Accommodating consumer diversity using business intelligence Your brand’s many faces A recent article in Quartz highlighted the significance of social media for the Fashion & Apparel industry. As buyer demographics change, it is vital that fashion brands market their products to a more diverse audience. Instagram and other social media platforms, with their global ubiquity, form a whirlwind of diversity with virtually limitless reach. These new channels of communication have become a battleground for cutthroat competition between savvy retailers that use smart, provocative marketing techniques to attract and retain a broad and loyal base of customers. Many fashion labels are quickly discovering the business benefits of casting a wider net. As the article mentions, “People will want to buy into a brand they see themselves in”. Achieving inclusive focus Catering to a diverse audience comes with its own challenges. You need to reevaluate your brand’s customer segmentation. You also need ways to personalize your marketing collateral for each sub- segment of your newer, more heterogeneous group of consumers. However, deadlines and your marketing budget put hard limits on how many versions of your brand messaging you can create. So how do you achieve mass appeal without trying to be all things to all people? Digital business intelligence and analytics are redefining how clothing and other experience goods are marketed online to multiple demographics. Leading online retailers use multiple cues from their visitors’ browsing behavior to deliver messaging and visuals that have the highest chance of converting that visit
into a sale by resonating with them on a personal level. The more closely your brand’s messaging aligns with a particular visitor’s age, gender, ethnicity, occupation, and interests, the more likely you are to convert that sale. Some obvious visitor parameters include geographic location and preferred language, but there are many other subtle details that can be tallied and analyzed using machine learning, and then aligned with data from customer surveys and other sources to maximize the impact of each online touchpoint. Some patterns that AI discover during “unsupervised learning” might even seem highly counterintuitive, but yield very robust and repeatable results. The ability to discover these patterns allows AI-powered digital tools to surpass human intuition in online content personalization. Styles for your subculture If your brand has a digital fashion analytics solution, but is only using it to personalize marketing campaigns, then you’ve missed the trick. The business intelligence that these solutions provide can be applied across the entire product lifecycle, from concept to clearance. Instead of focusing solely on how to sell the products you have, the most agile global fashion brands also use social media and browsing analytics to inform their design process. Everything from product wishlists to heatmaps of mouse-clicks on your online store can yield clues about which of your products are receiving the most attention at any given moment. When combined with AI-powered demographics, your brand gains superpowers – you can predict, with a remarkable degree of accuracy, the styles, colors, and variants that will appeal more strongly to a particular target group. So it isn’t just your online messaging that will evolve – your products will quickly assume the qualities that consumers find irresistible. Local looks for global brands Even your brick-and-mortar retail operations will enjoy the fruits of online analytics. Empowered by detailed demographics, you can make sure that each of your stores is stocked with styles that appeal to the people in that neighborhood. Several brands leverage detailed time-series machine learning to determine geographical differences in product seasonality. In plain English, digital solutions have helped top fashion retailers discover that not everyone starts wearing sweaters in the same week of autumn. As a result, they have been able to optimize their supply chain to ensure that when the weather changes, their new releases dominate the fashion landscape of every city and country they operate in.
Everything old is new again Similarly, machine learning can make radically effective recommendations for product repositioning that at first might strike you as sheer insanity. Suppose a particular color or style of cardigan has been popular among 50-something plus-size women in New England for several months, but is now suffering a decline in demand. Instead of opting for margin-sapping markdowns to clear your stock and cut your losses, your analytics solution might discover that similar styles are now gaining favor among 20- somethings in the Pacific Northwest! You’ve just been handed an opportunity to charge full price for something you were about to sell at cost. Different age and income groups also respond differently to various promotional strategies. Some might prefer buy-one-get-one-free offers, while others might respond better to rewards programs. Each group might have a tendency to buy particular sets of products at the same time. Once again, consumer analytics provide clear and quick insights into these differences, and help you retain more customers at a higher lifetime value. Conclusion Every person’s tastes are unique, sometimes exceptionally so, but statistics have shown that demographics-based similarities are real. Tapping into these patterns makes good business sense, and can provide an undeniable advantage to tech-savvy fashion brands. Where other apparel manufacturers and retailers rely on volatile and inconsistent “expert opinion”, data-driven businesses let the numbers do the talking. People around the world use clothing to announce aspects of their culture and group identity. By asking the right questions and leveraging AI-powered analytics, your brand can discover the messaging and product features that resonate with each potential customer. For more details on how machine learning and analytics can inform your fashion brand’s business decisions, please contact Visionet Systems.