Intro

Welcome to the fourth part of the multi-part blog series on AI/ML in the Fashion Retail Space by AICorespot. This part will explore AI’s presence in e-commerce, and an increasingly popular feature, fitment rooms – otherwise referred to as virtual trial rooms. We hope that you will enjoy reading our blog series as much as we enjoyed creating it.

AI/ML and the Fashion Industry are worlds apart in their philosophies, outlook, innovation, and general disposition – but as we’ve explored prior, they collide together in ways to create value for the consumer at an unprecedented level. Even more so than value, convenience is the name of the game. AI and ML facilitate a customer-centric approach to Fashion Retail and is a common-sense integration in a market environment where customers crave experiences more so than mere products or services.

AI/ML within Fashion is the opposite of a double-edged sword, it’s a retractable katana with a stun gun for a handle. Both the seller and the buyer are infinitely better off with its integration into Fashion Retail, and the benefits range from inventory scheduling, trends prediction, churn prevention, and emergent-tech augmented options for consumers. So, it’s a win-win for everybody, a match made in heaven.

Web-based Product Recommendations

Visual Similarity Recommendations

AI leverages visual detection and critical product traits to suggest visually similar alternative options for every item on a fashion e-tailers online inventory. Chatbots serve as an innovative interface between the customer and the business, with NLP seeing significant inroads during the pandemic era. Specialists have predicted that a majority of interactions that clientele have with a business will be with chatbots in the very, very near future. Some estimates even pegged this at last year, 2021, and we cannot argue that NLP advancements have not go on unabated.

Clients are rerouted to several relevant product pages on an e-tailer’s website when an inventory article is not available in a specific size. This serves as a very effective call-to-action, and prevents the loss of a customer – psychological studies have proven that individuals are vulnerable and frustrated when they don’t get their desired product, this is the perfect way to tap into that frustration and possibly lead them to something better. It’s smart, and just sensible business overall. Contrast this with the brick-and-mortar stores of 50 years ago.

Without the power of the internet and emergent tech, the customer would’ve just sighed and left the store, hoping for better days. With ML, the customer has options. And these options are not just with regards to the alternatives that they have when a product they like is out of stock.

The Elephant in the Room – Customer Churn

• The precise meaning of ‘churn’ in the retail space has always been kind of elusive. Subscription businesses are covered with regards to measuring churn.
• Subscription-based businesses have the luxury of knowing the terms and duration of engagement, while retail timelines are a lot more random.
• No shopping in a quarter would tag a client as ‘inactive’, in half a year, they’d become ‘lapsed’ and in a year, they have ‘churned’.
• The issue with this model is that is woefully misaligned with real-time client behavior.

Churn prevention and client retention stratagem

To accomplish their objectives, the retail brands are required to integrate advanced marketing tech that would be compatible with the agile-data-centric procedures it would need to deploy customized client retention and churn minimization techniques.

Following a market assessment, the retailer can combine and analyze comprehensive client behavioral data from all channels available at their disposal, and this is mostly driven by AI and ML strategies. Off-the-shelf products usually feature very friendly self-service interfaces.

On-field use case of ML – personalization is the precursor to positive outcomes – and it does this efficiently

To measure the results, a retailer deployed ML strategies in the pursuit of churn prevention and deployed a solid test design that could facilitate them to measure the incremental influence of their deployment.

Following two weeks of a comprehensive evaluation, the retailer witnessed extremely positive results:

• A 15% increase in buy rates amongst high-value clients
• $2M estimated appreciation in yearly preserved net margins
• A 20% reduction in client churn rate.

These are not numbers to scoff at. The next step after figuring out these numbers would be to initiate a large-scale omnichannel reengagement campaign – fully automated.

“Get the Look” Recommendations

AI utilizes bleeding-edge fashion trends and leading style philosophies to display total outfit suggestions for every product on an e-tailers web storefront.

Clients are receptive to new clothing but tend to be oblivious regarding styling. ML-driven clothing suggests the different manners in which they can combine differing articles of clothing to form new looks.

Inspiring the customer with high-quality AI-based styling implies upselling and appreciating basket size starting with a single product, and moving on to a total look, put together with complimentary items in the inventory.

Customized Recommendations

• Personalize for Region

AI can customize and curate recommendations and suggestions to display differing results for differing regions a retailer is operational in. Market trends, style philosophies and identities, and individual tastes can have considerable variance between different geographical locations.

What a ‘region’ is can be customized to each retailer dependent on their regional strategies. For instance, one retailer might leverage Europe and Asia as being macro-regions. Meanwhile, yet another different retailer might wish to further customize outcomes for micro-regions within the European region to display different suggestions in Northern Europe, Southern Europe, and Central Europe.

• Customization per client segment

Retail brands can tackle their different client segments with customized suggestions that link up best with every segment.

Also, AI facilitates retailers to concentrate on different client segments they desire to tackle. For one client segment, it might make more common sense, it makes more logical sense to display suggestions that promote upselling, while for another one, the objective might be cross-selling.

• Customization per unique client

The capacities of AI and ML can be harnessed to make visual similarity and product suggestions that are hyper-customized to every client, considering their unique traits and characteristics.

After this data has been identified and isolated, this is merged with data from the individual’s clients’ historical browsing and purchase behavior.

Every customer is displayed a different similarity and outfit recommendation personalized to them for each article on the web-store.

This takes online stylistic suggestions to the same quality standard upheld by a bespoke private styling service.

Retailers can adapt their private styling services furnished at the site to top-tier VIP clientele through to all client segments with no expenditure required for dedicated stylists.

Conclusion

This brings us to the conclusion of the fourth part of this multi-part blog series on AI and ML in the fashion retailspace. The next part will explore use cases, the statistics behind AI adoption, and a lot more.