By 2025, fashion brands will either be selling via algorithms, or they'll be selling by accident.
Brands that were once reliant on gut instinct and traditional retail methods now face customers who demand personalization and fluidity throughout the shopping experience. The differentiator comes down to three pillars of competitiveness: AI, Marketplace Strategy, and Logistics.
Combined, these three pillars will provide your brand immunity from consumer expectations.
According to Accenture, AI could increase retail profitability by up to 59% by the year 2035. Many fashion brands who were early adopters are already achieving such profitability, and are transforming the front-end experience, as well as the back-end operations.
The most successful fashion ecommerce brands also understand the true value of AI is taking data and turning it into dollars. Every click, scroll, and purchase generates valuable data, and AI systems can analyze it all to determine what the consumer base wants, when they want it and how much they are willing to spend to get it.
Adopting a more data-centric approach can help you make decisions that are far more gratifying and sustainable throughout your entire business. Instead of relying on gut instinct or what worked last season, AI finds patterns through millions of customer interactions to show incredibly accurate forecasts of demand.
The end result is fewer markdowns, efficient inventory management, and increased margins.
Consumers want or expect everything at their fingertips as a fashion ecommerce site. They do not want to comb through endless oceans of product catalogs — they expect fashion ecommerce sites to know their style preferences and show them the items they are looking for. AI accomplishes this by looking at an individual's browsing patterns, purchasing history, and behavioral trends to create personalized product recommendations.
The AI recommendation engine on Amazon accounts for 35% of the company's revenue, and high-end brands like Burberry are extending the AI applications best practice recommendations by utilizing AI assisted styling tools to create a completely personalized shopping experience.
In terms of on-site product discovery, natural language processing (NLP) is utilized to help alleviate issues with intent, context, and styling preferences. For example, if a customer searches for a "comfortable work dress", AI can understand that the customer is looking for both comfort and workmanship. With an understanding of intent and context, AI can deliver on each of the two requirements, enhancing the shopping experience and substantially increasing conversion rates.
Additionally, in the fashion ecommerce environment, perhaps more than any other retail vertical, one of the largest concerns has always been the fact that the customer is not able to try items on, eat, or touch them prior to purchase. Returns related to fit and or expectations can wreak havoc on profit margins. AI is working to address the issue from a number of perspectives:
Virtual try ons and AR engage use of fitting tools so customers can preview fit and styling reflects realistic expectations.
Digital fitting rooms reduce returns related to size and increase confidence in buyers.
Return analytics allow brands to look at the items consistently causing returns to spot product issues throughout the next production run. This could be issues with the original design that can now be fixed prior to the next production run.
Finally, and perhaps most importantly, demand forecasting using machine learning based AI can reduce overproduction, which is a systemic issue in the industry, almost 57%, of fashion waste ends up in landfills. Naturally, AI can easily enable brands to better utilize the insight derived from looking at return patterns and identifying commonalities like; consistently tight sleeves, or fabrics that do not photograph well, to provide actionable items that can be used to improve their product. It can also assist brands to match the actual production processes to real time demand signals which can help protect margin.
Best-in-class apparel retailers utilizing AI right now to get ahead of trends versus chasing trends. AI systems are being deployed by retailers such as Zara and Burberry to discover new trends in fashion through social media, consumer behavior, and search behaviour. The best apparel brands are going to the market quicker than ever from design concept to consumers.
It has never been easier to get into the consumers mind and figure out exactly when they are entering the market at their peak of demand, without waiting a few months to sell the product to them at clearance.
When shopping online, consumers are very conscious of multiple digital frontiers. They need smart solutions where it isn’t just about throwing product listings everywhere to manage omnichannel.
Not all marketplaces are the same, and so many resources spend across so many platforms means less profitability. High performing online fashion retailers will focus on marketplaces that most aligned with their audience, category positioning and operations.
Specialist marketplaces usually outperform general retail marketplaces as they attract a more targeted audience and conversion is higher. Category marketplaces understand the intricacies in fashion retail, and have tools built specifically for apparel and accessories.
Discount marketplaces are a really good tool for offloading overstocked products and to preserve brand image on your main channels. Therefore, discount marketplaces should be viewed as supplementing your full-price sales strategy, not replacing it.
Be careful not to spend too much time and money on any smaller marketplace that does not provide justification for the operational investment.
So, if you want to explore marketplace opportunities, weigh marketplace opportunities against potential return on investment and not just marketplace presence.
Marketplaces are all about profit, and marketplaces profit is driven through assortment planning.
Bundling these low ticket-item purchases creates an opportunity for you to increase average order values (AOV), while also reducing the handling costs per event.
Cost differentials for pricing plan options differentiate for product performance, seasonality and channel play.
Pricing strategy that requires localization to pay attention to, is assessing the shipping costs, return rates and the cultural tendencies related to your industry, to optimize margin returns.
In the world of fashion retail, timing is everything. The longer the time period you can sell a product at full price, prior to the 'sale cycle', the better (i.e., labour days) - this is entirely season dependent.
Advanced fashion ecommerce brands leverage advertising metrics to help them strategically find profitable niches with less competitive forces. Key performing indicators such as Cost-per-Click (CPC) are often lower to bid because they are often keywords that there are less advertisers competing for. With less advertising competition in place, an ecommerce entrepreneur can win more customer attention for lower investment.
You can frequently assess the numerous metrics like conversion rates, liquidation rate and average cost of acquisition of a new customer, to help you strategically decide on where to allocate your resource investment effectively.
The fashion business models that will thrive in ecommerce over the next 5 years will do so with the combination of AI and marketplace strategy. As well as continual analysis of customer behavior, sales trends and operational metrics, the barrier between brands that grow and plateau will be clearly visible.
Investments in technology, partners and operational best practices made today will define your position for the next 10 years from now.
Cart.com’s Growth Marketing Team can help you develop and implement a comprehensive plan that leverages AI, optimizes your marketplace presence, get in touch with Cart.com today.