommend products to users. Collaborative filtering (CF) is based upon the premise that users who have purchased a particular product will have similar preferences to other users who also purchased the product. We discuss and compare three versions of a CF-based recommender system based upon customer purchase history, customer browsing history, and user segments respectively. The results in terms of increased sales suggest that RS offer substantial value as a mobile marketing service. We suggest ways in which RS can be generalized into RS generators for more rapid development and deployment. We then invert the RS perspective from product-centric to user-centric and suggest how this would work in customer targeting for mobile advertising campaigns. We conclude that RS in the largest sense are heavily model-feedback in nature and require increasingly sophisticated automated modeling and predictive analytics capabilities layered on a scalable big data infrastructure.

2相3元データ分析に際してのINDSCAL，INDCLUS，及びPREFMAP解併用の有効性—ケース研究

When we use multidimensional scaling (MDS) to analyze a dataset in which the magnitude of the values considerably differ (vary) among objects, for example, customer purchase history data, it often occurs that the only points of objects with large values scatter well in the configuration, but the others with small values do not scatter well. It is said that joint uses of MDS and cluster analysis is often desirable. In this study, we showed that the joint use of MDS and cluster analysis also worked well in the above case, through the analysis of two-mode three-way proximity data such as several variance-covariance matrices of objects obtained from each condition. We also used an external analysis with MDS to show the information about each of the two modes on the same configuration simultaneously, in order to easily observe the global structure of data through the configuration.

Recommendation Systems with Purchase Data

Abstract Many firms use decision tools called “automatic recommendation systems” that attempt to analyze a customer's purchase history and identify products the customer may buy if the firm were to bring these products to the customer's attention. Much of the research in the literature today attempts to recommend products that have a high probability of purchase (conditional on the customer's history). However, the author posits that the recommendation decision should be based not on purchase probabilities but rather on the sensitivity of purchase probabilities to the recommendation action. This article attempts to model carefully the role of firms' recommendation actions in modifying customers' buying behaviors relative to what the customers would do without such a recommendation intervention. The author proposes a simple consumer behavior model that accommodates a transparent role for a firm's recommendation actions. The model is expressed in econometric terms so that it can be estimated with available ...

I'm Here, But Maybe Not for Long: Using Complaints to Predict and Value Customers

The extant literature has related Customer Lifetime Value [CLV] to the customer's purchase history, but has paid little attention to other non-transactional customer-firm interactions like complaints. This paper makes three contributions. First, we build an easy-to-implement model that incorporates complaints into customer base analysis. Second, applying this model to more than two years of individual data from a major U.S. catalog retailer, we demonstrate that the occurrence and timing of customer complaints offer predictive value beyond the occurrence and timing of purchases. Lastly, we assess the link between complaints, purchases and CLV, which allows us to investigate the conditions under which observing a customer complaint increases or decreases CLV. We argue that a customer complaint may provide two opposing signals. The negative signal concerns potential defection. The positive signal is that the customer is still active and involved with the firm.

CUSTOMER‐BASED MEASURES OF INVENTORY AVAILABILITY

Traditional measures of inventory availability, such as fill rates, focus on the availability of individual items, not availability to individual customers. This is inadequate because managers today need to be customer oriented and focus on the service delivered to specific customers. This is especially true of key customers. The purpose of this research is to introduce customer‐based measures of inventory availability. Four such measures are proposed. Each is designed to enable managers to select the level of inventory availability to be offered to a specific customer. These measures use a customer's purchase history to estimate shortages in future purchases. The shortage frequency may then be managed.

A Model for Observation, Structural, and Household Heterogeneity in Panel Data

Standard methods of understanding customer behavior in marketing allow for differences in sensitivity across consumers, but often assume that the sensitivity of a particular individual is fixed through time. In many situations, this assumption may not be valid. Both the importance of variables, and the manner that they are combined to form an overall measure of value for an offer, can change. In this paper we propose an approach of modeling a customer's purchase history that allows identification of when these aspects of customer behavior are likely to change. This information is useful, for example, in planning when particular customers will be most likely to respond to an offer. Our approach nests common methods of dealing with individual differences, and allows for the introduction of covariates associated with changes in customer behavior. We illustrate our model with data from a national sample of credit card usage and adoption.