Customer Analytics in Fashion Retail Industry

Giri, Chandadevi; Thomassey, Sébastien; Zeng, Xiaoyang

doi:10.1007/978-981-13-7721-1_27

Cited by 11 publications

(10 citation statements)

References 3 publications

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“…Machine learning and deep learning models such as the support vector machine [1], neural network [17], and recurrent neural network [2] are among many forecast models that have gained popularity among forecast researchers and practitioners given their ability to overcome the drawbacks of traditional linear forecast models. NN models are considered the most efficient forecasting methods, as they have demonstrated high performance in various studies [18].…”

Section: State-of-the-artmentioning

confidence: 99%

“…Consumers now increasingly prefer to select their products from the immense pool of options and want them to be delivered in a very short time. It is challenging for fashion retailers to fulfil consumer demands in a short time interval [1]. Therefore, it is crucial for fashion apparel retailers to make efficient and quick decisions concerning inventory replenishment in advance based on the forecast of future demand patterns.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry

Giri

Chen

2022

Forecasting

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Compared to other industries, fashion apparel retail faces many challenges in predicting future demand for its products with a high degree of precision. Fashion products’ short life cycle, insufficient historical information, highly uncertain market demand, and periodic seasonal trends necessitate the use of models that can contribute to the efficient forecasting of products’ sales and demand. Many researchers have tried to address this problem using conventional forecasting models that predict future demands using historical sales information. While these models predict product demand with fair to moderate accuracy based on previously sold stock, they cannot fully be used for predicting future demands due to the transient behaviour of the fashion industry. This paper proposes an intelligent forecasting system that combines image feature attributes of clothes along with its sales data to predict future demands. The data used for this empirical study is from a European fashion retailer, and it mainly contains sales information on apparel items and their images. The proposed forecast model is built using machine learning and deep learning techniques, which extract essential features of the product images. The model predicts weekly sales of new fashion apparel by finding its best match in the clusters of products that we created using machine learning clustering based on products’ sales profiles and image similarity. The results demonstrated that the performance of our proposed forecast model on the tested or test items is promising, and this model could be effectively used to solve forecasting problems.

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Section: State-of-the-artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry

Giri

Chen

2022

Forecasting

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“…C. Giri and et al in (2019) [12]. has proposed a system for customer analyses in fashion, which described that the system can predict the customers' Requirements and can meet and understand them better than personal analytics.…”

Section: )mentioning

confidence: 99%

A Study about E-Commerce Based on Customer Behaviors

Mohammed¹,

Kadhem²

2021

ETJ

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E-commerce is one of the new virtual technologies that make life simpler for both traders, marketers, and customers. However, the main problem for the seller was how to know customer's intentions when they enter the website. This paper proposed to predict whether customers make a purchase or not from their previous behaviors. Therefore, this paper aims to predict the intentions of users that using online -shopping. the main aim of this study is to highlight customer behaviors to predict purchases and make a compression between the works that are related to the subject of this paper to conclude and suggest the best method to predict purchasing in e-commerce treading that depends on customer behaviors.

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“…Consumers choices are influenced by multiple factors such as social media, fashion shows, fashion events, etc. Fashion retailers face various challenges to fulfill rapidly changing consumer demands in a very short period of time [1]. For this reason, it is important for fashion retailers to optimize their decision plans quite ahead of the upcoming replenishment of their stocks by accurately predicting future market trends.…”

Section: Introductionmentioning

confidence: 99%

Forecasting New Apparel Sales Using Deep Learning and Nonlinear Neural Network Regression

Giri

Thomassey

Balkow

et al. 2019

2019 International Conference on Engineering, Science, and Industrial Applications (ICESI)

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Compared to other retail industries, fashion retail industry faces many challenges to foresee future demand of its products. This is due to ever-changing choices of their consumers, who get influenced by rapidly changing market trends and it leads to the short life cycle of a fashion product. Due to the advent of e-commerce business models, fashion retailers have to put a multitude of virtual product images along with their feature information on their websites in order for their customers to know the fashion products and improve their purchasing experience. It is imperative for fashion retailers to predict future consumer preferences in advance; however, they lack advanced tools to achieve this goal. To overcome this problem, this research work combines the historical information of products with their image features using deep learning and predicts future sales. Apparel images are converted into feature vectors and then are merged with historical sales data. We applied backward propagation neural network model to predict the sales of a new product. It is found that the model performs quite well despite the small size of the dataset. This approach could be promising for forecasting the new arrivals of apparels in the market, and fashion retailers could improve their efficiency and growth.

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Customer Analytics in Fashion Retail Industry

Cited by 11 publications

References 3 publications

Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry

Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry

A Study about E-Commerce Based on Customer Behaviors

Forecasting New Apparel Sales Using Deep Learning and Nonlinear Neural Network Regression

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