TikTok Shop
Redesigning the content driven shopping journey to improve conversion and trust (EU/US)
Context
Optimize TikTok Shop’s shopping path for content recommendation scenarios in European and American markets
This project just focuses on the shopping path from Short Video to Product Detail to Checkout. To optimize conversion efficiency in this short-video-driven journey, I reviewed TikTok Shop’s current customer journey, conducted user tests, and analyzed competitors. Based on these insights, I redesigned key parts of the path — including “Find Similar,” the product page’s first-screen information architecture, and the reviews section.
Business Validation
The business goal is to increase GMV in short-video-driven shopping scenarios
I run A/B tests to track key metrics across the Acquisition, Consideration, and Checkout stages, assessing whether the new design improves user trust, clarity, and purchase confidence — ultimately enhancing conversion efficiency from short videos to payment and increasing GMV in short-video-driven shopping scenarios.
10%
CTR from short videos → PDP
10%
PDP conversion rate
5%
Average Order Value (AOV)
GMV = Traffic × Conversion Rate × Average Order Value
Research Insights
The shopping journey can be divided into three key stages, each with friction that impacts conversion
To better understand user behavior and experience barriers, I conducted desk research, flow mapping, and usability testing. The findings showed that TikTok Shop’s short-video-driven shopping journey can be divided into three key stages: Acquisition, Consideration, and Checkout, supported by four classic shopping paths. Each stage reveals distinct behavioral patterns and friction points that directly impact conversion efficiency and user decision-making.
Current shopping path for content recommendation scenarios
Methodology
Mapping the AIDA Marketing Model onto the Conversion Funnel to guide design strategy
I applied the AIDA Marketing Model and mapped it to TikTok Shop’s Acquisition, Consideration, and Checkout stages, allowing me to define clear design objectives for each phase and identify where improvements could most effectively drive conversion.
Problem 01
In the acquisition stage, users can’t clearly access reliable information to decide which product to buy
In the acquisition stage, the biggest problem is information recognition. Users have already shown interest in a certain product and used the “Find Similar” feature to search, but they can’t find clear and reliable information to determine which product they should buy with confidence.
Before
Solution 01
Enhance product discoverability and clarify the content intent
By introducing a lightweight quick filter based on user mental models and displaying a set of trustworthy indicators, users can quickly identify which product they could buy. This reduces cognitive load and encourages confident click-throughs to product detail pages.
Before
After
Why Important?
In interest-based e-commerce, where decisions are emotion-driven and instant, the lack of trust signals or guidance often leads to drop-offs and purchase abandonment.
Problem 02
In the consideration stage, users can’t quickly find the key information they need in the review section
In the consideration stage, the main issue is the review section. Users can’t quickly get the key information they need, often feeling the reviews are inauthentic and hard to filter. Since reviews are crucial for their decisions, the design must make this information easier to find and trust.
Before
Solution 02
Enhancing review clarity and credibility for confident decisions
Redesigned the review layout to enhance credibility, readability, and engagement. Introduced verified-buyer indicators, selectable keyword tags, and a highlight feature to help users quickly access trustworthy information and reduce potential drop-offs.
Before
After
For the Highlight feature, I explored two options: AI-generated summaries for quick sentiment understanding, and featured photo/video reviews that surface top visual content with short autoplay to boost engagement.
Option 1
Option 2
Based on my ICE Scoring calculation (ICE = (Impact × Confidence) / Effort), I recommend prioritizing Option 1 (AI Summary) for testing, as it more effectively addresses the core issues of trust and information recognition with higher impact, greater confidence, and relatively lower implementation cost.
Problem 03
In the checkout stage, the “Also Ships Free” section fails to engage users
At checkout, the ‘Also Ships Free’ section offers little decision value. It focuses on free shipping rather than suggesting items that are useful or compatible with what users are buying. Since the carousel shows random, unrelated products, it feels generic and promotional, lowering trust instead of driving cross-sell.
Before
Solution 03
From generic ‘Also Ships Free’ to contextual checkout cross-sell to boost AOV
Replaced the generic ‘Also ships free’ carousel with a contextual Frequently Bought Together module tied to the specific item being purchased. The scenario-based copy makes recommendations feel more relevant and helpful at checkout, boosting cross-sell potential and average order value.
Before
After
Other Highlight
Optimizing First-Screen information hierarchy for clarity and trust
Reconstructed the first-screen information architecture based on usability testing insights to minimize visual noise, clarify information priority, and enhance users’ perception of the key signals they care about.
