Whether the pandemic redefined much of the retail landscape—or simply accelerated the inevitable transformation, the results are undeniable—consumer shopping patterns are forever changed. And no sector of the retail economy has been impacted more than mid-tier fashion.
It’s fair to assume that fashion retail will never return to the pre-COVID sales mix dominated by brick-and-mortar purchases, nor will we see the spikes of online sales akin to the height of the lockdowns. There will always be a tried-and-true segment of consumers that prefer the tactile experience of combing through the racks of their favorite stores and trying on items before purchase. While other customers—particularly millennials and Gen Z—eschew the local mall in favor of shopping for fashion apparel via websites and mobile apps.
In turn, we’re now witnessing a fluid omnichannel environment that many fashion retailers are struggling to navigate—to literally “get the numbers right” for both in-store and online sales—vendor orders, on-hand inventory, distribution center/store allocation—in addition to efficient shipping and buy online, pick up in store (BOPIS) fulfillment. When those core functions become unbalanced, customers become frustrated with chronic out-of-stocks, delivery delays and other inefficiencies—before eventually turning their attention toward competitors. At the same time, excess or misallocated inventory can quickly eat away at the retailer’s crucial margin.
In turn, innovative fashion retailers are rethinking their approach, introducing a new concept of omnichannel demand planning—a seamless, holistic approach of understanding demand and balancing allocation of inventory across both in-store and online channels. Mastering this cross-channel optimization begins with properly analyzing and processing all available data—store sales, online sales, price and promotions, holidays and events, online traffic, customer demographics—even potential effects of local weather patterns—among a wealth of other relevant variables.
Identifying all these disparate datapoints—and interpreting their effects on consumer demand—would prove a herculean task for humans and manual spreadsheets alone. In their place, artificial intelligence/machine learning (AI/ML) algorithms have already demonstrated game-changing value to retailers—providing the agility and scale to keep pace in an ever-competitive mid-tier fashion market.
What are some of the initial benefits an AI-powered solution can deliver for an omnichannel fashion retailer?
Augmenting existing data systems – Contrary to some misconceptions, AI/ML solutions needn’t require a costly ‘rip-and-replace’ of legacy data infrastructure. Incorporated in tandem, they can actually enhance today’s common enterprise resource planning (ERP) systems—such as SAP, JDA, and Oracle—to make them more intelligent. Once the AI solution has ingested all the relevant data, it will generate a unified demand signal that can be referenced as a single source of truth for omnichannel allocation and replenishment.
Pre-season Buy Planning — Mid-tier retailers in particular plan their pre-season buy manually. When human buyers make decisions framed around voluminous spreadsheets—further complicated by today’s unwieldy supply chains and extended lead times—they’re prone to ordering sub-optimal quantities. Too few units of a popular SKU can lead to early stock-outs—lost sales, margin and revenue. At the same time, excessive orders, too much inventory and additional profit-eating carrying costs may eventually require steep markdowns to move merchandise out the door—while tying up capital which may be better invested elsewhere. Another emerging application of AI/ML forecasting enables buyers to accurately sync pre-season orders with projected consumer demand, backed by data-driven insights.
Proactive inventory management – Leading-edge AI/ML technology is playing an increasing role in eliminating the costly guesswork from ordering and allocating fashion products across channels, as well as in replenishing everyday staples. Guided by AI/ML-powered demand forecasts, retailers can make better-informed inventory planning decisions that minimize the inherent risks associated with fashion retail—while simultaneously boosting profits.
Pricing optimization/consistency – The sea change among customers’ omnichannel purchasing habits have complicated traditional pricing strategies. Retailers that have traditionally relied upon strict markdown rules—which in some cases may have even pre-dated the internet—now recognize a need for nuanced pricing structures to glean higher margin across both in-store and online sales. This represents a prime opportunity for AI solutions focused on more granular data—real-time sales, online shopping patterns, on-hand inventory, and other tracking—to determine optimal lifecycle pricing for every SKU, while also balancing options for selling those items at a higher price through the online channel. In short, where omnichannel demand has significantly increased the complexity of pricing, the advanced data analytics behind AI have effectively brought order to the chaos.
While the practical business applications of AI/ML have already been taking center stage among retail—from demand planning, to inventory management, to lifecycle pricing and more—mid-tier fashion may still be playing catch-up with larger enterprises. Today’s fluctuating omnichannel environment is an ideal opportunity for this underserved sector to explore leveraging these next-generation data science tools to streamline end-to-end operations, build upon their brand identities and further drive essential loyalty among their customer bases.
