We are proudly a Ukrainian website. Our country was attacked by Russian Armed Forces on Feb. 24, 2022.
You can support the Ukrainian Army by following the link: https://u24.gov.ua/.
Even the smallest donation is hugely appreciated!
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Purchase Probability Prediction".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "Purchase Probability Prediction":
1
Parfenov, P. A., A. A. Timofeeva, G. B. Sologub e A. S. Alekseychuk. "Prediction the Probability of Purchases Recommended Items". Моделирование и анализ данных 10, n.º 4 (2020): 17–30. http://dx.doi.org/10.17759/mda.2020100402.
This paper discusses various methods for improving recommendation systems. A comparative analysis of two models for solving classification problems is performed: random forest and CatBoostClassifier. The research was performed on the data of the purchase history of Ozon customers. Standard methods that are often used in recommendation systems were used. We implemented collaborative filtering methods, cosine similarity of products from customer views per site visit, and similarity of text data. To evaluate the results, we used special metrics that evaluate the quality of predictions of the first k objects from the recommendations: Mean average precision (map@K) and Recall at K (recall@k). When generating additional features based on various methods that reveal the similarity of objects, an increase in the quality of model forecasts is noted. The CatBoostClassifier model showed the best results.
2
Fu, Zheng, e Lan Feng Zhou. "A Purchase Prediction Based on Collaborative Filtering Algorithm". Advanced Materials Research 989-994 (julho de 2014): 2241–44. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2241.
For a more accurate prediction of the probability of consumers to purchase a commodity, this paper build a users’ behavior model based on correlation analysis with apriori algorithm. The model is built by learning from users’ history data and behaviors’ at present, an experimental result demonstrates that this model can effectively predict consumer buying behavior, and it is better than some traditional methods.
3
Lv, Yihang, e Qin Liu. "Value perception impact and countermeasures analysis of new energy vehicle purchase behavior based on consumer level user review big data mining". MATEC Web of Conferences 336 (2021): 09030. http://dx.doi.org/10.1051/matecconf/202133609030.
The development of new energy vehicles is inseparable from the drive of consumers. Therefore, to explore the influencing factors of purchase behavior from the consumer's personal level is helpful for businesses to adopt corresponding sales strategies and the government to adopt relevant policies. Based on the individual level of consumers, this paper constructs a new energy vehicle purchase behavior prediction model from the review text, and explores the predictive effect of consumer personal factors on the purchase behavior of new energy vehicles. First of all, this paper proposes a quantitative method of consumer individual level factors, which combines word-of-mouth reviews with statistics. In this method, word2vec is used to train word vectors in word-of-mouth corpus to mine initial keywords, and core keywords are selected through statistical correlation analysis. Secondly, based on the core keywords of consumers' personal level, the gbdt model is constructed to predict the purchase behavior of new energy vehicles. The results show that the probability of correctly predicting consumers' purchase behavior is more than 72%.
4
Mau, Stefan, Irena Pletikosa e Joël Wagner. "Forecasting the next likely purchase events of insurance customers". International Journal of Bank Marketing 36, n.º 6 (3 de setembro de 2018): 1125–44. http://dx.doi.org/10.1108/ijbm-11-2016-0180.
Purpose The purpose of this paper is to demonstrate the value of enriched customer data for analytical customer relationship management (CRM) in the insurance sector. In this study, online quotes from an insurer’s website are evaluated in terms of serving as a trigger event to predict churn, retention, and cross-selling. Design/methodology/approach For this purpose, the records of online quotes from a Swiss insurer are linked to records of existing customers from 2012 to 2015. Based on the data from automobile and home insurance policyholders, random forest prediction models for classification are fitted. Findings Enhancing traditional customer data with such additional information substantially boosts the accuracy for predicting future purchases. The models identify customers who have a high probability of adjusting their insurance coverage. Research limitations/implications The findings of the study imply that enriching traditional customer data with online quotes yields a valuable approach to predicting purchase behavior. Moreover, the quote data provide supplementary features that contribute to improving prediction performance. Practical implications This study highlights the importance of selecting the relevant data sources to target the right customers at the right time and to thus benefit from analytical CRM practices. Originality/value This paper is one of the first to investigate the potential value of data-rich environments for insurers and their customers. It provides insights on how to identify relevant customers for ensuing marketing activities efficiently and thus avoiding irrelevant offers. Hence, the study creates value for insurers as well as customers.
5
Sugawara, Shinya, Tianyi Wu e Kenji Yamanishi. "A basket two-part model to analyze medical expenditure on interdependent multiple sectors". Statistical Methods in Medical Research 27, n.º 5 (1 de setembro de 2016): 1585–600. http://dx.doi.org/10.1177/0962280216665642.
This study proposes a novel statistical methodology to analyze expenditure on multiple medical sectors using consumer data. Conventionally, medical expenditure has been analyzed by two-part models, which separately consider purchase decision and amount of expenditure. We extend the traditional two-part models by adding the step of basket analysis for dimension reduction. This new step enables us to analyze complicated interdependence between multiple sectors without an identification problem. As an empirical application for the proposed method, we analyze data of 13 medical sectors from the Medical Expenditure Panel Survey. In comparison with the results of previous studies that analyzed the multiple sector independently, our method provides more detailed implications of the impacts of individual socioeconomic status on the composition of joint purchases from multiple medical sectors; our method has a better prediction performance.
6
Suh, Euiho, Seungjae Lim, Hyunseok Hwang e Suyeon Kim. "A prediction model for the purchase probability of anonymous customers to support real time web marketing: a case study". Expert Systems with Applications 27, n.º 2 (agosto de 2004): 245–55. http://dx.doi.org/10.1016/j.eswa.2004.01.008.
Shah, Ismail, e Francesco Lisi. "Forecasting of electricity price through a functional prediction of sale and purchase curves". Journal of Forecasting 39, n.º 2 (5 de setembro de 2019): 242–59. http://dx.doi.org/10.1002/for.2624.
Wang, Fei, Yu Yuan e Liangdong Lu. "Dynamical prediction model of consumers’ purchase intentions regarding anti-smog products during smog risk: Taking the information flow perspective". Physica A: Statistical Mechanics and its Applications 563 (fevereiro de 2021): 125427. http://dx.doi.org/10.1016/j.physa.2020.125427.
Guerrieri, Mirko, Marco Fedrizzi, Francesca Antonucci, Federico Pallottino, Giulio Sperandio, Mauro Pagano, Simone Figorilli, Paolo Menesatti e Corrado Costa. "An innovative multivariate tool for fuel consumption and costs estimation of agricultural operations". Spanish Journal of Agricultural Research 14, n.º 4 (2 de dezembro de 2016): e0209. http://dx.doi.org/10.5424/sjar/2016144-9490.
The estimation of operating costs of agricultural and forestry machineries is a key factor in both planning agricultural policies and farm management. Few works have tried to estimate operating costs and the produced models are normally based on deterministic approaches. Conversely, in the statistical model randomness is present and variable states are not described by unique values, but rather by probability distributions. In this study, for the first time, a multivariate statistical model based on Partial Least Squares (PLS) was adopted to predict the fuel consumption and costs of six agricultural operations such as: ploughing, harrowing, fertilization, sowing, weed control and shredding. The prediction was conducted on two steps: first of all few initial selected parameters (time per surface-area unit, maximum engine power, purchase price of the tractor and purchase price of the operating machinery) were used to estimate the fuel consumption; then the predicted fuel consumption together with the initial parameters were used to estimate the operational costs. Since the obtained models were based on an input dataset very heterogeneous, these resulted to be extremely efficient and so generalizable and robust. In details the results show prediction values in the test with r always ≥ 0.91. Thus, the approach may results extremely useful for both farmers (in terms of economic advantages) and at institutional level (representing an innovative and efficient tool for planning future Rural Development Programmes and the Common Agricultural Policy). In light of these advantages the proposed approach may as well be implemented on a web platform and made available to all the stakeholders.
10
Bemmaor, Albert C. "Predicting Behavior from Intention-to-Buy Measures: The Parametric Case". Journal of Marketing Research 32, n.º 2 (maio de 1995): 176–91. http://dx.doi.org/10.1177/002224379503200205.
The author develops a probabilistic model that converts stated purchase intents into purchase probabilities. The model allows heterogeneity between nonintenders and intenders with respect to their probability to switch to a new “true” purchase intent after the survey, thereby capturing the typical discrepancy between overall mean purchase intent and subsequent proportion of buyers (bias). When the probability to switch of intenders is larger (smaller) than that of nonintenders, the overall mean purchase intent overestimates (underestimates) the proportion of buyers. As special cases, the author derives upper and lower bounds on proportions of buyers from purchase intents data and shows the consistency of those bounds with observed behavior, except in predictable cases such as new products and business markets. However, a straightforward modification of the model deals with new product purchase forecasts.
Teses / dissertações sobre o assunto "Purchase Probability Prediction":
1
Alstermark, Olivia, e Evangelina Stolt. "Purchase Probability Prediction : Predicting likelihood of a new customer returning for a second purchase using machine learning methods". Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184831.
When a company evaluates a customer for being a potential prospect, one of the key questions to answer is whether the customer will generate profit in the long run. A possible step to answer this question is to predict the likelihood of the customer returning to the company again after the initial purchase. The aim of this master thesis is to investigate the possibility of using machine learning techniques to predict the likelihood of a new customer returning for a second purchase within a certain time frame. To investigate to what degree machine learning techniques can be used to predict probability of return, a number of di↵erent model setups of Logistic Lasso, Support Vector Machine and Extreme Gradient Boosting are tested. Model development is performed to ensure well-calibrated probability predictions and to possibly overcome the diculty followed from an imbalanced ratio of returning and non-returning customers. Throughout the thesis work, a number of actions are taken in order to account for data protection. One such action is to add noise to the response feature, ensuring that the true fraction of returning and non-returning customers cannot be derived. To further guarantee data protection, axes values of evaluation plots are removed and evaluation metrics are scaled. Nevertheless, it is perfectly possible to select the superior model out of all investigated models. The results obtained show that the best performing model is a Platt calibrated Extreme Gradient Boosting model, which has much higher performance than the other models with regards to considered evaluation metrics, while also providing predicted probabilities of high quality. Further, the results indicate that the setups investigated to account for imbalanced data do not improve model performance. The main con- clusion is that it is possible to obtain probability predictions of high quality for new customers returning to a company for a second purchase within a certain time frame, using machine learning techniques. This provides a powerful tool for a company when evaluating potential prospects.
Trabalhos de conferências sobre o assunto "Purchase Probability Prediction":
1
Suyama, Noriyuki. "Forecasting customer purchase with maximization of prediction probability". In the 3rd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3361785.3361812.
Sharma, Archika, e M. Omair Shafiq. "Predicting purchase probability of retail items using an ensemble learning approach and historical data". In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00118.
Krockow, Wolfram. "Predicting Gas Turbine Reliability to Assess Risks for Purchased Power Agreements". In ASME 1996 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-gt-266.
Availability and Reliability have become a major concern for operating companies; especially for those where unscheduled outages due to purchased power agreements may be highly penalized. On the other hand, high revenues by the sale of electricity can be achieved if the power generating set performs according to its demand. Economic calculations demonstrate that the bottom line between profit and loss lies within the high 90th% of availability. This paper summarizes the results of an analysis based on the regulations in France with its so-called EJP days (Effacement Jour Pointe). Any loss of highly penalized EJP days is quantified based on known RAM (Reliability, Availability, Maintainability) values of the equipment, as these are the Mean Time Between Failures MTBF’s and the Mean Times To Repair MTTR’s of the overall equipment as well as its subassemblies. In formulating the demand, the past 12 years of assigned EJP days by the Electricité de France, EdF, was analyzed to derive probability ratings of seasonal distributions, weekly distributions and day block distributions. The mathematics of this simulation model are based on well proven statistical procedures (i.e. the Monte Carlo Method). By performing parameter variations, the model can also quantitatively predict how much the Mean Time Between Failures of a heavy duty gas turbine must usually be better for this application when compared to an aeroderivative gas turbine. This is because it normally takes longer to repair or replace a heavy duty gas turbine versus an aeroderivative unit in case of a major unscheduled or forced outage.
4
Wang, Mingxian, e Wei Chen. "Predicting Consumer Choice Set Using Product Association Network and Data Analytics". In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12425.
Although discrete choice analysis has been shown to be useful for modeling consumer preferences and choice behaviors in the field of engineering design, information of choice set composition is often not available in majority of the collected consumer purchase data. When a large set of choice alternatives exist for a product, such as automotive vehicles, randomly choosing a small set of product alternatives to form a choice set for each individual consumer will result in misleading choice modeling results. In this work, we propose a data-analytics approach to mine existing data of choice sets and predict the choice set for each individual customer in a new choice modeling scenario where the choice set information is lacking. The proposed data-analytics approach integrates product association analysis, network analysis, consumer segmentation, and predictive analytics. Using the J.D. Power vehicle survey as the existing choice set data, we demonstrate that the association network approach is capable of recognizing and expressively summarizing meaningful product relations in choice sets. Our method accounts for consumer heterogeneity using the stochastic generation algorithm where the probability of selecting an alternative into a choice set integrates the information of customer profile clusters and products chosen frequencies. By comparing multiple multinomial logit models using different choice set compositions, we show that the choice model estimates are sensitive to the choice set compositions and our proposed method leads to improved modeling results. Our method also provides insights into market segmentation that can guide engineering design decisions.
