Project Problem Summary

Our project addressed a key issue in e-commerce: the preference for in-store shopping's sensory experiences, especially in sectors like groceries and luxury goods. Exacerbated by COVID-19's impact on retail, there was an urgent need to replicate these in-store advantages online. Our focus was to bridge this sensory gap in digital shopping, allowing consumers to make confident online purchases. Given the project's 12-week timeline, our initial challenge was to strategically narrow down the scope to create a feasible yet innovative solution.

Selecting the Market

We narrowed our focus to three target markets: Brazil, China, and India. Our market research centered on three key questions: how consumers in these markets make purchasing decisions, differences in their in-person vs. online shopping behaviors (especially regarding sensory experiences), and their engagement with digital technologies like AR, VR, and AI. After gathering insights from secondary research, we systematically categorized the potential advantages and disadvantages for each market.

Uncovering Assumptions

In the Assumption Slam workshop I led, we scrutinized our beliefs against the data from secondary research. The goals were to verify our assumptions and assess the risks of proceeding with unvalidated beliefs. We listed assumptions about the problem, market, and industry, categorizing and mapping them on axes of known vs. unknown and risk vs. less risk. This exercise laid the groundwork for our primary research, highlighting areas needing deeper exploration and aiding in the formulation of interview questions.

User Interviews

We conducted interviews with individuals in India who fit our target audience profile. Each participant had recent experience purchasing furniture and had shopped both in-person and online. Our interview questions covered a range of topics, from general buying habits to specific preferences in furniture shopping.

Affinity Diagramming

In our affinity diagramming session, we efficiently categorized extensive fieldwork data into themes such as 'sensory experience' and 'purchase journey'. This approach not only clarified our findings but also fostered valuable discussions among team members, enriching our understanding of the research problem.

The Results

Our team developed a personalized quiz app for furniture stores on Shopify, designed to offer tailored recommendations based on users' personal style and room specifics. Users engage with the app either by uploading room photos for AI analysis followed by a few questions, or by completing a design preference quiz. The final output presents aesthetically and functionally curated furniture suggestions. While our solution currently doesn't include AR, it's compatible with existing AR visualization apps on Shopify. The prototype, crafted using Shopify's Polaris design system, ensures a seamless and engaging user experience.

Next Steps and Key Learnings

Looking ahead, our plans involve refining the prototype with user feedback, expanding to desktop-first markets, and adapting the solution for other industries.

Key learnings from this project emphasized the importance of simplicity in design, the benefits of choice reduction for user decision-making, and the value of continuous feedback. We also recognized the potential for earlier user engagement in the process and learned effective techniques for user testing observation and feedback.