From $0 to $14M Back in a Single Month for a Travel Brand

NOBL Travel came to us with nothing on Google or YouTube and has stayed with us ever since. We built the channel from zero into a primary revenue engine, scaling to $2M in spend in December for $14M back, a 7x return at peak volume. The discipline behind it matters as much as the number: rigid targets enforced through their MTA, consistent incrementality testing, and Prescient running the media mix model, so every dollar of that return was measured and proven, not assumed.

Problem Assessment

NOBL came to us with no presence on Google or YouTube at all. No account history, no conversion data, no channel to optimize. A blank page.

For a premium travel brand, that is a harder starting point than it sounds. Luggage is a considered purchase with a long research window, and demand is heavily seasonal, so the channel has to be built and trusted well before the peak trading period it needs to carry.

There was also a measurement problem waiting at the end of it. A brand with strong existing demand can pour budget into paid search, watch the reported return climb, and never know how much of that revenue would have arrived anyway. Building the channel was only half the job. Proving it was additive was the other half.

Building the Channel From Zero

We started narrow and let the account earn its way outward.

The first build focused on capturing the demand that already existed, through tightly structured brand and non-brand search, a clean product feed, and Shopping coverage across the core range. That gave us conversion volume, clean signal, and a reliable baseline to measure everything else against.

Only once that foundation was stable did we expand into the upper funnel with YouTube prospecting and Demand Gen aimed at people who had never encountered the brand. Expanding in that order matters. Layering awareness spend onto an account with no signal density is how budget disappears without a trace.

Enforcing Rigid Targets Through Their MTA

Platform-reported ROAS was never the scorecard.

Every campaign was held to targets set and enforced through NOBL’s multi-touch attribution model. Google’s own numbers were used for in-platform optimization, but the decision to scale, hold, or cut a campaign came from the MTA. One shared source of truth across the whole media mix, not a number the ad platform grades itself on.

Those targets were rigid on purpose. When spend is climbing fast, the temptation is to soften the target to keep growth on the board. We didn’t. Campaigns that could not hold the number were pulled back, and budget moved to the ones that could.

Proving Incrementality, Not Assuming It

Alongside the MTA, we ran consistent incrementality testing throughout the account’s growth.

The question at every stage was the same: if this campaign were switched off, what would actually be lost? Testing gave us a measured answer rather than an attributed one, and it decided where the next increment of budget went. Campaigns that proved genuinely additive got funded. Campaigns that were quietly harvesting demand the brand already owned got restructured or cut.

That discipline is why the channel could be scaled aggressively without anyone having to take the growth on faith.

Media Mix Modeling With Prescient

Prescient ran the media mix model across the account.

MTA answers where a conversion was influenced. Incrementality answers whether a channel is producing net new revenue. MMM answers the strategic question above both, which is how budget should be split across the entire mix as spend scales and channels start to overlap.

With all three in place, allocation decisions stopped being arguments about attribution and became a modeling exercise. That is what made a step change in spend a calculated move instead of a gamble.

Scaling Into Peak Season

With the structure, the targets and the measurement stack all in place, we scaled hard into December.

$2M in managed spend that month returned $14M, a 7x return at peak volume. Because the measurement infrastructure had been built alongside the channel rather than bolted on afterwards, the brand could push into peak season knowing exactly what the incremental return on the next dollar looked like.