Apenas uma postagem? Previsões de vendas diárias de empresas varejistas de beleza e cosmético a partir da influência de mídias sociais

Gabriel Gomes Pessanha, Eduardo Almeida Soares

Resumo


Objetivo: Estudar a relevância das postagens no Instagram na construção de modelos de previsão de variação de receitas de vendas diárias para empresas varejistas do setor de beleza e cosméticos.

Metodologia: Foram consideradas séries temporais de vendas diárias entre os anos de 2017 e 2019 de 10 empresas varejistas do setor de beleza e cosméticos. Métodos baseados em aprendizagem de máquina foram empregados e os modelos de previsões foram incrementados com variáveis numéricas do perfil oficial da empresa, da postagem feita pelo influenciador digital contratado e as características das imagens postadas pelo influenciador digital foram incluídas nos modelos. 

Relevância e Originalidade: O estudo é inovador, pois ultrapassa as reflexões qualitativas sobre a temática e traz evidências empíricas quanto aos impactos na acurácia da previsão a partir da inclusão de variáveis de mídias sociais. Apresentou-se uma estratégia de fusão de dados (numéricos e imagens) para a previsão de vendas diárias de empresas de varejo do setor de beleza e cosméticos.

Principais resultados: Os modelos se mostraram eficientes na previsão e a importância das variáveis likes e engajamento reforça a ideia de que a identificação e referência social gerada pelo ID são importantes aspectos no processo de decisão de compra. Constatou-se que as imagens são responsáveis por adicionar atributos exclusivos que ajudam na previsão e no entendimento dos padrões das séries de vendas.

Contribuições teóricas e metodológicas: O estudo demonstrou, de modo promissor, a eficiência dos métodos baseados em aprendizagem de máquina na previsão de vendas a partir de dados do Instagram, especialmente, no que se refere à incorporação e extração de dados de imagens.


Palavras-chave


Mídias sociais; Imagens; Inteligência artificial; Previsão de vendas; Marketing digital; Influenciador digital

Texto completo:

PDF (English)

Referências


Abolghasemi, M., Eshragh, A., Hurley, J., & Fahimnia, B. (2019). Demand Forecasting in the Presence of Systematic Events: Cases in Capturing Sales Promotions. arXiv preprint arXiv:1909.02716.

Agnihotri, R., Dingus, R., Hu, M. Y., & Krush, M. T. (2016). Social media: Influencing customer satisfaction in B2B sales. Industrial Marketing Management, 53, 172-180.

Alalwan, A. A. (2018). Investigating the impact of social media advertising features on customer purchase intention. International Journal of Information Management, 42, 65-77.

Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics, 34(7), 1177-1190.

Araujo, T., Neijens, P., & Vliegenthart, R. (2017). Getting the word out on Twitter: The role of influentials, information brokers and strong ties in building word-of-mouth for brands. International Journal of Advertising, 36(3), 496-513.

Arora, A., Bansal, S., Kandpal, C., Aswani, R., & Dwivedi, Y. (2019). Measuring social media influencer index-insights from Facebook, Twitter and Instagram. Journal of Retailing and Consumer Services, 49, 86-101.

Asur, S., & Huberman, B.A. (2010). “Predicting the Future with Social Media”, available at http://www.hpl.hp.com/techreports/20 I 0/HPL-20 I 0-53.pdf

Audrezet, A., De Kerviler, G., & Moulard, J. G. (2018). Authenticity under threat: When social media influencers need to go beyond self-presentation. Journal of Business Research, 177, 557-569.

Babić Rosario, A., Sotgiu, F., De Valck, K., & Bijmolt, T. H. (2016). The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 53(3), 297-318.

Bakhshi, S., Shamma, D. A., & Gilbert, E. (2014). Faces engage us: photos with faces attract more likes and comments on Instagram. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems – CHI ’14). Association for Computing Machinery, New York, NY, USA, 965–974.

Casaló, L. V., Flavián, C., & Ibáñez-Sánchez, S. (2018). Influencers on Instagram: Antecedents and consequences of opinion leadership. Journal of Business Research, 177, 510-519.

Chen, I. F., & Lu, C. J. (2017). Sales forecasting by combining clustering and machine-learning techniques for computer retailing. Neural Computing and Applications, 28(9), 2633-2647.

Childers, C. C., Lemon, L. L., & Hoy, M. G. (2019). # Sponsored# Ad: Agency perspective on influencer marketing campaigns. Journal of Current Issues & Research in Advertising, 40(3), 258-274.

Chen, T., He, T., Benesty, M., Khotilovich, V., & Tang, Y. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2, 1-4.

Cui, R., Gallino, S., Moreno, A., & Zhang, D. J. (2018). The operational value of social media information. Production and Operations Management, 27(10), 1749-1769.

De Veirman, M., Cauberghe, V., & Hudders, L. (2017). Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude. International journal of advertising, 36(5), 798-828.

De Vries, N. J., & Carlson, J. (2014). Examining the drivers and brand performance implications of customer engagement with brands in the social media environment. Journal of Brand Management, 21(6), 495-515.

Djafarova, E., & Rushworth, C. (2017). Exploring the credibility of online celebrities' Instagram profiles in influencing the purchase decisions of young female users. Computers in Human Behavior, 68, 1-7.

Dhanesh, G. S., & Duthler, G. (2019). Relationship management through social media influencers: Effects of followers’ awareness of paid endorsement. Public Relations Review, 45(3), 101765.

Erkan, I., & Evans, C. (2016). The influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior, 61, 47-55.

Felix, R., Rauschnabel, P. A., & Hinsch, C. (2017). Elements of strategic social media marketing: A holistic framework. Journal of Business Research, 70, 118-126.

Fildes, R., & Goodwin, P. (2007). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces, 37(6), 570-576.

Fildes, R., Goodwin, P., & Önkal, D. (2019). Use and misuse of information in supply chain forecasting of promotion effects. International Journal of Forecasting, 35(1), 144-156.

Fuchs, C. (2017). Social media: A critical introduction. (2nd ed.) London: Sage.

Gensler, S., Völckner, F., Liu-Thompkins, Y., & Wiertz, C. (2013). Managing brands in the social media environment. Journal of interactive marketing, 27(4), 242-256.

Gillon, K., Aral, S., Lin, C. Y., Mithas, S., & Zozulia, M. (2014). Business analytics: radical shift or incremental change? Communications of the Association for Information Systems, 34(1), 13.

Godey, B., Manthiou, A., Pederzoli, D., Rokka, J., Aiello, G., Donvito, R., & Singh, R. (2016). Social media marketing efforts of luxury brands: Influence on brand equity and consumer behavior. Journal of business research, 69(12), 5833-5841.

Goodwin, P. (2002). Integrating management judgment and statistical methods to improve short-term forecasts. Omega, 30(2), 127-135.

Gruhl, D., Guha, R., Kumar, R., Novak, J., & Tomkins, A. (2005). The predictive power of online chatter. Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (pp. 78-87).

Hajli, M. N. (2014). A study of the impact of social media on consumers. International Journal of Market Research, 56(3), 387-404.

Hajli, N., & Sims, J. (2015). Social commerce: The transfer of power from sellers to buyers. Technological Forecasting and Social Change, 94, 350-358.

Highfield, T., Leaver, T. (2014). A methodology for mapping Instagram hashtags. First Monday, 20(1).

Hu, Y., Manikonda, L., & Kambhampati, S. (2014). What We Instagram: A First Analysis of Instagram Photo Content and User Types. Proceedings of the International AAAI Conference on Web and Social Media, 8(1). Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14578.

Huang, T., Fildes, R., & Soopramanien, D. (2014). The value of competitive information in forecasting FMCG retail product sales and the variable selection problem. European Journal of Operational Research, 237(2), 738-748.

Hudson, S., Huang, L., Roth, M. S., & Madden, T. J. (2016). The influence of social media interactions on consumer–brand relationships: A three-country study of brand perceptions and marketing behaviors. International Journal of Research in Marketing, 33(1), 27-41.

Hughes, C., Swaminathan, V., & Brooks, G. (2019). Driving brand engagement through online social influencers: An empirical investigation of sponsored blogging campaigns. Journal of Marketing, 83(5), 78-96.

Hwang, K., & Zhang, Q. (2018). Influence of parasocial relationship between digital celebrities and their followers on followers’ purchase and electronic word-of-mouth intentions, and persuasion knowledge. Computers in Human Behavior, 87, 155-173.

Hyndman, R. J., & Athanasopoulos, G. (2014). Optimally reconciling forecasts in a hierarchy. Foresight: The International Journal of Applied Forecasting, (35), 42-48.

Jiménez-Castillo, D., & Sánchez-Fernández, R. (2019). The role of digital influencers in brand recommendation: Examining their impact on engagement, expected value and purchase intention. International Journal of Information Management, 49, 366-376.

Jin, S. V. (2018). “Celebrity 2.0 and beyond!” Effects of Facebook profile sources on social networking advertising. Computers in Human Behavior, 79, 154-168.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

Kim, N., & Kim, W. (2018). Do your social media lead you to make social deal purchases? Consumer-generated social referrals for sales via social commerce. International Journal of Information Management, 39, 38-48.

Kim, E., & Kim, Y.-C. (2018). Communication Infrastructure, Migrant Community Engagement, and Integrative Adaptation of Korean Chinese Migrants in Seoul. Communication Research, 1-23.

Kourentzes, N., & Petropoulos, F. (2016). Forecasting with multivariate temporal aggregation: The case of promotional modelling. International Journal of Production Economics, 181, 145-153.

Kremer, M., Siemsen, E., & Thomas, D. J. (2016). The sum and its parts: Judgmental hierarchical forecasting. Management Science, 62(9), 2745-2764.

Kumar, A., Bezawada, R., Rishika, R., Janakiraman, R., & Kannan, P. K. (2016). From social to sale: The effects of firm-generated content in social media on customer behavior. Journal of Marketing, 80(1), 7-25.

Kulkarni, G., Kannan, P. K., & Moe, W. (2012). Using Online Search Data to Forecast New Product Sales. Decision Support Systems, 52 (3), 604–611.

Lahuerta-Otero, E., & Cordero-Gutiérrez, R. (2016). Looking for the perfect tweet. The use of data mining techniques to find influencers on twitter. Computers in Human Behavior, 64, 575-583.

Lamond, D., Dwyer, R., Ramanathan, R., Black, A., Nath, P., & Muyldermans, L. (2010). Impact of environmental regulations on innovation and performance in the UK industrial sector. Management Decision, 48(10), 1493-1513.

Lassen, N. B., Madsen, R., & Vatrapu, R. (2014). Predicting iphone sales from iphone tweets. 2014 IEEE 18th International Enterprise Distributed Object Computing Conference (pp. 81-90). IEEE.

Lawrence, M., Goodwin, P., O'Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of forecasting, 22(3), 493-518.

Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on social media: Evidence from Facebook. Management Science, 64(11), 5105-5131.

Liaw, A., & Wiener, M. (2002). Classification and regression by random Forest. R news. 2(3), 18-22.

Lin, H. C., Bruning, P. F., & Swarna, H. (2018). Using online opinion leaders to promote the hedonic and utilitarian value of products and services. Business Horizons, 61(3), 431-442.

Lipizzi, C., Iandoli, L., & Marquez, J. E. R. (2015). Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams. International Journal of Information Management, 35(4), 490-503.

Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of marketing, 70(3), 74-89.

Lou, C., & Yuan, S. (2019). Influencer marketing: how message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 19(1), 58-73.

Lu, B., Fan, W., & Zhou, M. (2016). Social presence, trust, and social commerce purchase intention: An empirical research. Computers in Human Behavior, 56, 225-237.

More, J. S., & Lingam, C. (2019). A SI model for social media influencer maximization. Applied Computing and Informatics, 15(2), 102-108.

Oliva, R., & Watson, N. (2009). Managing functional biases in organizational forecasts: A case study of consensus forecasting in supply chain planning. Production and operations Management, 18(2), 138-151.

Ramanathan, U., & Muyldermans, L. (2011). Identifying the underlying structure of demand during promotions: A structural equation modelling approach. Expert systems with applications, 38(5), 5544-5552.

Schivinski, B., & Dabrowski, D. (2016). The effect of social media communication on consumer perceptions of brands. Journal of Marketing Communications, 22(2), 189-214.

Schouten, A. P., Janssen, L., & Verspaget, M. (2020). Celebrity vs. Influencer endorsements in advertising: the role of identification, credibility, and Product-Endorser fit. International journal of advertising, 39(2), 258-281.

Shiau, W. L., Dwivedi, Y. K., & Lai, H. H. (2018). Examining the core knowledge on facebook. International Journal of Information Management, 43, 52-63.

Simonyan, K., & Zisserman, A. (2014). Very Deep convolutional networks for large-scale image recognition. Computer Science. arXiv preprint arXiv:1409.1556.

Slack, N., Chambers, S., & Johnston, R. (2009). Administração da produção (Vol. 2). São Paulo: Atlas.

Sokolova, K., & Kefi, H. (2020). Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. Journal of Retailing and Consumer Services, 53, 101742.

Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300.

Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. (2016). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research, 252(1), 1-26.

Tien, D. H., Rivas, A. A. A., & Liao, Y. K. (2019). Examining the influence of customer-to-customer electronic word-of-mouth on purchase intention in social networking sites. Asia Pacific Management Review, 24(3), 238-249.

Trapero, J. R., Kourentzes, N., & Fildes, R. (2015). On the identification of sales forecasting models in the presence of promotions. Journal of the operational Research Society, 66(2), 299-307.

Welbourne, D. J., & Grant, W. J. (2016). Science communication on YouTube: Factors that affect channel and video popularity. Public understanding of science, 25(6), 706-718.




DOI: https://doi.org/10.5585/remark.v20i4.17914

Apontamentos

  • Não há apontamentos.


Direitos autorais 2021 Revista Brasileira de Marketing

Revista Brasileira de Marketing – ReMark

Brazilian Journal of Marketing - BJM

e-ISSN: 2177-5184
https://periodicos.uninove.br/remark

Rev. Bras. Mark. - ReMark ©2021 Todos os direitos reservados.

Esta obra está licenciada com uma Licença 
Creative Commons Atribuição-NãoComercial-CompartilhaIgual 4.0 Internacional