Social Commerce —
Revolutionizing E-commerce: A Human-Centric Approach with AI Assistance
Revolutionizing E-commerce: A Human-Centric Approach with AI Assistance
Role
UX Research, Interaction, Visual design, Prototyping & Testing
Date
2023
Tool
Figma
Overview
As the digital landscape undergoes rapid evolution, AI SocialShop stands at the nexus of e-commerce innovation and social media vibrancy. Marrying the sophisticated intelligence of AI with the unfiltered authenticity of user-generated content, this e-commerce platform aspires to not only facilitate shopping but to revolutionize the very essence of online buying. Our ambition? A harmonious blend of AI-curated product experiences, user and influencer-driven video presentations, and an AI haggle mechanism that epitomizes the future of shopping
Background
E-commerce has become an indispensable part of the modern shopping experience. However, like all industries, it is not without its challenges. Several users of prominent e-commerce platforms like Jumia, Konga, M&S, ASOS, eCrater, Vevor, and OnBuy have expressed various pain points which highlight the gaps in the current offerings. One innovative solution to address some of these challenges is the introduction of an AI-driven haggle feature, simulating the real-world bargaining experience but in the digital realm.
Objective
To craft an e-commerce haven that resonates deeply with the millennials and Gen Z, infusing cutting-edge AI capabilities with robust social media integrations. The cornerstone of AI SocialShop is its emphasis on user-centric content and experiences, ensuring every shopping journey is dynamic, engaging, and unmistakably unique.
Features Blueprint
AI-Powered Curator
Uses intricate algorithms to analyze user preferences, offering curated product showcases.
Offers insightful product backstories, deepening user-product connection.
AI-Driven Recommendation
Mining user interactions to present finely-tailored product suggestions.
Inclusive user feedback loop ensuring the AI's evolving accuracy.
AI Haggle Mechanism
A first-of-its-kind feature allowing users to 'negotiate' prices, ensuring the thrill of the deal.
Ensures fairness, adjusting offers based on product demand, stock levels, and user purchase history.
Generative AI Displays
Dynamic visualization of products adapting to diverse scenarios, settings, and body physiques.
Video-Centric Product Showcases
Amplifying user and influencer voices through video narratives, coupled with direct purchase pathways.
Instilling trust via a rigorous content verification framework.
Process
Using a user-centric approach, we identified needs, prototyped solutions, and refined through feedback, ensuring an intuitive and engaging experience.
Overview
Immersion - Competitive Analysis
Questionnaires and Interviews
Self-Documentation
Meetings
UX Observation
Immersion into the World of E-Commerce
Ventured into online shopping communities, closely observing and participating in buyer-seller interactions.To gain a more holistic understanding of the e-commerce landscape, I embarked on an immersion journey into multiple platforms: eCrater, Konga, M&S, ASOS, Jumia, Vevor, and OnBuy. By navigating these platforms as a user, examining their user interfaces, functionalities, and overall user experience, I gathered firsthand knowledge of the strengths and weaknesses each platform presented.
Competitive Analysis of eCrater & Jumia
We undertook an in-depth analysis of customer feedback and reviews for both platforms to gain insights into their strengths, areas of improvement, and overall user experience.
This table presents a streamlined, at-a-glance breakdown of both Jumia and eCrater in terms of their strengths, drawbacks, and neutral aspects. Such a comparative structure is immensely useful when assessing platforms side by side, highlighting where each shines and where they might fall short.
Survey & Questionnaires
Our intent to deeply understand the online shopper's psyche led us to launch a far-reaching survey. The response was nothing short of phenomenal. With over 2,300 responses, it was evident that users were enthusiastic about shaping the future of e-commerce. Their detailed feedback, taking an Average Completion Time of 12:09, unveiled layers of their digital shopping desires, frustrations, and aspirations.
Comprehensive Interviews
We continued on the journey, engaging five diverse participants in comprehensive interviews about their online shopping habits, their experience with AI in e-commerce, and their thoughts on an AI-driven haggling feature. The feedback that emerged not only helped validate some of our foundational beliefs but also insights previously uncharted territories of user needs and aspirations. Here's a condensed insight
Interview Summary
Drowning in insights, patterns began to solidify. One clear takeaway? Users yearned for a shopping platform that wasn't just transactional but conversational. They sought a space where interactions felt real, even if with a digital entity.
Who are the respondents?
Profiles:
Office worker from San Francisco
Software engineer, aged between 28-33
Entrepreneur from Lagos, aged 28
Product Designer named Dee
Digital Marketer aged 30+
How often do they shop online?
Frequency:
Mostly weekly, with the Digital Marketer shopping monthly.
What are they buying, and where?
Items of interest: Gadgets, clothing, fashion items, electronics, accessories, and sometimes groceries.
Popular platforms: Amazon, Jumia, Konga, AliExpress, local Instagram shops, Walmart, and Target.
Factors influencing their purchase decisions?
Primary considerations: Reviews, product images, product demonstration, return policy, brand reputation, and price.
Negotiation in Online Shopping
Experience with price negotiation: A mix, some have negotiated in physical stores, but not online.
Receptivity to online negotiation: Most seem receptive and find the idea of AI-assisted negotiation intriguing, equating it to the local market experience.
Expectations from an AI-driven negotiating system:
Desired Feature: Understanding of the user's budget, relevant product suggestions, knowledge of local terms, a sense of humor, and ensuring privacy.
Concern: Some are wary of pushy AIs, others raise concerns about privacy.
Preferred Mode of Communication: A mix of different styles, with humor being a notable preference.
What frustrates them about online shopping?
Pain Point: Slow website speeds, hidden charges, lack of product details, delivery delays, receiving products different from what's shown in pictures, and payment issues.
Memorable online shopping experiences?
Positive Experiences: Amazon's one-day delivery surprise, Jumia's Black Friday deals, high-quality products at great prices, quick delivery times, and personal experiences with sellers.
How do they feel about sharing data and incentives?
Incentive for Sharing Data: Free shipping, exclusive deals, and discounts. However, a few respondents aren't comfortable sharing data.
Feedback for Improvement: Easier return policies, more tech like Augmented Reality (AR), improved customer service response times, more use cases, and better price negotiation tools.
Thoughts or experiences on AI in shopping?
Opinions: Positive feedback about having AI for negotiation. They believe it's a game-changer and would love to see it in action, equating it to real-world haggling.
Interpretation
People from diverse professional backgrounds shop online frequently, primarily for gadgets, clothing, and other electronics. They rely heavily on reviews, product demonstrations, and the platform's reputation. The idea of having an AI-driven negotiation tool in e-commerce platforms is welcomed, with the hope that it mirrors real-life haggling. Concerns center around potential pushiness of such systems and privacy issues. While there are notable frustrations with online shopping, especially around product deliveries and hidden charges, there are also moments of delightful customer experiences, such as unexpected fast deliveries or exceptional Black Friday deals. The opportunity lies in addressing these pain points, enhancing the shopping experience with innovative tech like AI and AR, and ensuring transparent communication, especially around data privacy.
Define
Identification of Theme
Story Telling
Sorting and Condensing
Definition of Findings
Framing Opportunities
Identification of Theme - Unraveling the Threads
5 Ws for the AI-Driven Haggling Feature
How Might We (HMW) Questions:
HMW make the AI haggling process feel more human, ensuring users find it relatable?
HMW design a system that educates users about AI haggling while they're using it, ensuring a learning-while-doing experience?
HMW foster a sense of control for the user, allowing them to set parameters or influence the AI's haggling strategy?
HMW integrate feedback mechanisms seamlessly into the shopping experience, ensuring continuous user insights without disrupting their journey.
HMW design the AI's interactions in such a way that they're adaptable to the user's shopping habits and preferences, ensuring a tailored experience every time?
Story Telling - User Narrative
Chioma, the Busy Entrepreneur in Lagos
Balancing between her startup and personal needs, Chioma relies on Jumia and Konga for her weekly shopping. She yearns for an online experience reminiscent of a local market, where prices are negotiable. A high-speed delivery, like Jumia’s Black Friday deals, always wins her over.
Rohan, the Spontaneous Digital Marketer
Rohan doesn't stick to patterns. He shops when he spots a good deal on platforms like Aliexpress or Jumia. His key pain point? Payment issues. But the thought of a user-friendly interface that mimics a human seller makes him optimistic about the future of online shopping.
Dee, the Meticulous Designer
For Dee, online shopping isn't about frequency, but necessity. When local stores fall short, Konga and Aliexpress come to her rescue. She deeply values the human touch in shopping and wishes for an AI that mirrors the empathy of a physical store seller.
David, the Global Shopper
David, a software engineer in his late twenties, weekly sources unique items from AliExpress. He trusts products with ample reviews and a clear return policy. While he finds a lack of product details irksome, the thought of having an AI-driven negotiating feature piques his interest.
Jenna, the Tech Enthusiast from San Francisco
Jenna is a 30-year-old office worker. Every week, she relishes the thrill of hunting down the latest gadgets online. Amazon’s Prime membership ensures her purchases land swiftly at her doorstep. She values one-day delivery, a testament to Amazon's service. Yet, she wishes for more personalized deals based on her shopping patterns.
Definition of Findings
Most respondents appreciate fast delivery and are drawn to platforms that offer this. Brands like Amazon and Jumia are notable for this.
There's a general excitement about AI-driven negotiation features on e-commerce platforms. However, there's also a concern about these features becoming just another marketing gimmick.
Shoppers want the AI to be relatable, understand local terms, and not be too pushy.
Slow website speeds, hidden charges, and lack of product details are major turn-offs.
Shoppers value their privacy and are hesitant about sharing data with third parties.
Free shipping, exclusive deals, and discounts are the most desired incentives.
Framing Opportunities: For Innovation and Problem-solving
AI Negotiation Tool: Develop an AI tool that facilitates genuine price negotiations, ensuring users feel they're getting true value for their money.
Enhanced Product Details: Invest in providing exhaustive product details, including use-cases not typically listed, to minimize return rates and improve user satisfaction.
Localized AI Interactions: Ensure the AI understands and uses local terms and idioms, bridging the gap between tech and tradition.
Robust Payment Systems: Streamline payment processes to eliminate glitches, ensuring a smooth checkout experience.
Tailored Shopping Experiences: Using data analytics, craft personalized shopping experiences offering tailored deals and product recommendations based on user preferences and shopping history.
Design
Set Design Challenge
Brainstorm
Jam
Designing
Set Design Challenge
Given that users are overwhelmed with information, how might we curate and deliver only the content that aligns with a user's preferences without making them feel boxed into an echo chamber?
Brainstorm/Jam - Unleashing Creativity
Journey Through AI Curator: A Visual Map of User Touchpoints — From First Interaction to Seamless AI Conversations.
Revolutionizing the AI Curator Experience: Blending Learning, Integration, and User Feedback for a Tailored User Journey.
Dive into the Digital Marketplace: An Intuitive Flowchart Illustrating the AI-Driven Haggling Journey, from Product Selection to Feedback. Experience modern shopping with a twist
A Seamless Buyer's Journey with AI Integration
From the initial step of Entry & Onboarding where users can sign-up and get acclimated through intuitive tutorials, to Profile Personalization that tailors the experience based on users' preferences and social data, the buyer is placed at the forefront. As they navigate to Discover & Browse, AI-driven recommendations guide their journey, ensuring a blend of trending and personalized product suggestions. The Purchase & Checkout phase simplifies the buying process with a streamlined cart review and intuitive checkout steps. Finally, the Post-Purchase stage fosters continuous engagement, offering tracking, feedback mechanisms, and a dedicated space for rewards and referrals. Throughout, the Primary Navigation ensures that essential features are within easy reach, while the Secondary Navigation offers supportive functionalities like search and support. This flow captures a harmonious blend of user-centric design and cutting-edge AI integration.
Designing
Homepage (Post login)
Product Page (Generative AI Demonstration)
AI Curator
Settings (Tweak to perfect)
AI Bargain Arena
Finalised Bargain
Evolve
Performance Metrics
Technological Enhancements
Feature Updates
Market Trends Adaptation
Long-Term Vision
Performance Metrics
To measure the success of our AI-driven haggling feature, the following KPIs could be considered post-launch:
User Engagement: Tracking how often users engage with the haggling feature.
Conversion Rate: Monitoring the percentage of haggling instances that lead to successful transactions.
Customer Satisfaction: Using post-interaction surveys to gauge user satisfaction with the haggling process.
Haggling Efficiency: Measuring the average time it takes to complete a haggle.
Price Improvement: Assessing the average discount users achieve through haggling versus standard pricing.
Technological Enhancements
Natural Language Processing (NLP) Improvements: To make the haggling interaction more intuitive and human-like.
Machine Learning Optimization: To refine the algorithms for better deal predictions and user experience.
Data Analytics: To gather insights from user interactions and improve the feature's efficiency and effectiveness.
Feature Updates
Additional enhancements could include:
Behavior-Driven Bargaining: Tailoring haggling strategies based on the user's past behavior, making the AI more adept at recognizing when to push for a better deal or accept an offer.
Sentiment Analysis Integration: Incorporating sentiment analysis to better gauge user satisfaction and adjust haggling techniques in real-time.
Customizable Haggling Profiles: Allowing users to set predefined haggling preferences for automatic or semi-automatic negotiations, saving time and effort.
Contextual Learning Algorithms: Enhancing AI to consider the context of the purchase, such as urgency, item rarity, or personal importance, to adjust haggling strategies accordingly.
Transparent AI Decisions: Offering users insights into the AI's decision-making process, thereby building trust through transparency and control.
Integration Points
Seamless integration with payment systems to ensure smooth transaction completions post-haggling.
Enhancing compatibility with other shopping platform features, such as wishlists or alerts when a haggled item’s price drops.
Long-Term Vision
Customized AI Negotiator: Tailoring negotiation strategies to individual user profiles.
Predictive Haggling: Implementing predictive analytics to suggest items to users that they may want to haggle for.
Integrated Marketplace: Allowing users to use the haggling feature across multiple vendor platforms within the app.
What’s next?
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