In the realm of fashion and apparel, the field of trend forecasting — or anticipating the fashion of the future to guide upcoming fashion collections is a notoriously difficult task that requires a mix of science and art.
The discipline was the sole focus of a few elite fashion forecasters and experts who attended New York and Paris Fashion Weeks and other runway shows, taking into popular culture in their fashion forecasts.
Editors of prestigious fashion magazines were also involved in determining and influencing the latest fashions. Imagine the scene in The Devil Wears Prada when fashion genius Andy Sachs (Anne Hathaway) observes incredulously, “So because she [Runway Editor Miranda Priestley] pursed her lips, he’s going to change his entire collection?” Runway’s Art Director Nigel responds: “You still don’t get the conce, do you? Her view is the only thing that counts.”
It’s A Whole New (Trendsetting) World
Today, the number of fashion-conscious people has grown as fashion bloggers, celebrities, Instagrammers, TikTok influencers, and many more influence what’s trendy.
What’s trending and what’s not is changing quickly, which is often accelerated by the pace of social media, as new trends and fashions are one click away. The discerning shoppers can spot Taylor Swift’s newest Chiefs merchandise, or the Duchess’s brand-new knotted earrings, only to have these items sell out in a matter of hours.
Fashion trends are often unpredictable and sporadic, and determined by emotions, social influences, and popular cultural trends (think: Barbiecore aesthetic). Forecasting is not an easy job.
However, it’s something that fashion and apparel retailers have to do right. Being late to the party or off by the shade (with the spring line of clothing with orchids, when pastel violet is the color that’s “in”) can have e significant impact on final sales.
“A big part of success when working in this industry is agility: paying attention to newness and not ignoring it,” Jeffrey Roy, the editorial fashion specialist at a well-known luxury apparel retailer, has told Constructor. “You have to be open to ideas that are emerging and never totally know how they’re going to hit.”
The reason why a purely manual forecasting method isn’t working is
With ever-changing, interconnected, and often difficult-to-determine fashion influences, are retailers able to take a manual approach to forecasting?
“Outlook not so good,” your Magic 8 Ball might answer.
The method is not just time-consuming but however, but it is severely restricted in its effectiveness. It is susceptible to errors and delays in response time, as well as susceptible to the forecaster’s personal preferences.
Tap into Artificial Intelligence
As modern AI is bringing efficiency to the workplace and the field that deals with trend forecasting has been reaping rewards as well.
AI models have been standardized and improved fashion forecasting by immediately processing huge amounts of historical and current data, discovering patterns, and flagging what’s likely to be the most notable trends.
Particularly, AI can process data such as fashion show pictures and social media post reviews as well as in-store and online sales data, queries from search engines, as well additional information about behavior to make predictions more quickly and with greater accuracy than human beings.
There are a variety of benefits that come with enhanced forecasting through AI, which include:
- Giving customers a better experience and ensuring that retail inventory is aligned with the items that customers are most likely to purchase.
- Sales are increasing due to more products that are on target, getting to the right customers.
- This is a way of creating a more sustainable fashion industry and reducing the amount of items manufactured, then discarded, and eventually destined for the garbage dump. As per Fibre2Fashion, “the fashion industry is responsible for more carbon emissions globally than all sea shipping and flights combined, accounting for more than 10 percent. Furthermore, greenhouse gas emissions from the fashion industry are predicted to rise to more than 50 percent in 2030.”
What Are the Best Ways to Use AI for Trend Forecasting?
AI can be used to enhance and speed up the forecasting of trends in a variety of different ways. We’ll discuss some of the most common instances in the following.
Analysis of runways
AI models in the present can analyze how runway fashions will influence fashion trends in the mass market and luxury. They scour huge sets of photos and extract information on the most popular cuts, patterns, colors, palettes, and many more.
Additionally to that, search engines like Tagwalk incorporate proprietary AI and human tags to help highlight trends, to allow users to search fashion shows as well as accessories, designers, and other trends based on keywords.
Social analysis
The use of AI to analyze social media is a crucial element in forecasting since social media has a significant impact on and interacts with the fashion industry. Social media provides influencers with the opportunity to have a large audience and allows fashion brands to engage with their customers and provide information about trends at consumers’ fingertips.
Principal players in this sector are:
- London-based consultancy WGSN employs proprietary AI models that take into account social media and listening (among other information) in forecasting trends.
- Paris-based Heuritech is a company that provides “AI-powered insights to back fashion intuitions.” Every day, Heuritech analyzes millions of photos uploaded to social media platforms, using technology for virtual recognition to measure and predict what individuals wear according to the market.
- T-Fashion The company utilizes AI to analyse the influencer and social media data, as well as information, as well as data points from the targeted audience for the purpose of helping “unveil the future of fashion trends.”
- Trendalytics, which interprets Google Trends information, social media, and market data, e-commerce, to anticipate new trends as well as those that are in the process of becoming obsolete.
Free tools can help you spot trends in social media and show the trending topics. For instance, fashion and clothing brands and forecasters can instruct TikTok’s algorithm to highlight trends relevant to their markets to stay abreast of the latest developments.
Analysis of customer feedback
Customer feedback analysis uses AI — specifically, natural processing of language (NLP) to discover patterns, sentiment, and information from feedback provided by customers in a variety of types. These include product reviews, chatbots, online surveys, social media posts, and more. Different products deal with this issue and include Akkio, BazaarVoice, and Yotpo.
Brands may also think about feeding customer data into ChatGPT or like Generative AI (GenAI) models that can quickly conduct sentiment analysis and discover the most relevant trends from their customers’ feedback.
Armed with this information, Fashion and apparel brands can make informed choices about their products and fashions.
Demand sensing
It’s important to be aware of trends for the coming season. Fashion retailers and supply chain planners must also be able to gain an idea of the near-term future demand. What’s to come over the next few days (or perhaps hours) from today?
Utilizing the latest data on demand with AI sensors, these solutions can assist planners in making better decisions in the short term and respond with confidence. This method of forecasting is different in contrast to demand-planning, which creates strategic forecasts for horizons that span longer periods (e.g., months, years, and even years).
Popular demand-sensing AI-based products are Logility, Stylumia, and Woven Insights.
Data from the search engine and analysis of volume
The data from shoppers’ searches can also assist brands to gain an advantage of trends and take crucial short and long-term choices regarding the planning of inventory, promotions campaigns, product tagging, and more. The first indication of consumers’ preferences is usually the initial step on their journey.
Tools such as Google Search Console or Semrush, which are now incorporating additional AI features, will display the keywords or search terms that could bring a user to the site of a retailer initially.
When shoppers arrive on the site, fashion companies must know what they are doing. What are the terms they’re using? What are the most popular ones? What terms yield tangible results? What ones aren’t?
The most effective AI-powered product discovery software provides a user-friendly dashboards that provide real-time data. Utilizing the information from current searches, retailers are able to adapt their strategies for identifying products. They can adopt measures to speed up the shopping experience for shoppers while using the information to boost the ranking of products, like eliminating zero results for mistakes in searches.
Analysis of Clickstream’s behavioral data
In terms of strategic planning, one of the best sources for the interests of shoppers is the shoppers themselves.
This is why clickstream data, or the record of your buyers’ online actions, is a crucial indication of intention. Clickstream data shows what people choose to click or hover over, how long they spend the most time looking at, searching for the item, adding it to their cart, or removing it from their cart, etc.
Product discovery platforms specifically designed for e-commerce use clickstream as an essential signal. This lets fashion retailers better understand the behavior of shoppers as well as anticipate demands, and even create rankings for all products on the site.
How Generative AI (GenAI) Currently Plays a Role
With the advancements in Generative Artificial Intelligence (GenAI) technology, Fashion and apparel retailers can now more easily detect and anticipate changes in fashion trends, too. With this technology and intelligence, they can provide appealing product suggestions that reflect the individual’s preferences and are designed to be optimized for important business KPIs.
Here’s an example of how GenAI-based Attribute Enrichment helped a prominent fashion label to use real-time information and capitalize on consumer demand.
When TikTok introduced the term “LumberJane” to refer to waffle and baggy fleece shirts, however, many retailers of apparel did not manage to turn this trend into sales. This is because when customers were searching for “LumberJane” on their sites, but got none of results. This wasn’t because those kinds of clothes weren’t available in their catalog; in fact, because the information on the garments was not tagged in this manner.
Utilizing Attribute Enrichment. This fashion company could enrich its information about its products in the era and speed of social networks. GenAI’s tool identified trends in shopping and search, and then auto-populated “LumberJane” as a searchable item attribute on relevant clothing items. In this manner, the retailer instantly provided results that were in line with the shopper’s intent and could turn the demand into sales.
Additionally, results from other retailers that use Attribute Enrichment are 97%+ accuracy in tagging products. This is a lot higher than the accuracy achieved by humans performing the same task, and it also saves precious time.
Combining AI with Human Touch
Recent advancements in AI have allowed retailers to anticipate, plan, and respond to demands like never before. However, does this mean that AI is removing humans from the equation?
Utilizing AI-powered tools, fashion merchandisers and eCommerce teams can see their tedious and manual workload reduced.
For instance, Birkenstock merchandisers reduced manual work by 20% with the use of AI-based technology. The team was able to concentrate considerably on more strategic and creative aspects of their jobs.
Furthermore, instead of having to take a “black box” approach, certain search engines for product discovery allow retailers to quickly learn and refine the algorithms that drive products and results.
In a recent piece from The Guardian, Francesca Muston, the VP of fashion at WGSN, explained how human perspectives and imagination are often a great complement to AI models in forecasting, by putting the information from the models in a larger social context.
“If we’re forecasting, for example, for the beauty industry, that team of people may have worked on formulations of face creams and will have an innate understanding of the product,” she said to The Guardian.
Improve Forecasting, Trend-Spotting, and the Bottom Line
Even in its early stage, it’s evident that AI-driven technologies are making a significant impact on the operations of fashion retailers’ efficiency, customer experience, and profit marginsparticularly in the area of improving forecasting and spotting trends.
An earlier McKinsey survey found that more than three-quarters (73 percent) of fashion executives believe that GenAI is a top priority for their businesses over this year. In their “Unlocking the Future of Fashion” report, McKinsey predicts that in the coming 3 to 5 years, GenAI will bring between $150 billion $2.5 billion in the fashion, clothing, and luxury industries’ operating profits.
Does your company have the ability to leverage GenAI to gain in competitive advantage? What does the Magic 8 Ball say?”Outlook good!”
To understand how to utilize AI in a manner that’s not just glitzy but also delivers tangible benefit for your customers as well as your business, read this Constructor eBook, “Data-Driven Merchandising in Fashion & Apparel: Why It’s the Perfect Fit.”
