An equipment-rental company started measuring quotes instead of clicks with a rebuilt GTM container

Representative examplesEvery case study in this library is an illustrative composite of the kind of engagement we deliver — written to show our method and standards, not to name clients.

Where they started

Heavy-equipment rental is a quote-first business: fleet availability lives in one legacy system, quotes go out by email from a quoting tool older than the website, and bookings close days later over the phone with an account manager. The marketing team sits upstream of all of it, running campaigns for excavators, telehandlers, and scaffold packages to a site whose job is to start those conversations. The analytics estate reflected the company's history — a tag container grown over years by multiple agencies, layers of legacy scripts, and reporting that counted what was easy to count.

What it was costing

The rental site's 'conversions' were page views; real quotes went out by email from a legacy quoting tool, so marketing optimized traffic to pages that never produced a quote, and the container itself had 180 tags of accumulated history nobody dared delete.

What they could see

• The tag container held years of accumulated tags from defunct campaigns, and no one could say which ones still fired.
• Marketing optimized toward pages that never produced a quote, because quotes lived in email and never reached analytics.
• The reported conversions were page views — brochure pages, contact pages — that shifted meaning whenever the site changed.
• Sales counted a quote differently than marketing counted a lead, and both argued from the same spreadsheet.

The constraints we worked inside

• The quoting tool predates the web analytics era and fires no events; quotes must be inferred without touching the tool.
• The existing container cannot be rebuilt in place during business hours — the estate must be migrated, not abandoned.
• Sales defines a qualified quote differently than marketing defines a lead; both definitions must coexist.

What had been tried before

Marketing had declared spec-sheet downloads and contact-page views to be conversions, so the ad platforms had something to optimize toward.

Downloads and page views correlate weakly with quoted jobs; budgets drifted toward content that attracted students and job-seekers, not site managers who rent equipment.

The team once began a container cleanup in-house, deleting tags they believed were dead until a checkout page lost its tracking for a week.

Without an inventory tying each tag to its owner and purpose, deletion is guesswork; after the incident, the container froze and every addition since had been permanent.

The owner asked the quoting tool's vendor for an API or webhook so quotes could reach analytics directly.

The installed version is two major releases back, and the vendor quoted a custom build on a platform scheduled for retirement — more than the whole measurement project would cost.

What we proposed

We proposed an archaeology-then-migration plan: inventory every tag, disposition each as keep, replace, or retire with the list signed off, then migrate to a clean server-side container — never rebuild in place on a live estate. For the missing conversion, we proposed deriving quote events from the quoting tool's own notification emails, parsed at the mail gateway the company already runs, so the tool itself is untouched. Marketing leads and sales-qualified quotes would be distinct events in one naming scheme, letting both teams argue from the same data. The reasoning: the business's real conversion already exists — as an email — and infrastructure should observe it, not interrupt it.

Just as important is what we ruled out, and why:

• Replacing the legacy quoting tool with a modern web-quote product — A multi-year operations decision with fleet-system integrations and account-manager retraining attached; piggybacking measurement on it would have tied analytics to a procurement cycle.
• Abandoning the old container and starting a fresh client-side one in parallel — Cleaner on paper, but the estate's legacy tags carried remarketing audiences and consent behavior nobody fully understood — silent retirement risked breaking live campaigns mid-flight.

How the work ran

Delivered by the growth pod — analytics specialist over 5 weeks, with working increments reviewed with the client every week.

The stack, and the reasoning

Google Tag Manager (server-side)

A server-side container sits between browser and vendors, which fits a company that cannot risk page-level breakage — tags fail in the pipeline, never on the site.

GA4

The marketing team already knows its reports, and the two-definition scheme maps cleanly to GA4's event naming, so training was measured in hours.

Mail-gateway event derivation

The quoting tool fires no events and cannot be modified, but it emails every quote — parsing those notifications observes the real conversion without touching a legacy system.

BigQuery

Quote events derived from email need a durable home with history; the warehouse also lets sales and marketing keep separate definition views without duplicate tracking.

Migration inventory

The signed-off inventory — keep, replace, retire — is what made the migration defensible; without it, the same cleanup had already failed once and would fail again.

What went wrong

How we worked together

Cadence

Mondays for a 30-minute working call with the marketing manager; sales joined twice for the definition sessions; a written migration status note went out every Friday.

Client side

The marketing manager drove adoption and sign-offs; the operations director supplied mail-gateway access and guarded the quoting tool's downtime window; the previous agency remained for legacy questions.

Decisions

The keep, replace, retire list was decided in one two-hour session with marketing, sales, and IT in the room — argued once, signed, and never reopened.

They provided

Mail-gateway credentials and a sandbox mailbox, read access to the quoting tool's notification traffic, tag manager admin, and the sales team's definition of a qualified quote.

What changed

The headline: quotes measurable as conversions end to end, on a migrated container with a signed-off tag inventory — Page-view conversions → quote events, read from Container migration log. A second check: tags in the production container after disposition at 180 → 44.

Marketing and sales stopped debating whose conversion was real, because both now appear in the same reports under different names. The marketing manager plans campaigns against quote volume instead of page traffic, and the derived quote events have entered the company's pricing debates — the first attribution argument in years ended because both sides opened the same table instead of trading exports. The container itself became explainable: any tag can be traced to an owner and a purpose on one sheet, so the fear of touching it, which had frozen the estate for years, is gone.

The result was read from Container migration log against the pre-engagement baseline over the stated window, with a guardrail check on tags in the production container after disposition. Where platform-reported numbers and business outcomes differ, this record says which layer it is quoting.

What they own now

• The migrated server-side container with the signed-off tag inventory as its founding document.
• The mail-gateway quote parser with its per-branch daily count check.
• A conversion definition sheet separating marketing leads from sales-qualified quotes.
• The BigQuery tables holding derived quote events, owned by the company's IT contractor.
• A one-page runbook for adding or retiring tags without freezing the container again.