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Book part
Publication date: 10 March 2021

Niladri Syam and Rajeeve Kaul

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Machine Learning and Artificial Intelligence in Marketing and Sales
Type: Book
ISBN: 978-1-80043-881-1

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Article
Publication date: 1 February 2024

Ismael Gómez-Talal, Lydia González-Serrano, José Luis Rojo-Álvarez and Pilar Talón-Ballestero

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into…

608

Abstract

Purpose

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.

Design/methodology/approach

A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.

Findings

The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.

Research limitations/implications

This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.

Originality/value

The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

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Article
Publication date: 1 January 2006

M.P. Martínez‐Ruiz, A. Mollá‐Descals, M.A. Gómez‐Borja and J.L. Rojo‐Álvarez

To analyze the impact of temporary retail price discount on a consumer goods product category using semiparametric regression and considering different promotional price discount…

2481

Abstract

Purpose

To analyze the impact of temporary retail price discount on a consumer goods product category using semiparametric regression and considering different promotional price discount characteristics as well as brand characteristics.

Design/methodology/approach

A semiparametric regression model using Support Vector Machines, which aim to evaluate retailers' decisions about temporary price discounts, has been developed. The model is derived from the analysis of historical sales data, which provide precise evaluation of previous temporary price discounts periods. The model is also consistent with ample empirical evidence showing that historical retail sales data can be used to evaluate the impact of past promotions.

Findings

Provides an estimation of the shape of the deal effect curve, indicating which temporary price discounts are more effective to increase sales and showing the existence of different threshold and saturation levels. Confirms that promotional price discounts accelerate sales especially during week ends. Evidences that promoting high‐priced (high‐quality) brands has a stronger impact on sales of low‐priced (low‐quality) brands than the reverse and that cross‐price effects are stronger on the sales of brands with similar prices. Suggests the convenience of the use of the proposed semiparametric methodology to the study of the promotional effects considered.

Research limitations/implications

It is not possible to generalize the modelled shapes of the deal effect curves. There is no information available on feature advertising nor displays. It is important to determine the generalizability of these results to the study of additional promotional effects. It would also be interesting to assume that the retailer's deal policy is exogenous.

Originality/value

Provides a relevant tool to assess the set of price promotional periods by the grocery retailer. With a more precise and accurate knowledge about the performance of past temporary price cuts, retailers can implement more effective promotional periods.

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Journal of Product & Brand Management, vol. 15 no. 1
Type: Research Article
ISSN: 1061-0421

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Book part
Publication date: 30 September 2020

Madhulika Bhatia, Shubham Chaudhary, Madhurima Hooda and Bhuvanesh Unhelkar

This chapter discusses about the advancement in the field of telemedicine and how often the general public are using the services that are provide by the telehealth and…

Abstract

This chapter discusses about the advancement in the field of telemedicine and how often the general public are using the services that are provide by the telehealth and telemedicine market. This chapter starts with the introduction of the medicine in the world, which were the earliest medical practice and how all that thing leads to the today’s telehealth market. This chapter also describes the models that are being used in today’s world, and how these models as implemented and how telemedicine services are implemented more efficiently. Telemedicine and telehealth market is growing day by day and a lot people are getting to know about it, but there is still some section of the society, specially the lower middle class and the people in the rural areas that do not have any access or knowledge about the concept of telehealth services.

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Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

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Article
Publication date: 4 July 2023

Zicheng Zhang, Xinyue Lin, Shaonan Shan and Zhaokai Yin

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore…

140

Abstract

Purpose

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

Design/methodology/approach

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

Findings

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

Originality/value

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

Details

Library Hi Tech, vol. 42 no. 6
Type: Research Article
ISSN: 0737-8831

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Article
Publication date: 12 September 2016

Anup Kumar, Amit Adlakha and Kampan Mukherjee

The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates…

2152

Abstract

Purpose

The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization.

Design/methodology/approach

Time series data relating to sales has been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products and selection of suppliers. A hybrid model has been proposed and explained with a hypothetical case, which considerably impacts the sales promotion and intelligent pricing decisions.

Findings

A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic. The model imitates sales promotion and price discounting strategy. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact.

Research limitations/implications

There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories.

Originality/value

The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

Details

Industrial Management & Data Systems, vol. 116 no. 8
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 29 February 2008

Óscar González‐Benito, María Pilar Martínez‐Ruiz and Alejandro Mollá‐Descals

The purpose of this paper is to incorporate explicitly consumer heterogeneity into market response models estimated with store‐level scanner‐data.

1025

Abstract

Purpose

The purpose of this paper is to incorporate explicitly consumer heterogeneity into market response models estimated with store‐level scanner‐data.

Design/methodology/approach

Latent structures in market response to a product category using aggregated scanner data registered by a supermarket are identified. Specifically, latent consumer segments with diverse preferences towards brands and different responses to marketing stimuli from data consisting of daily marketing actions (i.e. price, promotions, advertising, etc.) and sales of competing brands are identified.

Findings

The existence of different latent segments with diverse preferences and response patterns to marketing stimuli were detected. More specifically, the fit of the statistical analysis for the different model possibilities made it possible to identify four market segments. It was also found that the intrinsic brand attractiveness as a measure of consumer brand preference is different between segments. Finally, the price sensitivity is also different between segments.

Research limitations/implications

The time cost necessary to obtain the parameter estimates is too high, which is usual in the models estimated with iterative EM algorithms.

Practical implications

This work deepens one's knowledge of the identification and selection of latent market structures, specifically latent segments with different purchase patterns and behaviours. The possibility of developing the analysis with aggregated data at the store level increases the potential utility for academics and marketing managers.

Originality/value

Although most applications use weekly data, this proposal models daily fluctuations in sales – as a result, making it possible to obtain consumer segments based on daily changes.

Details

Journal of Product & Brand Management, vol. 17 no. 1
Type: Research Article
ISSN: 1061-0421

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Article
Publication date: 29 January 2020

Zhen Yang, Kangning Song, Xingsheng Gu, Zhi Wang and Xiaoyi Liang

Nitrogen oxides (NOx) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NOx hazards. To clarify, NO…

82

Abstract

Purpose

Nitrogen oxides (NOx) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NOx hazards. To clarify, NO removal process for pitch-based spherical-activated carbons (PSACs), an online prediction and optimization technique in real-time based on support vector machine algorithm in regression (support vector regression [SVR]) is discussed. The purpose of this paper is to develop a predictor and optimizer system on selective catalytic reduction of NO (SCRN) using experimental data and data-driven SVR intelligence methods.

Design/methodology/approach

Predictor and optimizer using developed SVR have been proposed. To modify the training efficiency of SVR, the authors especially customize batch normalization and k-fold cross-validation techniques according to the unique characteristics of PSACs model.

Findings

The results present that SVR provides a property regression model since it can linkage linear and non-linear process and property relationships in few experimental data sets. Also, the integrated normalization and k-fold cross-validation show a satisfying improvement and results for SVR optimization. The predicted results of predictor and optimizer in single and double factor systems are in excellent agreement with the experimental data.

Originality/value

SCRN-PO for predicting and optimization SCRN problems is developed by data-driven methods. The outperformed SCRN-PO system is used to predict multiple-factors property parameters and obtain optimum technological parameters in real-time. Also, experiment duration is greatly shortened.

Details

Engineering Computations, vol. 37 no. 5
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 14 April 2014

Alexander C. Larson, Rita L. Reicher and David William Johnsen

– The purpose of this research is to test for price threshold effects in the demand for high-involvement services for small businesses.

1125

Abstract

Purpose

The purpose of this research is to test for price threshold effects in the demand for high-involvement services for small businesses.

Design/methodology/approach

The authors use a stated preference choice-based conjoint study of small business telecommunications demand. Using survey data, individual-level parameter estimates for a demand model are achieved via the Hierarchical Bayes method of estimation.

Findings

For demand for small business telecommunications services, the authors find very strong positive impacts of nine-ending and zero-ending prices on the demand for a common bundle of telecommunications services (wired telephone service, broadband internet, and cellular telephone service), even at prices so high a shift in the left-most digit does not occur.

Practical implications

The advertising, brand, or product manager or statistician who assumes threshold effects are not extant in high-involvement service demand may find conventional demand estimation methods lead to erroneous conclusions and less effective pricing strategies.

Originality/value

In the statistical literature on price-ending effects on product demand, most products for which demand is modelled are low-involvement consumer products priced at less than ten monetary units per unit of product. There is a lacuna in this price-ending effects literature regarding small businesses and high-involvement services offered at three-digit prices via monthly subscription. This research indicates that testing for threshold effects should be de rigeur in the methodology of demand estimation for telecommunications or other high-involvement services.

Details

Journal of Product & Brand Management, vol. 23 no. 2
Type: Research Article
ISSN: 1061-0421

Keywords

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Article
Publication date: 7 March 2016

Anup Kumar

The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates…

1310

Abstract

Purpose

The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization. A modest attempt has been made in the study to capture the relationship between the sales promotion, price discount and the batch procurement strategy of a particular product category to maximize sales volume and profitability.

Design/methodology/approach

Time series data relating to sales have been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products that considerably impact the sales promotion and intelligent pricing decisions. A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic.

Findings

The model captures the lag effect of sales promotion and price discounting strategy; other strategies have been formulated based upon the sales forecast that was done for taking the lot sizing decisions regarding procurement of products in the selected category. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact.

Research limitations/implications

There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories.

Originality/value

The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

Details

Kybernetes, vol. 45 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

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