Our world is changing. See-now-buy-now, social media, trends, climate change, single use textiles, ethical sourcing… These are just a few of the factors that affects the modern world of apparel retail. Consumers are becoming more politically aware and are likely to be changing their shopping habits as we move further into the 21st century. So how do we use technology to keep pace with this change? Artificial Intelligence is one way we can look to the future and anticipate this ever changing landscape.
Looking at apparel in general, there have been many recent departures away from historic merchandising techniques. From where product originates as new locations are emerging as sourcing hubs; to how long a product is sold for, as fast fashion retailers have transformed the notion of a season; to how products are sold, as e-commerce retail permanently changed the manner of retail, apparel and fashion retail has changed vastly in the 21st century.
Today’s customer is also becoming more savvy. The number of shopping channels available to a customer has increased and now includes e-commerce sites often providing perks such as free shipping and a money back guarantee, allowing a customer to try on clothes in the comfort of their own home and return products at whim. No longer can a retailer count on simply opening the shop door, nor can they count on enterprise forecasting platforms. In the face of such a changing landscape, how can a brick-and-mortar or omni-channel retailer stay ahead of the game to better predict the future needs of its customers?
They have access to unprecedented amounts of data. By making use of artificial intelligence (AI) and machine learning techniques, in conjunction with industry knowledge and intuition, there is a way to look forward and anticipate the future. Using AI allows a business to better forecast demand, taking into account not just the internal metrics of a business but external factors such as upcoming weather patterns, sociological moves and fashion trends.
Trends are changing rapidly with the advent of social media and influencers. Products that may have remained fashionable for 3 months are now likely to have a seasonal shelf life of 6 weeks so getting a fast read on what products are on trend and what are not is key to a successful season. AI in forecasting helps in the planning process by allowing retailers to drill down very quickly to various levels of aggregation to allow a planner or buyer to understand the nuances of an upcoming trend and act quickly, generally in advance, rather than reacting as sales happen.
Different customer segments buy differently and this needs to be accounted for in the forecasting process. Fortunately, its ability to understand emerging trends in real-time by analysing all products’ sales in parallel, while also making use of changing customer, sociological, weather and other external data, makes AI a key tool in the forecasting process to allow retailers to sense trends earlier and react faster.
This can become even more powerful when considering that prices may need to react more dynamically with reference to shorter trend cycles, competitor behaviour, etc, when AI can forecast performance at various prices and recommend the price that yields the highest performance on a chosen metric (GMROI, sales or sell-through).
Ultimately, as retail becomes increasingly dynamic, retailers cannot afford to rely on the same linear forecasting tools they have for decades, and will have to switch to machine learning based methods. The retailers that do so first will gain a competitive advantage in doing so. Their tool reacts based on how the real world and the customer evolves.
With the focus now firmly on ethical sourcing and the level of wastage in the apparel industry (with attention being turned to the wastage practices of retailers as diverse as Burberry and H&M, which waste hundreds of millions of dollars worth of clothing) there are likely to be changes to buying patterns which AI can help steer us through.
Will natural fibres become more popular? Is a customer going to steer away from cheap mass produced goods in favour of better made, higher quality products? If so, is this because of a rational economic decision to maximise long-term utility through holding a product longer, or because of ethical considerations associated with the high level of wastage in fast fashion? What would your take be? What would more intelligent and dynamic forecasting do to help you predict customers’ direction better?