As we continue transitioning toward a post-pandemic economy, mid-tier fashion stands out as one retail sector that is irrevocably changed.
It's clear now that fashion retail will never return to a pre-COVID sales mix dominated by in-store purchases, nor are we ever going to see the levels of online sales that occurred during the height of the lockdowns. There remains a considerable segment of consumers that will always prefer the tactile experience of visiting their favorite stores to browse through racks and try on items before purchase. While other customers—particularly millennials and Gen Z—avoid the local mall in favor of purchasing clothes online via websites and mobile apps.
This results in an ever-changing omnichannel landscape that many fashion retailers are struggling to navigate—to literally “get the number right” for both in-store and online sales (e.g., vendor orders, on-hand inventory, DC/store allocation), plus efficient shipping and BOPIS fulfillment. In addition to these essential capabilities, out-of-stocks and/or delivery delays frustrate customers, who will quickly turn to competitors for alternatives. And, as we’ve discussed in a previous blog, excess or misallocated inventory can quickly eat away at the retailer’s profits.
To tackle these problems, successful fashion retailers are rethinking their approach and introducing the idea of omnichannel demand planning – a seamless, holistic approach to understanding demand and balancing the allocation of inventory across both in-store and online channels. Mastering this cross-channel optimization requires properly analyzing and processing all the available data – store sales, online sales, price and promotions, holidays and events, online traffic, customer demographics, and external factors such as weather, among numerous other relevant variables.
Interpreting all these data sources and their effects on demand would take a human more time and effort than is reasonable. By contrast, AI/ML algorithms have already demonstrated game-changing value to retailers—enabling them to keep pace in an ever more competitive mid-tier fashion space.
What are some of the specific benefits an AI-powered solution can deliver for an omnichannel fashion retailer?
Augment Existing Solutions – AI-powered solutions can easily be used to make existing ERP systems - such as SAP, JDA, and Oracle - more intelligent. Once the AI solution has ingested all the relevant data, it will generate a unified demand signal that can be used as a single source of truth for omnichannel allocation and replenishment, as we’ve already accomplished for one venerable 300-store fashion retailer in the Southeast.
Proactive Inventory Management – Leading-edge AI/ML technology is playing an important role in eliminating the costly guesswork involved in ordering and allocating fashion products across channels, as well as replenishing everyday staples. By making these decisions using AI/ML-powered demand, retailers can better minimize the risks associated with fashion retail—while simultaneously improving profits.
Pricing Optimization & Consistency – The omnichannel purchasing habits of customers have complicated traditional pricing strategies. Retailers that have traditionally used strict markdown rules are revaluating them as outdated. As an alternative, they are turning to data science that uses more granular data (real-time sales, online shopping patterns, on-hand inventory, etc.) to determine optimal lifecycle pricing for every SKU while also balancing the ability to sell those items at a higher price through the online channel. In short, where omnichannel demand has significantly increased the complexity of pricing, the introduction of AI has effectively brought order to the chaos.
We’re particularly excited about the near-term opportunities for mid-tier fashion retailers to capitalize on the recent advancements in AI to better help build strong brand identities and serve loyal customer bases. During this period of changing demand patterns, mid-tier retailers are better positioned than their larger competitors to pivot to meet the changing needs of consumers and take full advantage of omnichannel preferences.
While we’re proud of our track record of success for clients across the retail spectrum, we think mid-tier fashion is an underserved segment when it comes to the next generation of AI-powered demand planning, inventory management and lifecycle pricing. For more information on our retail solutions, contact us.