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  • 1.
    Giri, Chandadevi
    et al.
    Högskolan i Borås, Akademin för textil, teknik och ekonomi.
    Johansson, Ulf
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Löfström, Tuwe
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Predictive Modeling of Campaigns to Quantify Performance in Fashion Retail Industry2019Ingår i: 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    Managing campaigns and promotions effectively is vital for the fashion retail industry. While retailers invest a lot of money in campaigns, customer retention is often very low. At innovative retailers, data-driven methods, aimed at understanding and ultimately optimizing campaigns are introduced. In this application paper, machine learning techniques are employed to analyze data about campaigns and promotions from a leading Swedish e-retailer. More specifically, predictive modeling is used to forecast the profitability and activation of campaigns using different kinds of promotions. In the empirical investigation, regression models are generated to estimate the profitability, and classification models are used to predict the overall success of the campaigns. In both cases, random forests are compared to individual tree models. As expected, the more complex ensembles are more accurate, but the usage of interpretable tree models makes it possible to analyze the underlying relationships, simply by inspecting the trees. In conclusion, the accuracy of the predictive models must be deemed high enough to make these data-driven methods attractive.

  • 2.
    Giri, Chandadevi
    et al.
    Högskolan i Borås, Akademin för textil, teknik och ekonomi.
    Thomassey, Sebastien
    Balkow, Jenny
    Zeng, Xianyi
    Forecasting New Apparel Sales Using Deep Learning and Nonlinear Neural Network Regression2019Ingår i: 2019 International Conference on Engineering, Science, and Industrial Applications (ICESI), 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    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.

  • 3.
    Giri, Chandadevi
    et al.
    Högskolan i Borås, Akademin för textil, teknik och ekonomi.
    Thomassey, Sebastien
    Zeng, Xianyi
    Analysis of consumer emotions about fashion brands: An exploratory study2018Ingår i: Proceedings of the 13th International FLINS Conference (FLINS 2018): World Scientific Proceedings Series on Computer Engineering and Information Science / [ed] Jun Liu (Ulster University, UK), Jie Lu (University of Technology Sydney, Australia), Yang Xu (Southwest Jiaotong University, China), Luis Martinez (University of Jaén, Spain) and Etienne E Kerre (University of Ghent, Belgium), 2018, Vol. 11, s. 1567-1574Konferensbidrag (Refereegranskat)
    Abstract [en]

    Fashion products are characterized by high variability in terms of rapidly changing consumer preferences. Consumers express their emotions on social networks such as Twitter, Facebook and Instagram. The main objective of this paper is to explore Twitter data for recognizing customer sentiments about fashion brands and to analyze their overall perception towards the brands. Two brands, Zara and Levis, are considered and users’ tweets related to these brands are analyzed using text mining and Naïve Bayes classifier. The results from this study suggest that social media such as Twitter can serve to be the repository of consumer sentiments and opinions. Sentiment analysis of the tweets can indicate fashion trend and thereby enable fashion brand companies to quickly respond to the ever changing consumer demands.

  • 4.
    Giri, Chandadevi
    et al.
    Högskolan i Borås, Akademin för textil, teknik och ekonomi.
    Thomassey, Sebastien
    Zeng, Xianyi
    Customer Analytics in Fashion Retail Industry2018Ingår i: Functional Textiles and Clothing / [ed] Dr. Abhijit Majumdar, Prof. Deepti Gupta, 2018, s. 349-361Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper aims to give an overview of customer analytics in fashion retail industry in the era of big data. Fashion retail industry has been facing significant challenges since last few years due to rapidly varying customer demands. Nowadays, customers are much more informed and connected because of social media and other channels on the Internet. They demand more personalized services, and perception is not sufficient to understand our customers. Therefore, we need data to understand our customers and meet their expectation. We will discuss how customer analytics can create value in fashion retail industry, strategies and methodology to examine the consumer data. Employing and investing in these methods and technologies, industry will benefit from improved revenues, improve in sales, higher customer retention rates and thereby it will sustain in the uncertain markets. Segments are created using recency value of the customers, and their future behavior is predicted using transition matrix.

  • 5.
    Giri, Chandadevi
    et al.
    Högskolan i Borås, Akademin för textil, teknik och ekonomi.
    Thomassey, Sebastien
    Zeng, Xianyi
    Exploitation of Social Network Data for Forecasting Garment Sales2019Ingår i: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 12, nr 2, s. 1423-1435Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Growing use of social media such as Twitter, Instagram, Facebook, etc., by consumers leads to the vast repository of consumer generated data. Collecting and exploiting these data has been a great challenge for clothing industry. This paper aims to study the impact of Twitter on garment sales. In this direction, we have collected tweets and sales data for one of the popular apparel brands for 6 months from April 2018 – September 2018. Lexicon Approach was used to classify Tweets by sentence using Naïve Bayes model applying enhanced version of Lexicon dictionary. Sentiments were extracted from consumer tweets, which was used to map the uncertainty in forecasting model. The results from this study indicate that there is a correlation between the apparel sales and consumer tweets for an apparel brand. “Social Media Based Forecasting (SMBF)” is designed which is a fuzzy time series forecasting model to forecast sales using historical sales data and social media data. SMBF was evaluated and its performance was compared with Exponential Forecasting (EF) model. SMBF model outperforms the EF model. The result from this study demonstrated that social media data helps to improve the forecasting of garment sales and this model could be easily integrated to any time series forecasting model.

  • 6.
    Jain, Sheenam
    et al.
    Högskolan i Borås, Akademin för textil, teknik och ekonomi. Laboratoire de Génie et Matériaux Textiles (GEMTEX), ENSAIT, F-59000 Lille, France; College of Textile and Clothing Engineering, Soochow University, Suzhou 215168, China; Automatique, Génie informatique, Traitement du Signal et des Images, Université Lille Nord de France, F-59000 Lille, France.
    Giri, Chandadevi
    Högskolan i Borås, Akademin för textil, teknik och ekonomi. Laboratoire de Génie et Matériaux Textiles (GEMTEX), ENSAIT, F-59000 Lille, France; College of Textile and Clothing Engineering, Soochow University, Suzhou 215168, China; Automatique, Génie informatique, Traitement du Signal et des Images, Université Lille Nord de France, F-59000 Lille, France.
    Zeng, Xianyi
    Laboratoire de Génie et Matériaux Textiles (GEMTEX), ENSAIT, F-59000 Lille, France.
    Bruniaux, Pascal
    Laboratoire de Génie et Matériaux Textiles (GEMTEX), ENSAIT, F-59000 Lille, France.
    A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry2019Ingår i: IEEE Access, E-ISSN 2169-3536Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The enormous impact of artificial intelligence has been realized in transforming the fashion and apparel industry in the past decades. However, the research in this domain is scattered and mainly focuses on one of the stages of the supply chain. Due to this, it is difficult to comprehend the work conducted in the distinct domain of the fashion and apparel industry. Therefore, this paper aims to study the impact and the significance of artificial intelligence in the fashion and apparel industry in the last decades throughout the supply chain. Following this objective, we performed a systematic literature review of research articles (journal and conference) associated with artificial intelligence in the fashion and apparel industry. Articles were retrieved from two popular databases ‘‘Scopus’’ and ‘‘Web of Science’’ and the article screening was completed in five phases resulting in 149 articles. This was followed by article categorization which was grounded on the proposed taxonomy and was completed in two steps. First, the research articles were categorized according to the artificial intelligence methods applied such as machine learning, expert systems, decision support system, optimization, and image recognition and computer vision. Second, the articles were categorized based on supply chain stages targeted such as design, fabric production, apparel production, and distribution. In addition, the supply chain stages were further classified based on business-to-business (B2B) and business-to-consumer (B2C) to give a broader outlook of the industry. As a result of the categorizations, research gaps were identified in the applications of AI techniques, at the supply chain stages and from a business (B2B/B2C) perspective. Based on these gaps, the future prospects of the AI in this domain are discussed. These can benefit the researchers in academics and industrial practitioners working in the domain of the fashion and apparel industry.

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