In an eCommerce article, DataArt’s editorial team discusses the concept of trend scouting and outlines three stages of the trend scouting process.
“With the invention of new technology, such as machine learning one can collect and analyze user data more accurately. This makes it easier for companies to research and discover emerging trends, and use this knowledge to adjust their marketing strategy. This is called trend scouting, or demand forecasting. In this post, we will look at the process and its three main stages.”
The idea behind this approach is to “have computers make sense of textual content produced by people,” according to Andrey Nekhaenko, Retail & Agriculture Lead at DataArt. Having analyzed this content, computers can then recognize and categorize the patterns they find. The content is primarily sourced from social media, but also comes from internal information systems and databases.
“Retailers need natural-language analysis to decide what products to store, to see what sells, and to decide what to put in catalogues. It also is a great tool for retailers to strike a better deal with their suppliers. “
“The trend scouting process can be broken into three main stages. During the first stage, information needs to be gathered. The second stage is when this information is sorted — it has to be categorized and anything irrelevant should be removed, which requires to develop some algorithms internally. The third and final stage is processing. Data is processed using machine learning, which helps classify and analyze texts. “
The original article can be found here.
