Big Data analytics is gradually replacing the old-school fashion instinct. While the industry has always been continually reinventing items and trends, today this on-going process can benefit from critical information coming from a valuable tool: business analytics.
While trends, must haves, and styles follow each other against the background of market reactions and consumer behaviour, the industry calls for better forecasting tools in order to reduce the amount of unsold products and better promote their most likely best-sellers.
In this scenario, business analytics proves to be extremely valuable by detecting key information to forecast the fate of every stock keeping unit associated with a new item.
Business intelligence and best practices
A good business analytics system is able to read into large amounts of data and aggregate the information shared by the consumers through social media and other websites.
By embracing business intelligence we can achieve a deep insight in the consumers’ wishes and preferences about color, style, size, and other attributes of fashion products. This convenient perspective allows us to forecast the performance of new items by simply leveraging the features they share with other products which are already on the market, drastically reducing unsold inventory.
In view of the above, it is not surprising that some time ago SAP Performance Benchmarking has shown that those companies that were using business intelligence were achieving 54% higher operating margins than companies with low or no adoption of analysis strategies (source: Consumer Products Industry Insights based on SAP Benchmarking, 2012).
Using Data Analytics as a compass
New data-driven platforms are already helping buyers and brands make informed decisions.
Combining different factors such as search queries, social media activity, e-commerce best-sellers, and consumer feedback, business analytics can help each brand to successfully identify emerging trends, and act accordingly.
Moreover, the usefulness of business analytics does not end here: data can also warn us in advance about the mass adoption of a rising trend or its impending decline.
Artificial intelligence can analyse and aggregate the social media behaviour of an incredible amount of influencers and their followers. Data tools consider the same elements consulted by forecasters in order to to identify social shifts and new trends such as gender fluidity, environmental sustainability, or emerging subcultures. Data coming from social media platforms such as Facebook, Instagram, and Pinterest represent a massive collection of consumers’ wishes, ethics, and interests – and every fashion brand should be extremely interested in them.
Obviously, business analytics cannot replace creativity: data can monitor trends, but in order to launch one we still have to rely on the instinct and the trained eye of designers. What is clear is that now we have some useful tools to look more closely into the market and recognize when the ideal time for a specific product has come.