Does Merciv connect to our sell-through and returns data?

Yes — additive, never a replacement. Merciv joins the sell-through and returns data you already track to reviews, fit, and style conversation, so the number and the reason behind the season live in one cited answer.

Can it separate a fit problem from a price problem?

Merciv reads returns and fit conversation next to price sensitivity, cited, so a weak season points at the actual cause instead of defaulting to a markdown plan.

How far ahead of results day can this realistically see?

Merciv doesn’t forecast the quarter — it closes the gap between when a signal exists and when you see it. Reviews, returns, resale, and style conversation are tracked continuously, so a shift reaches you while you can still adjust the buy, not in the post-season read.

Is this social listening? We already have a tool that tells us very little.

Listening shows heat and stops there. Merciv ties that conversation to the buy — fit, returns, resale, competitive gaps — with every claim cited to source, so a merchant gets something to act on, not a sentiment chart.

Do you use synthetic shoppers for assortment reads?

No. Assortment and drop reads are grounded in licensed consumer data, never synthetic respondents, so the evidence reflects what shoppers actually buy and keep.

For fashion & apparel

Consumer intelligence built for fashion & apparel

The why behind the buy, before results day. Sell-through, reviews, resale, and style conversation, connected — cited evidence a merchant can act on while the assortment is still open, not the autopsy after markdowns are set.

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Use cases

The why behind the buy — while it’s still open

Bring the consumer why to line review
Evidence a merchant can act on, while the assortment is still open — not after the buy is committed.
Protect full-price sell-through
Where conviction justifies depth and where it doesn’t, before OTB locks.
Cut fit-driven returns at the buy, not the checkout
Size and fit signal early in the season, where more than half of online apparel returns start.
Read brand heat as a leading indicator
Search, creator, and wishlist signal months ahead of results day.
Use resale as a desirability read
Price retention and time-to-sell on your hero styles, tracked continuously rather than assembled by hand.
Diagnose a slow style mid-season
Wrong moment or wrong style? The answer before markdown cadence locks.
Answer dupe questions
Who your customer is substituting toward, and what they say they’re getting instead.
Test the price before you take it
What your consumer will accept this season, when most of the industry is raising prices at once.

The problem

Results day is the first time you hear why

Sell-through tells you what sold, returns tell you what came back, style conversation tells you the noise, and the reason behind the season is reconstructed after the markdowns are already committed.

Common questions

Executive-ready outputs out of the box

What can this tell me that our own sell-through can’t?
Sell-through tells you a style slowed. It doesn’t tell you whether that was fit, timing, or price — Merciv reads returns, resale, and what customers actually wrote, so the read points at a decision.
Does this replace EDITED or WGSN? We aren’t dropping either.
Neither one. Merciv isn’t built on a syndicated monopoly — it cites trend, returns, resale, DTC, and social into one merchant-ready read that sits alongside the tools your team already trusts.
Will this override my merchants’ judgment?
It gives them something to argue with. Every read is cited back to source, so a merchant can check the evidence and overrule it on the record — the taste stays theirs.

Trend read

Separate the runway signal from the feed noise

What’s compounding versus what’s peaking, with the evidence trail, before the buy is committed — not reconstructed after results day.

Fit signal

Hear fit problems before the returns report does

Sizing and fit complaints at the style level, surfaced from reviews and conversation in week one, so the risk is caught while the order can still change.

Say/Do gap

They say sustainable — do they check out?

Stated sustainability values next to observed purchase behavior — the category’s defining say/do gap, read with sources instead of asserted.

Drop timing

Time the drop with evidence a merchant can use

Demand signal and conversation momentum read together for the moment of release — arms the call, never overrides it.

Case studies

Proof from teams running the same playbook

Read case studies

Built for how fashion & apparel handles data

Visit the Trust Center

Licensed data, retailer terms respected
Built on licensed sources with retailer data terms honored, not scraped or resold
No PII, no individual-level decisions
Category and segment intelligence, never person-level targeting
Sourcing methodology published
How every answer is sourced is documented and reviewable
Every claim cited
Each answer links back to source material anyone can open
Zero training on your data
Your data is never used to train models, yours or anyone’s
SOC 2 Type II
Independently audited controls, cleared for procurement

Request a demo

See Merciv on your category’s data

Bring a category and the season you have to explain. We’ll show Merciv reading the fit, style, and price behind it, cited.

FAQ

Consumer intelligence built for fashion & apparel

The why behind the buy — while it’s still open

Bring the consumer why to line review

Protect full-price sell-through

Cut fit-driven returns at the buy, not the checkout

Read brand heat as a leading indicator

Use resale as a desirability read

Diagnose a slow style mid-season

Answer dupe questions

Test the price before you take it

Results day is the first time you hear why

Executive-ready outputs out of the box

Separate the runway signal from the feed noise

Hear fit problems before the returns report does

They say sustainable — do they check out?

Time the drop with evidence a merchant can use

Proof from teams running the same playbook

How a Leading Athletic Apparel Brand Analyzed 10,000+ Reviews to Uncover the Product Gap Competitors Were Exploiting

How a Fortune 50 Home Improvement Retailer Prevented a ~$12M Overstock with Real-Time Consumer Demand Intelligence

Built for how fashion & apparel handles data

Licensed data, retailer terms respected

No PII, no individual-level decisions

Sourcing methodology published

Every claim cited

Zero training on your data

SOC 2 Type II

See Merciv on your category’s data

Questions fashion & apparel teams ask