At Google Marketing Live 2026, Google announced Qualified Future Conversions (QFCs), a new always-on metric that uses Gemini to predict the future value of a current ad interaction. Google framed it as a way to “prove the impact of demand creation campaigns”, and for B2B SaaS — where the distance between a first click and a signed contract is measured in months, not minutes — that framing lands closer to the core problem than most product announcements do. This is a measurement change aimed squarely at the thing that has always made paid acquisition for software hard to justify: the best campaigns rarely close the deal themselves.
This guide explains what a QFC actually is, how the prediction works, why it matters more for long-sales-cycle SaaS than for almost any other advertiser, and — most usefully — what to do now while the feature is still in a restricted pilot. The short version: QFCs will not fix a broken account, but they reward the accounts that have already done the unglamorous work of measuring pipeline instead of leads. Getting that foundation right is the preparation, and it pays off whether or not you ever flip QFCs on.
What a Qualified Future Conversion actually is
A Qualified Future Conversion is a predicted conversion. Rather than waiting for a user to complete an action inside your attribution window, Google’s model estimates conversions that are likely to happen up to 180 days after an ad interaction, based on patterns in early journey signals and comparisons to historical users who went on to convert. The word “qualified” is doing real work here: a QFC is only counted when a user was exposed to your ad and then performed a defined leading user action — a branded search, an engaged visit, or a similar intermediate step — that historically precedes a genuine conversion. It is not simply crediting every impression with imaginary future value; it is crediting interactions that produced a downstream behaviour the model recognises as meaningful.
That structure is what separates QFCs from vanity forecasting. Google describes the metric as counting conversions where the user took a “leading user action” after ad exposure, which means the prediction is anchored to observed behaviour rather than pure extrapolation. As Search Engine Land’s GML 2026 coverage notes, the metric is positioned as the bridge “from discovery to decision” — a way to connect the top of the funnel to the revenue at the bottom. For a SaaS marketer, the practical translation is: a campaign that drives someone to later search your brand and return to your pricing page finally gets numeric credit for that influence, instead of being written off because the visitor did not convert on the first session.
Why this matters more for B2B SaaS than for anyone else
B2B SaaS has the longest, most fragmented buying journey in performance marketing, and that is exactly the environment where last-click attribution does the most damage. A prospect might click a non-brand search ad, read a comparison post, disappear for six weeks, loop in three colleagues, search your brand directly, and only then book a demo. Under conventional conversion counting, the original non-brand click looks worthless and the branded search gets all the credit — so budget drains away from the demand-creation campaigns that actually started the journey. This is the same measurement trap we cover in our guide to optimising for SQLs, not leads, and QFCs attack it from the attribution side.
The 180-day prediction window is what makes QFCs fit SaaS specifically. Most B2B software deals do not close inside a 30- or 90-day attribution window, so any metric that stops counting at 90 days structurally undervalues the channel. A metric explicitly built to project conversions up to six months out is far better matched to how software actually gets bought. It also complements the shift toward measuring what happens after the click, which is the whole premise behind our offline conversion stack for B2B SaaS — except where offline conversions report pipeline that has already happened, QFCs predict pipeline that is likely to. Used together, you get a rear-view mirror and a windshield.
How QFCs change bidding, not just reporting
QFCs are not only a reporting metric — the deeper change is that Smart Bidding can optimise toward predicted future value. Because each Qualified Future Conversion carries an estimated value, bidding algorithms can bid up campaigns that seed future conversions even when their direct conversion rate looks weak. That inverts a long-standing problem: demand-generation and brand-adjacent campaigns have historically been the first to get their budgets cut, because their last-click ROAS is unimpressive. If the algorithm can see and bid toward the future value those campaigns create, the money follows the influence rather than fleeing it.
The risk is the same one that shadows every predictive system: garbage in, garbage out. A model that projects future value is only as reliable as the conversion history it learns from, so accounts with thin data, duplicated conversions, or leads that never get qualified will produce noisy predictions — and if you bid on noise, you scale noise. Before you let QFCs steer spend, your conversion setup needs to be feeding the algorithm real, deduplicated, qualified outcomes. This is where value-based bidding discipline matters; our guide to the SaaS conversion value ladder covers how to assign meaningful values so that whatever the algorithm optimises toward — present or predicted — reflects actual business worth.
How to prepare while QFCs are in pilot
You almost certainly cannot switch QFCs on today. Google confirmed the metric is in a restricted pilot, globally across all languages, with a broader beta expected later in 2026. The good news is that the preparation is entirely within your control and pays dividends regardless. Start with conversion tracking hygiene: one clearly defined primary conversion per campaign objective, no double-counting, and enhanced conversions enabled so Google can match more of your real conversions. If your tracking is shaky, no predicted metric will save you — it will only launder the noise into a confident-looking forecast.
Next, feed pipeline back to Google. Import offline conversions so that the model learns from qualified opportunities and closed-won revenue, not raw form fills that your sales team knows are junk. Then get your branded-search measurement in order, because branded search is one of the leading user actions QFCs are built around — see our guide to measuring branded searches for how to instrument that cleanly. Finally, if you have been under-investing in upper-funnel work because you could not measure it, start building the demand-creation muscle now so you have campaigns worth crediting once QFCs arrive; our B2B SaaS demand generation guide covers where to begin.
The limits: treat prediction as a signal, not a target
A predicted metric is a powerful input and a dangerous sole target. QFCs are genuinely useful for the decisions attribution has always mangled — how to split budget between demand creation and demand capture, and whether a soft-looking brand campaign is actually pulling its weight. But early in a pilot, model predictions can drift, and a metric that projects six months into the future cannot be validated for six months. The disciplined approach is to use QFCs to inform allocation while continuing to grade the channel on what your CRM eventually reports as real pipeline and revenue.
Concretely: let QFCs argue for shifting budget toward upper-funnel campaigns, then check, two quarters later, whether closed-won pipeline actually rose in line with the predictions. If the model consistently over-promises, discount it and lean on your offline conversion data as the source of truth. If it tracks reality well, trust it more. This is the same posture we recommend for any automated signal in the account, from Smart Bidding to Google’s own suggestions — see how we evaluate Google’s AI recommendations. QFCs are the most SaaS-relevant measurement change Google has shipped in years, but they earn trust the same way every metric does: by matching what the business actually books.