Social Media's Influence On The Fashion Industry

The forecasting process in many fashion industries is characterised by a number of features that can make it especially challenging. As highlighted by Thomassey (2014), these features include strong seasonal patterns, very short product life-cycles, coupled with a huge product variety, and short planning horizons (generally a few weeks) to manage replenishments of stocks at retail outlets. In addition, several exogenous variables, some of which cannot be directly controlled by manufacturing companies, can have a strong influence on sales. These include macro-economic conditions, marketing strategies, retailing strategies and fashion trends. As a result of these factors, coupled with the absence of long sales time-series for most products, the

However, although leveraging practitioners’ experience and intuition can result in favourable outcomes in highly unpredictable environments, it can also pose some risks for companies, since judgmental interventions can be strongly influenced by cognitive biases. Social Media data can be characterized as noisy and unclear, and perhaps the most discussed limitation of Social Media data is its bias nature. The users of Social Media are just a sample of all internet users i.e. they don’t represent our entire population in general. Not everyone is using Social Media which is why the sample from which data sets come from is most likely a very biased one (Gayo-Avello, 2011; Jahanbakhsh & Moon, 2014). Likewise, Gayo-Avello (2011) suggests that researchers suffer under a so called ‘file drawer effect’ – this is an inclination towards reporting positive outcomes after a couple of confident results while ignoring or suppressing the negative ones, which is another form of biased behaviour and can have drastic

They offer two explanations for the poor performance of judgmental forecasting. First, there are a number of inherent biases, including optimism, wishful thinking, lack of consistency, political manipulation, and overreacting to randomness. Second, people have a limited ability to consider and process large amounts of information. On the other hand, judgmental methods are often preferred by practitioners, since they can incorporate special insights, trends, and macro-economic factors, which are hard, if not impossible, to quantify in practice and since practitioners are more acquainted with them. Moreover, a lack of data often rules out the use of complex forecasting methods. This is certainly true for the mail order retailer that we