How an Australian Marketing Agency Used AI to Predict Campaign ROAS Before Spending a Dollar

The most uncomfortable moment in any client-agency relationship is the one where the agency presents a campaign proposal and the client asks: "What return can we expect?" The honest answer — that no one knows until the money has been spent — is technically accurate but commercially damaging. It positions the agency as a service provider rather than a strategic partner, and it places the client in the position of funding an experiment rather than making an investment.

LoopBC, a Sydney-based digital marketing agency, came to PresciaIQ with a specific goal: to change that conversation entirely. They wanted to be able to walk into a client briefing with a data-backed ROAS forecast — not a range based on industry benchmarks, but a specific prediction based on the client's actual historical data, current market conditions, and the proposed campaign parameters.

The Problem With Benchmark-Based Forecasting

Most marketing agencies forecast campaign performance using industry benchmarks — average click-through rates, average conversion rates, and average ROAS figures for a given channel and sector. These benchmarks are useful as a starting point, but they are a poor basis for a client commitment because they describe the average of all campaigns, not the likely performance of this specific campaign for this specific client in this specific market at this specific time.

A retail client running a Meta campaign in November faces completely different auction dynamics to the same client running the same campaign in February. A campaign targeting a saturated audience segment will underperform benchmarks regardless of creative quality. A competitor increasing their bids by 40% in the week before launch will compress ROAS for everyone in the auction. None of these factors are captured by industry benchmarks.

LoopBC's team was experienced enough to account for some of these variables intuitively, but the process was inconsistent and non-scalable. Different account managers applied different mental models. There was no systematic way to quantify the risk of a campaign underperforming, or to identify which variables were most likely to drive that underperformance.

Integrating AdsIQ Into the Pitch Workflow

PresciaIQ integrated AdsIQ into LoopBC's existing campaign planning workflow in two stages. The first stage connected AdsIQ to LoopBC's ad account data — Meta, Google Ads, and programmatic display — giving the model access to 18 months of historical campaign performance across all active clients. The second stage built a pre-campaign scoring interface that LoopBC's account managers could use directly, without requiring data science expertise.

The interface works as follows: an account manager enters the proposed campaign parameters — channel, budget, targeting parameters, creative format, campaign objective, and flight dates — and AdsIQ produces a ROAS forecast with a confidence interval. The forecast is accompanied by the three variables most likely to cause the actual ROAS to diverge from the prediction, and a sensitivity analysis showing how the forecast changes if those variables shift.

For a retail client proposing a $50,000 Meta campaign targeting women aged 28–45 in Sydney for a fashion brand in October, AdsIQ might forecast a ROAS of 3.2–3.8x with 84% confidence, with the primary risk factors being audience saturation in the target demographic and a projected 15% increase in CPMs during the October–November pre-Christmas period. The account manager can then present this forecast to the client, explain the risk factors, and propose mitigations — such as audience expansion or a phased budget release — before a dollar is spent.

The Results: 84% Forecast Accuracy, 31% ROAS Improvement

Across the first six months of AdsIQ deployment, LoopBC's campaign forecasts achieved 84% accuracy — meaning the actual delivered ROAS fell within the predicted range in 84% of campaigns. More significantly, the campaigns where AdsIQ identified high-risk variables and the team acted on the recommended mitigations delivered an average ROAS 31% higher than the campaigns where the mitigations were not applied.

This 31% improvement is not attributable to better creative or larger budgets. It is attributable to better pre-campaign decisions — specifically, the ability to identify which audience segments were over-saturated before spend was committed, which flight dates carried elevated auction risk, and which channel mix was most likely to deliver efficient returns given current market conditions. These are decisions that previously required either expensive experimentation or experienced intuition. AdsIQ made them systematic and scalable.

The Commercial Impact for the Agency

Beyond the performance improvement, the AdsIQ integration changed LoopBC's commercial positioning in a meaningful way. The ability to present a data-backed ROAS forecast in a pitch is a genuine differentiator in a market where most agencies present case studies and benchmarks. It shifts the conversation from "trust us, we're good at this" to "here is the specific return we expect to deliver and here is the evidence base for that expectation."

This positioning change had a measurable impact on pitch conversion rates and on client retention. Clients who understood the forecasting methodology before the campaign launched were significantly less likely to question performance mid-flight, because they had a clear framework for evaluating whether the campaign was tracking within the predicted range or diverging from it — and why.

What Australian Marketing Agencies Can Apply From This

The LoopBC engagement demonstrates that predictive AI in marketing is not a technology for large enterprises with dedicated data science teams. It is a practical tool that a mid-sized Australian agency can integrate into existing workflows within weeks, without rebuilding their tech stack or retraining their team. The key is that the AI layer must produce outputs that are immediately actionable by the people running the campaigns — not outputs that require interpretation by a data scientist.

For Australian marketing agencies considering this approach, the most important first step is ensuring that historical campaign data is clean, consistently structured, and accessible. The quality of a ROAS prediction is directly proportional to the quality of the data it is trained on. An agency with 18 months of well-structured performance data across multiple clients and channels will get meaningfully better predictions than one with fragmented or inconsistently tagged data.

Frequently Asked Questions

How much historical data does AdsIQ need to produce accurate forecasts?

AdsIQ produces useful forecasts with as little as 6 months of campaign data, but accuracy improves significantly with 12–18 months of data across multiple campaigns and channels. For new clients with limited history, AdsIQ uses market-level signals and comparable campaign benchmarks to supplement the client-specific data.

Which advertising channels does AdsIQ support?

AdsIQ currently supports Meta (Facebook and Instagram), Google Ads (Search, Display, and Shopping), TikTok Ads, programmatic display, and YouTube. Integration with additional channels is available on request.

Can AdsIQ be used by in-house marketing teams as well as agencies?

Yes. AdsIQ is used by both in-house marketing teams and agencies. For in-house teams, the primary use case is budget allocation and campaign planning. For agencies, the primary use case is client forecasting and pitch differentiation. The interface is designed to be accessible to marketers without data science backgrounds.

Want to see AdsIQ forecast your next campaign's ROAS before you spend? Book a free discovery call with PresciaIQ.