Smart use of AI is poised to become a crucial factor in fashion retail - and no serious player can afford to ignore the advantages it can bring.
By Elise M. Laupstad
Over the past decade, we've witnessed significant changes in retail, driven, in part, by rapid technological advancements and growing customer demands for more personalised and relevant shopping experiences.
Artificial intelligence (AI) can optimise and automate business processes to free up employee time to focus on value-added tasks which will help satisfy increased consumer expectations.
What are the impacts for the fashion industry?
Let’s consider three key areas where AI and generative AI can be applied
to exploit exciting possibilities in the sector:
Could AI replace designers?
Probably not - at least not now. But AI can significantly enhance the design process.
The role of designers in developing collections will remain crucial.
However, AI can be a valuable resource when it comes to trendspotting and can generate thousands of product design suggestions, based on upcoming trends and the company's brand strategy.
This helps streamline, simplify, and enhance the design process and several major fashion houses are already using this approach.
For example, Zara, the Spanish fashion giant, analyses sales data and customer feedback using AI to identify trends and create collections that appeal to their customer base.
H&M and Zalando are other big names using AI to predict upcoming fashion trends and create new designs by analysing social media, fashion blogs and other relevant data sources.
Inventory optimisation and forecasting
AI can be a great help to retailers in demand forecasting. It
can analyse sales trends to improve the quality of forecasts, thereby contributing to the optimisation of production, supply and inventory.
The result is reduced unsold inventory, improved working capital and fewer out-of-stock items.
By using predictive analysis, AI can also optimise pricing strategies by identifying slow-selling items that should be activated through marketing and/or price adjustments. It can also predict how different price reductions will impact different customers.
These actions can apply to all customers, specific customer segments, or simply be used just to increase visibility without adjusting prices. By combining this analysis with customer data, personalised pricing can be offered to build loyalty and maximise profits. Managing prices actively throughout the season avoids large end-of-season price reductions.
Today, most leading brands and fashion houses use AI and machine learning to some extent in their forecasting efforts. A significant amount of data is collected from various sources, including sales history, customer behaviour, seasonality, weather conditions, and social media.
Early sales forecasts, made before a product is manufactured, often rely on historical data from similar products, combined with data collected from various trend analyses,, and take into account fashion shows from major fashion houses and activity by influencers and on social media channels, providing the basis for production volumes.
Continuous analysis, enabling dynamic pricing, ongoing marketing efforts, and production scaling, uses updated sales data, behavioural data, customer feedback, weather conditions and social media.
Amazon is known for being at the forefront of dynamic pricing. It automatically adjusts prices based on various factors, including competitors' prices for the product. AI algorithms allow Amazon to adapt its prices in real-time to attract more customers and maximise revenue.
Zara has a reputation for its "just-in-time" production model, where it maintains high levels of replenishment to reduce inventory costs and minimise the risk of overproduction.
Enhanced customer experiences
The third area where AI can be of great help to the fashion industry is customer experience.
AI can convert sketches and product information into 3D, enabling virtual fitting rooms for personalised avatars. This can help reduce return rates, increase conversions for digital purchases, and boost sales per customer by using AI to style avatars in outfits which drive additional sales.
Zalando has announced plans to launch a virtual fitting room with avatars of customers, allowing them to see how jeans will fit their body exactly. More than 30,000 Zalando customers have already tested the first version.
We also know that writing informative and persuasive product and sales texts takes time. But, as we've seen with Open AI and Chat GPT, generative AI can generate text suggestions at incredible speeds.
AI can also assist in structuring, creating, enhancing, and personalising digital product descriptions, streamlining work and providing a better customer experience.
By identifying and predicting trends through AI, marketing can become more accurate and personalised, taking into account unstructured data such as customer sentiment, store behaviour, and omnichannel data.
AI can also be helpful in generating creative designs and texts, reducing manual work that currently takes time.
Stitch Fix, an online personal styling service, is one of several companies using generative AI models, combined with human expertise, to improve and streamline advertising campaigns on both Facebook and Instagram, as well as to write effective product descriptions for its website.
Although chatbots have thus far had limitations, we believe that generative AI can help reverse their poor reputation.
AI agents can handle advanced inquiries and offer multilingual support. The technology will bring customers significantly closer to the experience of talking to a human being, as it can use personal data, see and contextualise information and adapt the conversation tone naturally.
However, we should never forget the value of having a real person available to serve our customers!
How to get started?
Overall, the potential for AI in retail is enormous and exciting.
From optimising design to inventory management and enhancing customer experiences, AI can help retailers stay ahead and provide customers with the best possible experience. It can help increase top-line and bottom-line performance, and make the industry more environmentally friendly.
However, it is important to note that AI is a broad term encompassing a wide range of tools and platforms, and will require adaptation to your specific needs.
Implementation needs careful consideration and planning. It is crucial to identify the specific areas where AI can provide the most value to your business and to collaborate with experienced professionals to develop and implement effective AI strategies.
As AI technology continues to evolve and improve, we are likely to see more innovative use cases in retail.
The key to success will be continuous curiosity, staying updated on the latest developments, and having a plan for using AI as a tool for growth and success.
At Columbus, we can help you explore the possibilities of using AI for your business.
Feel free to contact us for a talk or more information.