How to Forecast Demand Accurately for the Next Season: The Billion-Dollar Fashion Retail Question

Here are 6 commonly used methods.

1-Relying on designers, buyers, and merchandisers’ opinion

Despite all the developments in AI-based demand forecasting, many fashion retailers still use a gut-based approach and trust their buyers, merchandisers, and designers to make pre-season forecasts.

Merchandisers read the market, buyers pay visits to production and design houses, and designers use their personal observations of what people will buy. In this method, long-term forecasts are limited by intuitions. This is more of an art and creativity-based method rather than an accurate science-based approach.

Besides, every designer or buyer can work on a narrow segment of the merchandise. For example, one can be working on the scarfs, whereas the other can be working on the crop tops. Therefore, using this method alone, fashion retailers can’t foresee the effects such as cannibalization or product substitution accurately. 

2- Finding similar items in the past and projecting from there

Fashion retailers might have similar products that are close enough to make comparisons. Think of a retailer who wants to forecast demand for a ‘never-out-of-stock product’ like a black dress for the next season.

Typically, the retailer has access to the historical data of existing or previously sold black dresses for the past few years. Looking at previous years’ data can help in forecasting demand at sufficient levels for existing black dresses. But they can’t be 100% efficient in predicting demand for a new item. Because of the fast-changing nature of the fashion industry, it’s quite impossible to fulfill the demand of tomorrow’s consumers if forecasts are based solely on yesterday’s data of similar products.

3- Working with a trend forecasting agency

Unlike other retail industries, fashion is heavily trend-driven. Fashion retailers can work together with data-driven trend forecasting companies that offer predictive analytics on upcoming trends and products.

Using trend forecasting to predict fashion direction and analyzing social media and fashion week trends can be helpful in better demand planning. However, this method is more likely to work for short-term forecasting, as trends can change seemly overnight. And the question remains: is this data applicable and reliable for forecasting items that will be in the market 6 months or a year from now?

4- Using product attribute and image processing data

As fashion retailers don’t have detailed information about the actual products for the next season, they try to take advantage of image processing and attribution data to find similar items.

They use these items’ features, design attributes (such as patterns, color, fabric, materials, and so on) also visual similarities. And generate long-term forecasts using machine learning models.

5- Getting customers’ insights on upcoming fashion trends

Today retailers meet their customers through various channels, survey them to understand their preferences better, and use the input to identify the trends they’re likely to follow in the upcoming season. They then use this data for the next season planning.

It’s always good to use customers’ opinions, analyze where they are getting inspired and use this data to predict trends. This was an efficient method in the pre-AI and analytics revolution era. Today future demand cannot be modelled purely based on customer preferences and predictions alone.

6- Combining all the methods into one holistic approach

Next season forecasting has long been an art form, but with the growth of AI-powered advanced prescriptive, predictive, diagnostic and descriptive analytics, it now becomes more of a science. Although forecasting continues to be a complex task with a track record of high error percentages, one thing is clear: Fashion retailers need to continuously look for new ways and methods and become more efficient and agile in how they forecast demand for the next season.