{"id":8693,"date":"2023-07-21T11:27:14","date_gmt":"2023-07-21T11:27:14","guid":{"rendered":"https:\/\/komorebi.ai\/?page_id=8693"},"modified":"2024-02-13T11:58:10","modified_gmt":"2024-02-13T11:58:10","slug":"assessing-the-effect-of-advertising","status":"publish","type":"page","link":"https:\/\/komorebi.ai\/en\/assessing-the-effect-of-advertising\/","title":{"rendered":"Assessing the effect of advertising expenditures upon sales: A Bayesian structural time series model"},"content":{"rendered":"
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Assessing the effect of advertising expenditures upon sales. A Bayesian structural time series model<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Applied Stochastic Models in Business & Industry, 2019<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The study introduces a robust application of the Nerlove-Arrow model via a Bayesian structural time series model to predict<\/strong> the weekly sales<\/strong> of a international fast-food chain of restaurant<\/strong><\/a> as a function of its advertising expenses. \n

\nThanks to its flexibility and modularity, this model can adapt to various markets or scenarios, and its Bayesian aspect allows for the integration of prior information which can aid managerial decision-making regarding advertising budget allocation across different time periods and channels<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Accurate sales predictions based on advertising spend benefiting various industries in strategic advertising expenditure, budget allocation, and marketing campaign planning.<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Where does it apply?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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It can be used by companies in the retail industry, online marketplaces,<\/strong> hospitality, entertainment, and consumer goods<\/strong> industries among others. \n

\nParticularly in marketing and advertising departments, the model can aid in decision making for budget allocation,<\/strong> choosing effective advertising channels, and planning marketing campaigns.<\/strong> It will also be beneficial for digital marketing agencies to improve their services for clients.<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Why does it matter?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This data-driven model for managing advertising investments presents a novel approach to shaping marketing strategies. \n

\nLeveraging the firm's advertising data and the Nerlove-Arrow model via a Bayesian structural time series, it provides accurate predictions<\/strong> of economic indicators, such as global sales,<\/strong> while considering external<\/strong> environmental factors.<\/strong> Its modularity and flexibility make it easily adaptable to other business scenarios. \n

\nFurthermore, the model assists in variable selection and includes prior information, facilitating insight<\/strong> into effective advertising channels<\/strong> and supporting decision-making on advertising budget allocation. \n

\nFuture extensions involve designing a more detailed model to account for channel interactions, long-term effects, individual restaurant performance, and potential impacts from promotions. As such, this model is significant for firms that aim for strategic, efficient advertising expenditure and targeting.<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Assessing the effect of advertising expenditures upon sales. A Bayesian structural time series model<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Journal of the American Statistical Association, 2019<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tVictor Gallego <\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tChief Research Officer<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tAdvertising, Sales Forecasting, Bayesian Structural Time Series<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Other publications<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Adversarial Machine Learning: Bayesian Perspectives\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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AI in drug development: a multidisciplinary perspective\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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How do tuna schools associate to dFADs? A study using echo-sounder buoys to identify global patterns<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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A Bayesian approach to enhance defense against security threats in Machine Learning systems, applicable across cybersecurity, unsupervised learning and natural language processing.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The review explores the use of AI, particularly Bayesian approaches, in speeding up and reducing the cost of drug development across all stages.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Machine Learning to analyze tuna behavior around Fish Aggregating Devices, providing insights to enhance sustainable fishing, marine biology research and conservation policies.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tGame Theory, Cybersecurity<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tDrug Development, Decision Support<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Learn More >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Learn More >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Learn More >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Evaluaci\u00f3n del efecto de la inversi\u00f3n en publicidad sobre las ventas Applied Stochastic Models in Business & Industry, 2019 Resumen El estudio presenta una aplicaci\u00f3n del modelo de Nerlove-Arrow mediante un modelo bayesiano de series temporales estructurales para predecir las ventas semanales de una cadena internacional de restaurantes de comida r\u00e1pida en funci\u00f3n de sus […]<\/p>","protected":false},"author":1,"featured_media":8738,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"yoast_head":"\nAssessing the effect of advertising expenditures upon sales: A Bayesian structural time series model - Komorebi AI<\/title>\n<meta name=\"description\" content=\"Accurate sales predictions from ad spend aid in strategic advertising, budget allocation, and campaign planning in various industries.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/komorebi.ai\/en\/assessing-the-effect-of-advertising\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Assessing the effect of advertising expenditures upon sales: A Bayesian structural time series model - 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