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Spring brings fresh trends – and fresh challenges. Short product cycles and rising acquisition costs mean fashion brands need to act fast. With AI and predictive analytics, you can optimize stock, personalize campaigns, and boost loyalty – driving full-price sell-through and reducing markdowns.
The start of a new fashion season offers exciting wardrobe possibilities. From a spring/summer trend revamp – think floaty romantic boho blouses and floral print dresses, polka dots and bows or a bold hit of 80s tailoring, to a simple wardrobe refresh with an update on those outfit staples (just add a Polo Shirt!). Got them in your collection? Then it’s about time to get your marketing game sorted.
Why it matters: fashion is tough, and brands are quickly disappearing. Operating margins have dropped by 20% over the past 7 years, conversion rates are stuck at 2-3%, and you’re likely to sell only 50-60% of your stock at full-price. Which is a pretty rough deal. And while spring may offer fashion brands a sales boost, it also comes with unique challenges. Short product life cycles, fast-moving trends, and high customer acquisition costs often take a toll on profitability. The answer: A smart, data-driven marketing campaign.
On paper, spring seems perfect for launching collections. With the turn of the season, many consumers will be thinking about fresh new looks for the warmer weather ahead.
But product life cycles are painstakingly short – Shein is running on a staggering 52 cycles. Social media platforms like TikTok mean trends come and go in the blink of an eye (ever think you’d see Y2K fashion making a comeback? Us neither). And it’s more expensive than ever to acquire new customers. Shopify estimates customer acquisition costs for retail to be €129 per customer, with fierce competition and the constant need for innovation and expensive marketing driving these costs.
Fashion brands need to move fast. They need to target the right people to increase customer lifetime value and take advantage of customer demand before the season changes. Unsold inventory can lead to heavy markdowns, reducing your profit margin and leaving you with excess stock.
To stay ahead, fashion brands need:
That’s where data-powered insights and cross-channel automation come into play. By using tools that leverage intent signals, and behavioral data, you can make smarter, faster decisions across every touchpoint; from web to email to app.
French fashion label Sézane exemplifies the power of thoughtful inventory management with its unique capsule model. Releasing 12 curated collections each year, Sézane creates a sense of urgency and exclusivity that keeps customers coming back for more – without the need for markdowns (ever!). This strategy pays off with a staggering 95% full-price sell-through rate. By tightly aligning supply with demand to guide production and having their trading and marketing team fully asligned, Sézane not only protects its margins but also reinforces its premium positioning. It’s a clear case of how the elimination of data silos and intentional scarcity can elevate both brand value and profitability.
Here are some real-life examples and actionable takeaways from successful spring campaigns:
Delivering a consistent and personalized message across all channels builds trust, engages customers through multiple touchpoints and lets you promote timely collections and items.
Think push notifications, website, email, social media, and in-store experiences. Nobody’s Child promotes its spring collections across several mediums, including its website, emails, and social media. Their content stands out with thoroughly executed personalization and well-selected content.
Many fashion retailers often miss huge opportunities with in-store shopping. Some collect more than half of their revenue in-store, but have no way to understand who is buying what.
Understanding and integrating customer data from different channels (online, in-store, social media) creates a seamless shopping experience and builds a 360-degree view of your customer behavior. AI can identify in-store purchases, tag transactions and payment methods and unify your in-store data with online data, like social media posts and ads. This gives you a comprehensive overview of how online ads lead to offline conversions or sales.
H&M are very disciplined in bridging the online and offline experience by asking customers to sign in or register with their email address at the point of purchase in-store. This small step enables the brand to surface any eligible online discounts while simultaneously linking the shopper’s in-store activity to their digital profile. For the customer, it’s a win – seamless access to promotions and a unified purchase history across all channels. For the brand, it’s a smarter, more connected view of customer behavior that powers more personalized engagement.
AI makes it easier to deliver highly tailored experiences to different customer segments. By using browsing history and past purchases, it can suggest products that match customer preferences and seasonal trends. Brands like JIGSAW have seen great results with the approach.
JIGSAW uses AI to analyze buying history and browsing habits and provide targeted recommendations. The brand then uses this information to send personalized notifications and emails, helping to increase customer engagement and an uplift in sales of products customers were already considering buying. This data also helps you to understand demand, helping to avoid overproduction, reduce markdowns and maintain higher profit margin.
Loyalty programs are gaining popularity to connect with customers. 9 in 10 companies now offer some form of loyalty program to consistently target loyal customers with incentives. Fashion loyalty programs offer exclusive access to new launches, discounts and free delivery, and can be a way for brands to connect more with their customers.
Spanish fashion retailer Mango has a successful membership program it calls ‘Mango likes you’. It has over 8 million active members across 25 highly profitable countries. According to Mango, the initiative is part of Mango’s commitment to increase its focus on the customer. Using data collected by its customer department, Mango has an omnichannel overview of its customers and uses it to design initiatives that improve the customers experience through hyper-personalization strategies for all points of contact.
TIP: introduce a loyalty program and let AI unearth what your customers value most. This could be early access to new collections, membership-only discounts, community initiatives and free delivery.
We’ve now seen all the ways rich customer data can help make your spring fashion campaign a success. Let’s recap some best practices:
Spring collections offer a valuable opportunity for fashion brands to drive sales and build customer loyalty, but only if the right strategies are in place. Using AI, predictive analytics, and personalized marketing, brands can optimize their spring campaigns, increase full-price sell-through, and create a seamless customer experience.
Fashion is complex. It’s emotional, and heavily impacted by social media, market forces – and the weather. By using your own data, you can figure out the best ways to meet your customers where they are and improve profitability. We recommend you invest in tools leveraging AI and predictive analytics. Establish personalized marketing messages based on customer data and behavior. Deliver a consistent brand experience across all customer touchpoints. Engage lifetime customers with a loyalty program, and optimize inventory management to reduce markdowns and maximize profit.
Interested in finding out how data and AI can help your fashion brand succeed? Talk to us today.
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