7-figure gymwear brand

7-figure gymwear brand: $3,000/month off Klaviyo and 34% more flow revenue — without growing the list

A UK gymwear brand doing £150k/month came to us with an inflated Klaviyo bill, broken segmentation, and a Welcome flow leaking revenue. Four months later, the bill was less than half of what it was, the Welcome flow was generating £13 per recipient, and they were sending to a smaller, cleaner list with better numbers across the board.

$3,010

Klaviyo savings per month (December + March combined)

£13.00

Welcome flow revenue per recipient by Feb (from £8.06 in Dec)

34.3%

flow revenue increase month-over-month in February

The brand

A 7-figure UK gymwear brand doing roughly £150,000/month in sales, with a 7-year operating history and a list of around 105,000 active Klaviyo profiles when we came on. Strong traffic from paid ads, a growing product line, and consistent campaign frequency. The team was running Klaviyo themselves and knew it wasn’t fully dialled in — they wanted someone to take the channel off their plate and tighten the operation.

The leak

The account was technically functional but expensive. Most of the problems traced back to the same root cause: the list and segmentation hadn’t kept up with the brand’s growth.

  • Inflated billing. 105,932 profiles sitting against a 135,000-profile plan, with email sends at 98% of capacity. They were paying for volume they weren’t converting.

  • Every campaign went to the entire list. No engagement-based suppression. Unengaged profiles were dragging down open rates, click rates, and inbox placement.

  • Spam complaint rate above Klaviyo’s recommended threshold. Deliverability was at risk if sending behaviour continued unchanged.

  • Flow library was thin. Welcome and Abandoned Checkout existed but were short and dated. Winback, Post-Purchase, and Review Request were missing or incomplete. No SMS integration in any of them.

  • No zero-party data. Pop-up was capturing emails but not what kind of customer was signing up (gym vs. streetwear) — meaning Welcome content couldn’t be tailored.

The foundation we built

We worked through the core systems in order. Each fix made the next one work better.

List health and segmentation

Created a master Newsletter list to consolidate scattered audiences. Built engagement-based segments (7-day, 30-day, 60-day, 90-day) and customer-journey segments. Ran re-engagement campaigns to unengaged profiles, then suppressed the non-responders. Set up a Sunset flow to keep this process running automatically every month.

Pop-up capture

Split the pop-up form into mobile and desktop variants. Tested timing, copy, and offer presentation independently on each. Mobile submit rate climbed from baseline to 9.43% in January and 9.91% in February — both rated “Excellent” against Klaviyo benchmarks. Adding the gym vs. streets question (zero-party data) didn’t reduce submit rates.

Welcome flow

Rebuilt the Welcome flow with updated copy, design, and sequencing. Added an SMS Welcome flow timed to fire 2-3 hours after the email. The initial Welcome email saw 3x the click rate after the redesign. Revenue per recipient went from £8.06 in December → £13.00 in February → £12.50 in March. The SMS Welcome flow hit an 11.2% click rate and 7.9% placed order rate once corrected.

Core flow ecosystem

Built or rebuilt every major automated flow with combined email + SMS sequences:

  • Welcome (email + SMS)

  • Abandoned Checkout

  • Browse Abandonment (email + SMS)

  • Post-Purchase

  • Winback (email + SMS)

  • Review Request

  • Sunset

By February, automated flows were generating 30% of total email revenue, up from 20% when we started. Total flow revenue was up 34.3% month-over-month in February alone.

Billing

Tighter segmentation immediately reduced the active profile count. After the first month, the Klaviyo plan dropped by ~$2,110 USD/month — less than half of what it had been. In March, continued list cleaning let us reduce the plan by a further ~$900 USD/month. Total monthly savings: roughly $3,000.

The results

Over four months from December 2024 through March 2025:

  • Klaviyo plan cost down ~$3,000 USD/month through list cleaning alone. Same brand, smaller plan, better performance.

  • Welcome flow revenue per recipient: £8.06 → £13.00 — a 61% improvement.

  • Flow revenue contribution: 20% → 30% of total email revenue.

  • Email attribution: 34% → 41% of total business revenue (December vs. March).

  • Mobile pop-up submit rate: 9.91% — rated “Excellent” against Klaviyo benchmarks.

  • SMS list: 14,508 → 22,815 profiles — up 57% in four months.

  • Campaigns to 34% fewer recipients, but 8% more unique opens in December — the cleanest signal that segmentation was working.

Deliverability stayed safe throughout. Open rates held around 41-42% (well above Klaviyo’s 33% recommendation). Bounce rate stayed under 0.40%. Unsubscribe rate stayed under 0.20%. Given the high campaign sending frequency, that’s meaningful — the list was clean enough to sustain it.

Why it worked

The cost savings and the performance improvements came from the same set of decisions. They weren’t a trade-off.

  • Smaller list, same engaged audience. The profiles we removed were the ones never opening, never clicking, never purchasing. Removing them reduced billing and improved deliverability simultaneously.

  • Email signal got cleaner. Inbox providers saw a higher proportion of engaged opens-to-sends, which lifted inbox placement for the entire list.

  • Foundation came before complexity. We rebuilt the core 5 systems (capture, welcome, abandonment, post-purchase, winback) before adding SMS coordination or zero-party data. Each layer worked because the one underneath it was solid.

  • The Welcome flow alone justified the engagement. A flow generating £13/recipient is the result of a properly structured sequence sent to people who just opted in. Once that mechanic was working, every new subscriber from the pop-up improvements compounded.

Book a call

Book a 30 minute call so I (Nick) can understand what you’re looking for, and if I can help.

More case studies