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Field & Track

Case studies

What the work
actually looks like.

Three representative engagements, each structured the same way: the situation, what we built, and the result.

Representative engagements based on the work we do and the results this work typically produces.

Case 01

Connecting website behaviour to bookings

~20–30%

improvement in new-member LTV-to-CAC within two quarters

  • Attribution
  • BigQuery
  • Meta
  • Google

Situation

A studio was spending across Meta, Google, and referral but couldn't tell which channels produced members who stuck around, not just which produced sign-ups. So budget kept flowing to the cheapest lead, regardless of whether those people stayed.

What we built

We tied front-end CMS and website behaviour to the back-end booking platform, creating a single view from first website touch, to first class, to lifetime value. Every channel could now be judged on the members it produced, not the clicks.

Result

Spend was reallocated away from the cheapest-lead channel toward the highest-LTV channel.

Case 02

Automated Mariana Tek sync for live dashboards

~3–5×

efficiency in reactivation spend vs. cold acquisition

  • Data Sync
  • Mariana Tek
  • Klaviyo
  • Snowflake

Situation

A multi-location operator was exporting data from Mariana Tek by hand every month. Dashboards were always stale, and ad audiences were built manually, late, and inconsistently across locations.

What we built

Automated, ongoing sync from Mariana Tek into a warehouse feeding live dashboards, plus auto-refreshing Meta and Klaviyo audiences, including lapsed members, high-value members, and trial non-converters, rebuilt on a schedule.

Result

The manual export work disappeared, dashboards stayed live, and reactivation spend went to audiences that were always current.

Case 03

Pre-churn detection & intervention

~15–20%

reduction in voluntary churn among flagged members

  • Lifecycle
  • Klaviyo
  • Mariana Tek

Situation

Cancellations always arrived as a surprise. By the time a member emailed to cancel, they were already gone, mentally checked out weeks earlier, with no signal anyone acted on.

What we built

An attendance-decay signal that flags members whose frequency is dropping (say, 3×/week down to 1×/week), feeding an automated win-back sequence triggered before cancellation, not after.

Result

At-risk members were reached 3–4 weeks before they would have cancelled, while there was still a relationship to save.

Recognise your studio in any of these?

Most owners see at least one. Let's talk about which one is costing you the most right now.

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