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Menswear · Shopify · Meta Ads · Custom apps

Hussars

Selling made-to-measure tailoring online, without losing the fitting room.

Client: Hussars
Sector: Menswear · Ready-to-wear + bespoke
Market: Spain & EU
Role: E-commerce & growth lead (embedded)
Timeline: Sep 2025 – 2026
Scope: Shopify · Custom apps · Meta Ads · Klaviyo · Reporting

Hussars makes heritage-inspired menswear: ready-to-wear jackets and a made-to-measure service. I joined to run the whole digital side: the Shopify store, the bespoke funnel, acquisition on Meta, email in Klaviyo, and the internal tools that keep the numbers honest.

Outcomes

−27%size-related exchanges after the fit finder

2.1×bespoke consultations booked vs. old page

10 hof manual reporting removed each week

3.8×ROAS on warm & hot audiences

The challenge

Two businesses under one roof. Ready-to-wear needs a fast, confident checkout; bespoke needs trust, education and an appointment. The store treated both the same, sizing questions flooded the inbox, ad spend was one big campaign, and reporting meant exporting CSVs every Monday morning.

The approach

Split the journeys, then connect the data. Each path got its own landing, its own campaigns and its own emails, plus one reporting layer that reads them all.

A bespoke funnel that answers “how much?” first

I designed and built the made-to-measure landing as a story with a clear exit: craft → process → fabrics → price → appointment. Interviews with the tailor showed the #1 reason people didn’t book was price uncertainty, so the page tells you before you ask.

The estimator lets visitors combine garment, fabric, lining and construction and see a live price range. It qualifies leads before the call and turns a vague “contact us” into a specific, confident booking.

9 garments × 5 fabrics × 5 linings × 2 constructions
Prices stored in metaobjects, editable without code
Booking pre-filled with the chosen configuration

Hussars · hussars.net/pages/ropa-a-medida — Fig. 01Bespoke landing and price estimator.

A fit finder that answers the question before it’s asked

Sizing was the biggest source of support emails and exchanges. I built a Shopify app block that asks three things (height, weight and how you like your jackets to fit) and recommends a size with a confidence level and a plain-language reason.

Under the hood: an estimate of chest and shoulder measurements, compared against each garment’s flat measurements plus the ease of its cut. Garment data lives in product metafields, so new styles plug in without touching code. Try it:

Acquisition, organized by temperature

The Meta account was one campaign with everything in it. I rebuilt it around how warm the audience is, because you don’t talk to a stranger the way you talk to someone with a jacket in their cart.

New drops go cold with 9:16 video and a hook in the first two seconds. Warm audiences get carousels with more reasons. Hot audiences see dynamic catalog ads, including real low-stock sizes (“last units in your size”): urgency that happens to be true. Pixel and Conversions API were set up first; without them, none of this is measurable.

Cold

60% of budget

Who: Never heard of the brand
Goal: Discovery & desire
What we show: New drops, one hero product
Format: 9:16 video, hook in 2 s
Message: “This exists, and it’s for you.”

Warm

25% of budget

Who: Visited, watched or followed
Goal: Consideration & trust
What we show: A wider edit of the catalog
Format: Carousel, up to 10 cards
Message: “Here’s why it’s worth it.”

Hot

15% of budget

Who: Added to cart, or bought before
Goal: Purchase & retention
What we show: What they viewed; low-stock sizes
Format: Dynamic catalog ads
Message: “Last units in your size.”

Meta Pixel + Conversions API · Product catalog · UTM discipline

Email that knows your size

Klaviyo flows built on the same logic as the ads, with one twist: they use what we know about fit. Back-in-stock alerts are size-specific, and a post-purchase check-in on day 10 asks how the jacket fits, catching exchanges early and feeding the fit finder’s data.

-2dDay 0+1d+3d+5d+10d+30d

Welcome seriesNewsletter sign-up

Brand principlesHow our sizing worksBespoke, explained

Browse abandonmentViewed product, no cart

The piece you looked atFit & fabric details

Cart abandonmentStarted checkout

Still in your bagQuestions about fit?Your size is running low

Post-purchase fit checkOrder delivered

Care guideHow does it fit?Complete the look

Back in stock, by sizeSubscribed to a size

Your size is back

Bespoke appointmentConsultation booked

What to bringSee you todayFabric follow-up

Reporting that writes itself

Every Monday someone exported orders, pasted them into a sheet and tried to reconcile ad spend. I replaced it with a small pipeline: Google Apps Script pulls orders, refunds and inventory from the Shopify Admin API nightly, a Sheets model joins them with ad spend, and a digest lands in the team’s inbox at 8am.

Sell-through by size, return reasons, margin by channel. Ten hours a week back, and decisions made on Monday, not Thursday.

01Shopify Admin APIOrders · refunds · inventory

02Apps ScriptNightly at 02:00 · cleans & joins

03Sheets modelAd spend · margins · sizes

04Monday digestInbox at 08:00

Weekly digest · Week 4108:00

Net revenue€18,420+12%

Ad spend€3,960MER 4.65

Returns4.1%−1.8 pt

Bespoke leads23+6

Sell-through by size · Harrington

S58%

M86%

L79%

XL41%

→ Reorder M before Friday. XL: move to warm-audience carousel.

The store itself

Behind the visible work: catalog and variant clean-up, markets and languages, checkout and payment methods, shipping rules, and a product page that puts fit, fabric and care where people look for them.

Hussars · hussars.net/products/chaqueta-harrington — Fig. 02Product page: fit, fabric and care where people look for them.

Hussars · mobile — Fig. 02Product page: fit, fabric and care where people look for them.

Design decisions

The system behind the screens

A heritage menswear label selling online has to feel like a tailor’s shop, not a fast-fashion feed. Every choice below follows one rule: let the cloth be the color, and let the interface behave like a well-cut jacket: quiet, structured, exact.

Color

#1C1C1C Ink Text, buttons, the dark bar 17.0:1 · AAA

#FFFFFF Shirt The page: photos carry the color 17.0:1 · AAA

#6AAAAA Monogram teal Only the monogram and stars 6.4:1 · AA

#108474 Deep teal Links and focus states 4.6:1 · AA

#EFEFEF Lining Panels, sold-out sizes 14.8:1 · AAA

#7A1F2B Burgundy Campaign color, from the Harrington itself 10.2:1 · AAA

Almost no UI color. The burgundy and navy come from the garments in the photography; the teal monogram is the only brand accent, so it always reads as a signature.

Typography

Instrument Sans400 · 0.18em · caps

Chaqueta Harrington

Headings · product namesWide-tracked capitals echo the woven labels inside a jacket.

Nunito400 · 0

Lana española, forro al tono, half canvas.

Body · descriptions · pricesRounded and warm for long fabric and care descriptions at 14 px.

Grid & spacing

Columns: 12
Max width: 1440px
Gutter: 24px
Base unit: 8px

8-point spacing on a 12-column grid. The product image takes 7 columns, the buying column 5. Never 50/50, so the garment always wins.

Decisions, and why

01
Price estimator before the booking form

WhyInterviews showed price uncertainty was the #1 reason not to book a fitting.

Instead of~~A “contact us for a quote” form that hides the number.~~
02
Fit finder inline, next to the sizes

WhyPeople decide size at the size selector, so help belongs exactly there.

Instead of~~A size chart in a modal nobody opens.~~
03
Monochrome UI, colorful garments

WhyBurgundy and navy jackets look truer next to neutral chrome.

Instead of~~A brand color on every button competing with the product.~~
04
Separate journeys for ready-to-wear and bespoke

WhyOne buys in minutes, the other needs trust and an appointment.

Instead of~~One generic store template for both.~~