Sussed

Product Experience

Problem Space 

Problem Statement  

“Fashion brands need a way to streamline operations between marketing and manufacturing departments/teams because disjointed operations affects profit margins”

Problem Background  

The users of our software are fashion brands with 8-20 million dollars in annual revenue that primarily use manually(human-led) processes for their marketing and manufacturing decision making. Fashion brands are experiencing over-manufacturing resulting in surplus inventory. Surplus inventory results in wasteful outputs to the environment and revenue loss due to unsold or discounted products. Fashion brands experience not being able to accurately predict trends in-time to fulfill manufacturing timelines and determine the right quotas. 

To gain a comprehensive understanding of their customers, a fashion brand conducted an end-user online survey, user interviews, and MVP (Minimum Viable Product) testing. The goal was to refine their brand strategy and improve the user experience on their website. Here are the key findings:

Research Insights

The user pain points identified through MVP feedback and other sources include:

Lack of Data-Driven Insights: Brands currently rely on guesswork for predicting product success, indicating a need for more data-driven insights.

Difficulty in Trend Prediction: Brands struggle to predict trends and the lifespan of specific styles, which can impact inventory management.

Minimum Order Volumes: Brands are constrained by minimum order volumes, affecting their ordering quantities and potentially leading to excess inventory.

Shipping Costs: Air shipping from Asian manufacturers is considerably more expensive than sea shipping, affecting operational costs.

Desire for Niche Tools: Brands expressed a desire for algorithms similar to Shein's, suggesting a need for niche and specific tools.

Supporting Data:

The MVP received feedback from 40 testers, with 22 recorded responses specifically addressing the accuracy of style recommendations. Additionally, users found the "Sussed" experience helpful in choosing outfits when shopping with their favorite brands. These findings indicate user satisfaction with the style recommendation feature and the overall usefulness of the app.

Feedback from Users:

Users provided feedback highlighting the following positive aspects:

Helpful User Experience: Users appreciated the game-like experience of the user interface.

Style Recommendations: Users generally found the "Sussed" experience helpful when shopping with their favorite brands.

Desire for Specialized Algorithms: Brands expressed a desire for algorithms similar to Shein's, indicating a need for specialized tools.

In conclusion, the insights gathered from end-user surveys, interviews, and MVP testing have revealed critical pain points for both users and fashion brands. These insights range from the lack of data-driven insights to operational challenges such as minimum order volumes and shipping costs. The feedback also highlights positive aspects of the user experience, such as the game-like interface and the usefulness of style recommendations. These findings provide valuable input for refining the brand's strategy, improving the user experience, and optimizing inventory management.

Landing on the Solution

Our software will leverage AI to understand shoppers' needs by analysing past orders and intake quizzes, curating a virtual closet with clothing options that can be tried on virtually using augmented reality technology. Consequently, this software refines user preferences down to the minutiae of colour, size, and style, creating a unique virtual closet tailored to your customers' desires. Storing user preferences in this manner will help mitigate issues related to returns and website abandonment that often arise when customers struggle to find their desired style using manual filters. On the fashion brand's end, we provide a condensed hub to streamline buyer-to-merchandiser workflows, incorporating historical and current data to assess current inventory needs and predict the future growth potential of inventory choices.

Explanation of Solution

AI-Powered Shopper Understanding: The software employs artificial intelligence (AI) to comprehensively understand the preferences of shoppers. It achieves this by analysing their past purchase history and intake quizzes, which may include questions about their style preferences, sizes, and colour choices.

Virtual Closet Creation: After gathering insights from past orders and intake quizzes, the software curates a virtual closet for each shopper. This virtual closet consists of clothing options that align with the individual's specific preferences and needs.

Augmented Reality (AR) Try-On: The software offers an augmented reality (AR) feature that allows shoppers to virtually try on the clothing options from their curated virtual closet. This means customers can visualize how different clothing items will look on them without physically trying them on.

Fine-Grained User Preferences: The software goes beyond basic preferences and refines user preferences down to the finest details, such as color, size, and style. This ensures that the virtual closet is tailored precisely to what the customer desires.

Reduced Returns and Abandonment: Storing user preferences in this detailed manner helps mitigate two common issues in online shopping: returns and website abandonment. When customers can easily find clothing that matches their preferences, they are less likely to return items or abandon their shopping carts due to frustration.

Streamlined Fashion Brand Operations: On the fashion brand's side, the software provides a centralised hub. This hub assists in optimising the workflows between buyers and merchandisers. It achieves this by incorporating historical and real-time data to assess the brand's current inventory needs. Furthermore, it utilises data analytics to predict the future growth potential of various inventory choices.

In essence, this solution aims to offer a highly personalised shopping experience for customers, reducing the barriers to making a purchase. Simultaneously, it assists fashion brands in making data-driven decisions about their inventory management and merchandising strategies, ultimately improving their operational efficiency.

User Flows/Mockups

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