Problem Space
Online shopping offers convenience, but uncertainty in sizing remains a significant challenge for H&M shoppers, often leading to hesitation, abandoned carts, and high return rates. Many customers struggle with choosing the right size, as standard size charts lack personalization and do not account for individual body variations. Despite existing size guides, shoppers often rely on trial and error, resulting in frequent returns due to poor fit.
Problem Statement:
How can H&M online shoppers have a better experience when selecting clothing using AI, thereby optimizing online sales and reducing return rates?
Problem Background :
Many H&M online shoppers struggle with finding the right size when shopping online. Inconsistent sizing across different clothing categories leads to uncertainty. Returns due to poor fit increase costs for both customers and the company.
Research Insights:
50% of surveyed shoppers returned items due to sizing issues. 100% of respondents want to see how clothes will fit before purchasing. 87.5% prefer AI-driven size recommendations, but privacy concerns exist. AI accuracy and trust are key barriers to adoption.
User Pain Points:
Using H&M online shoppers, we identified users and received seven survey responses, where participants exclusively completed user surveys due to time constraints. Each user answered 19 key questions along with two bonus questions regarding their willingness for follow-up and suggestions on improving the online shopping experience at H&M through AI-driven solutions.
Feedback:
Our preliminary user research to validate this problem with H&M online shoppers in Canada found that:
- Five out of eight respondents stated that price plays a major role in their decision to shop at H&M online.
- Three out of eight participants reported sizing issues, and four had returned items due to poor fit, indicating challenges with size accuracy and consistency.
- All eight respondents (100%) emphasized the importance of seeing how clothes fit on their body, highlighting a strong need for improved visualization tools.
- Seven out of eight participants expressed interest in personalized size recommendations based on body measurements, showing a high demand for AI-driven sizing solutions.
- While all eight respondents (100%) are open to uploading their measurements for a virtual fitting tool, four expressed privacy concerns regarding photo uploads, underscoring the need for secure and transparent data handling.
Landing on the Solution
Based on the research conducted and the survey results gathered, we identified key features to focus on for our proposed solution, including:
- AI-Powered Size Recommendation
- AI-Driven Overlay for Fit Visualization
- Fit Preference Customization
- Personalized Recommendations Based on Past Purchases
- Optional Photo Upload for Privacy-Conscious Customers
Explanation of Solution
Feature
Description
Benefits
AI-Powered Size Recommendations
Uses customer-inputted body measurements (height, weight, and fit preference) to suggest the most accurate size.
Recommended size is based on trends of other customer purchases of similar size, and data from returns based on issues with sizing.
Reduces size-related uncertainty and increases shopping confidence.
Minimizes guesswork, leading to fewer sizing-related returns.
AI-Driven Overlay for Fit Visualization
Generates a virtual representation of how clothes will fit based on user measurements.
Allows customers to see how clothes fit before purchasing.
Fit Preference Customization
Allows customers to select their preferred fit (tight, regular, or loose) to adjust size recommendations.
Customers can see insights from customers on how they found the fit of the clothing in an AI summary.
Gives customers control over how they want their clothing to fit.
Reduces dissatisfaction due to fit preference mismatches.
Personalized Recommendations Based on Past Purchases
Learns from previous purchases and returns to refine future size suggestions.
Continuously improves recommendation accuracy over time.
Optional Photo Upload for Privacy-Conscious Customers
Customers can choose to upload a photo for AI to analyze fit while ensuring privacy controls.
Allows customers concerned about data privacy to opt out of facial recognition.
Provides an additional layer of accuracy in fit recommendations.
User Flows/Mockups:
Link to User Flow: User Flow H&M SmartFit
Customer Insights
Customers highly value accurate size recommendations but remain concerned about sizing inconsistencies across different clothing categories. Many desire visualization tools to see how items fit before purchasing. However, privacy concerns regarding photo uploads highlight the need for secure and transparent data handling. Trust in AI accuracy is also a key factor, as some users are skeptical due to past inaccurate recommendations. Additionally, while customers appreciate personalized size suggestions, they want greater control over their data usage.
Future Steps
Possible Additional Problems to Address:
- Incorporating Augmented Reality (AR) Features: Explore virtual try-on technology to provide more interactive and realistic size visualizations beyond 2D overlays.
- In-Store SmartFit Integration: Extend the feature to H&M physical stores, allowing customers to scan items and receive instant AI-powered size recommendations while shopping in person.
Learnings
Product Manager Learnings:
Dooshima Nenchi
Participating in this Co.Lab AI Product Management course has enabled me learn the following:
- Balancing Innovation with Usability – While AI-powered solutions offer great potential, I learned that user trust and adoption depend on transparency, accuracy, and ease of use, making it essential to balance cutting-edge features with intuitive design.
- User-Centered Problem Solving – Conducting research and analyzing survey data helped me understand the importance of addressing real user pain points, such as sizing inconsistencies and privacy concerns, when developing AI-driven solutions.
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.