Being on trend no longer guarantees sales and profitability in the fashion sector. In minutes, consumers can spot, own, and share a trend on social media, from any corner of the globe. As a result, hits can sell out rapidly, while misses do not move, even with heavy discounting.
The traditional product-development model is too slow. How to fashion brands outpace competitors? Top performers routinely use consumer insights very early in the product-design process and can have products ready for purchase in weeks, not months. But established brands have great difficulty doing either well. This needs to change, as up-and-coming brands are breaking the rules and resetting consumer expectations.
Most fashion companies understand the need for speed and data-based decision making. Data analytics is not new to the industry, which has long used spreadsheets and analyzed sales information. However, new sources of data are now available, such as the information on mobile devices or social media sites. The biggest change is the growth of unstructured data —texts, images, audio and YouTube videos. One method being deployed by retailers to discover more about what customers might want is the use of cognitive computing — programs that simulate human thought process and mimic the functions of the brain.
By tracking how customers behave while shopping, data analytics can also help to improve the design and management of shops and department stores. Despite the growth of online fashion outlets, many consumers still visit stores to touch and try clothing or shoes before buying.
Top-performing fashion companies have adopted a more sophisticated model based on understanding what the consumer wants. This model allows them to incorporate what has been selling and respond quickly to what is generating early sales.
The next-generation model should be based on anticipating what the consumer wants. Powered by predictive analytics and artificial intelligence, this model would proceed from design to delivery in close to real time.