Whatnot: Competitive Benchmark Research Study
Provided three evidence-backed opportunities to redesign desktop seller signup to increase GMV.
Whatnot wanted to grow GMV through desktop sellers, but its signup was optimized for mobile, not for desktop sellers.
Context: Whatnot, a mobile-first live marketplace, saw the new-seller journey (signup, Go-Live, first sale) as a key lever for GMV.
Why desktop: desktop sellers are more goal-driven but appeared to drop off more than mobile sellers, an assumption flagged for validation.
How I scoped it
Solopreneurs, who contribute the majority of GMV and match Whatnot’s hobbyist, community-driven identity
Signup, the foundational flow that shapes seller readiness and engagement
What could desktop-first marketplaces teach Whatnot about preparing first-time solopreneurs to sell?
Defining the benchmarking lenses: deductively, from AI-assisted secondary research, for consistency; inductively, from themes that emerged across signup flows. Validated against a first-time seller user story.
Minimal friction
Step count, login options, autofill, redundant fields
Clear structure
Progress, stage grouping, feedback, back navigation
Contextual guidance
Inline help, input examples, error correction, FAQs
User segmentation
Branching and tailored copy for seller types
Trust & activation
Verification, fee transparency, payout, confirmation
Benchmarked three desktop-first marketplaces, mapping their signup flows and rating them on 18 parameters to identify opportunities for Whatnot.
Why secondary research: the team needed quick insights within a one-week timeline.
Competitor selection
- Screened 11 marketplaces against 6 criteria
- Desktop-driven, live selling, peer-to-peer, community, new sellers, niche items
- Selected eBay, Etsy and Drip for alignment with Whatnot’s model
User empathy with story
- Wrote a first-time seller user story to validate benchmark points
- Sellers want few steps, questions that fit them and to know why data is needed
- Progress, save-and-resume and confirmation build confidence
Analysis
- Mapped each competitor’s signup flow step by step
- Rated 18 parameters across 5 lenses: high, medium, low or deferred
- Kept qualitative notes per rating in a benchmark spreadsheet
“I just want the signup to be quick and make sense… It helps when the platform tells me what it needs and why, instead of asking for things like ID or bank info out of nowhere.”From the first-time seller user story
Whatnot made account creation simple but deferred the setup and trust-building that competitors addressed earlier in the seller journey.
- Landing page
- Seller account
- Category & subcategory
- Seller separation
- Seller details
- Intro video
- Dashboard
- Landing page
- Seller account
- Category & subcategory
- Seller separation
- Product details
- Account confirmation
- List product
- Dashboard
- Landing page
- General account
- Seller account
- Seller separation
- Shop personalization
- Seller details
- Payout
- Identity verification
- Shop paywall
- Offers
- Dashboard
- Landing page
- Seller account
- Account use
- Product category
- Personal details
- Payment
- Identity verification
- Dashboard
Listing, payout and verification steps. Competitors collect them at signup; Whatnot defers them.
| Parameter | Whatnot | Etsy | eBay | Drip |
|---|---|---|---|---|
| Minimal friction | ||||
| Step count & layout simplicity | High | High | Low | Med |
| Google/Apple login | Med | High | Low | Low |
| Autofill options | High | Med | High | Low |
| No redundant fields | Med | Med | High | Low |
| Clear structure | ||||
| Progress indicator | Med | High | Low | Low |
| Logical stage grouping | Med | Med | Low | Med |
| Feedback between steps | High | High | Low | Med |
| Back navigation without reset | High | Low | Low | Med |
| Contextual guidance | ||||
| Tooltips & inline help | Low | High | High | Low |
| Example input formats | High | Med | High | Low |
| Inline error correction | High | High | High | High |
| FAQ / help link presence | Med | High | Low | Low |
| User segmentation | ||||
| Branching for seller types | High | High | High | Med |
| Tailored copy & flow | Med | High | High | Low |
| Trust & activation | ||||
| Secure verification (KYC/email) | Deferred | High | Deferred | Med |
| Transparency on data use & fees | Med | High | Low | Low |
| Smooth payout setup | Deferred | High | Deferred | Deferred |
| Confirmation state | High | High | High | Low |
An opportunity to shift desktop sellers to a multi-step signup
eBay, Etsy and Drip collect identity verification, payout setup and initial listing details during signup; Whatnot defers these to later stages.
- Collect these details during desktop signup to reduce friction and cognitive load at Go-Live, smoothing the path to first listing.
Trade-off: more steps may raise signup drop-off but could lift activation and time-to-first-listing downstream.
Whatnot asks for a product category without saying why
Etsy is fully category-agnostic; eBay goes further and prompts the first listing during signup.
- Remove the category question to cut a step, or
- if drop-off is steep after signup, capture the first listing in detail during signup and keep only Go-Live afterwards.
Trade-off: the second option adds steps up front, trading early drop-off for downstream activation.
Etsy builds trust during signup; Whatnot delays it until after
Etsy uses positive microcopy, microinteractions and always-accessible FAQs early in the flow.
- Add reassurance, visible help and microinteractions to reduce anxiety.
- Surface fees, data use and why each step is needed up front.
Competitive research set a direction; next, I’d test with real sellers.
Next phase
- Primary mixed-methods research with real sellers completing at least two of signup, Go-Live and first sale.
- Measure completion time, success rate and drop-off, plus ease, trust and clarity.
If I had more time
- Extend the study to the Go-Live flow, where most seller drop-off appears to occur, based on a stakeholder conversation.
Limitations
- This phase relied on secondary data and a proxy user story, not research with real sellers.
- Higher desktop drop-off was a surmise, to be validated with a data scientist before proceeding.
- Three competitors, rated by a single researcher: patterns, not benchmarks.