AI/ML in the Fashion Retail Space Part 3
An inquest into fashion
If two industries are characteristic of being opposed to each other, it’s fashion and the emergent world of AI. In simple words, Fashion embraces what is daring, and all that is novel in pursuit of art and aesthetics – subjective goals, while AI/ML embraces those same two ideals in the pursuit of innovation and the maximization of efficiency and effectiveness – objective, data-based goals.
Fashion and AI/ML, and other emergent innovations of the industry 4.0 revolution are a match made in heaven for this reason. In literary and narrative theory, a ‘foil’ is a character that serves as a complementing to a protagonist, or any other character – personifying everything the protagonist isn’t, and therefore being capable of thinking in ways that the protagonist (or antagonist, as the case may be) cannot. With the interaction of these two forces, the story, play, or poem is given a wholistic, sensible, and fulfilling conclusion, making the story better off for it.
Similar is the relationship between AI/ML and the Fashion Industry. The Fashion Industry, by definition, is the one most likely to deploy radical tech strategies, analogous to their risqué designs, and daring trends. Another organic advantage of the Fashion Industry is that the core audience, a dominant proportion, view technology as fashion in-and-off itself, and thus more willing to accept drastic change in how we interact with retail brands. Fashion has always been at the forefront of making political messages, propagating, and aligning certain beliefs with their brands, and this niche space is now warming up to the next logical upgrade, Industry 4.0.
Let us take a deeper look at all present applications of AI/ML within the fashion space that can be infused into every retail entity’s value chain.
- Artificial Intelligence, or AI, for short, has taken over several domains with the prospective to incite a paradigm shift in business
- It does this through increased speeds, reduced operational expenditure, and availability of a treasure trove of big data, when combined, keeps them in the race
- AI is a sub-domain of computer science that is a stimulant for smart behavior in computer-managed machines to carry out activities related to human reasoning and capacities
- While fashion is the home of creativity, technology is the home of innovation – conflict is possible. But when all is said and done, it is a marriage made in heaven.
- AI utilizes human, logical reasoning capacities as a model, or as a guide, but by no means is the end goal substituting it.
- Ideal use cases in fashion view AI “Augmentative Intelligence”. Harnessing ML, algorithms, and rich data to augment the capacities of humans and enterprises.
- The important point here is to comprehend differing applications of AI in fashion and take up a personalized approach that plugs just into the spheres where it can potentially inject value into your business model.
AI within business operations
Merchandising and Styling Platforms
Personal Styling Platform
- Retailers can develop, track, and manage detailed client profiles leveraging an AI Styling Platform that automates/scales styling services of outstanding quality.
- AI produces hyper-customized suggestions for every customer that can be speed edited on the platform.
- Lastly, these outfits and combos can be moved to your online cart, shared through email, or delivered to the client as a fashion concierge styling box.
Visual Merchandising Platform
- VM teams can leverage AI-driven frameworks to hasten product page curations for the web store.
- AI curates bespoke web pages to tackle divergent requirements, trends, client segments, and geo regions more efficiently.
- It leverages a drag-and-drop interface to edit automated product pages; cutting down on expenditure and the expense of manual product presentation
- Merchandising managers can leverage an AI-driven internal dashboard in tracking comprehensive performance analysis of every member on their team.
- Allocation of team members to the client/product segments they are proficient at optimizes team assets and enhances cumulative performance.
Operations and Retail Decision-Making
- AI furnishes comprehensive analytics and data that facilitates fashion retail to precisely decide ideal geolocation and market drop calendar for inventory during tracking and managing the cumulative product life-cycle on a real-time basis.
Product and Pricing Mix Strategy
- Retailers can leverage AI-driven rich data to tackle every client and market segment with a customized product and price strategy.
- Data-driven decision makings are the need of the hour to accomplish the ideal product-pricing mixture for every market implies reduced surplus inventory and lesser price slashes.
- AI improves stock turnover by predicting the requirement to “shift” older stock and undertaking analysis of it against demand predictions. Inventory can be reassigned to targeted locations to fulfill demand and avoid store clustering. Markdowns and promo techniques can be planned and given priority based on what connects with the correct value-seeking clients at the correct time.
- Surveying market pricing trends, AI suggests ideal pricing points in the pursuit of optimizing revenue by obtaining the leg ahead.
- Retailers can identify the best seasonal timing to retain reduced prices during retention of minimal margin and when to mildly rake up prices to pad out the bottom line.
Supply Chain: Enhanced Efficiency, Sustainability, and Agility
Intuitively getting the client and the market atmosphere better minimizes the production of styles that will not sell which is a direct translation into reduced product waste output.
AI can also witness implementation into the supply chain management procedure at the preliminary stages to enhance efficiency, agility, and sustainability.
Bringing together historical timeline data with real-time information like weather reports, AI can make precise Time to Market (TTM) predictions.
Outfitting enterprises with real-time data on inventory performance levels and hot trends, AI facilitates agile supply chain strategies. Retailers can identify the best and worst performers ahead of time to cease scheduled production/manufacturing of products that are not popular with the clientele and rake up the orders on hero products.
Analysis of sophisticated aspects that are a contributor to carbon footprint, AI can detect the most sustainable supply partners and modes of transport for an enterprise that still makes financial and operational logic.
Retailers can develop business models that are more sustainable, without any compromises on the bottom line.
That brings us to the conclusion of the third part of this multi-part blog series on AI and ML in the Fashion Retail domain. In the next part of this blog series, we will take another deeper look at AI’s presence in e-commerce, particularly conversational commerce. Stay tuned.