Discover how Quicken used forecast-led planning to justify an aggressive year-end budget increase and deliver a 19% revenue lift while holding iROAS steady.
Quicken achieved a 19% increase in gross revenue within three weeks by using MMM-based forecasting to conduct geo experiments, model various budget scenarios, and reallocate spend to higher-iROAS channels before committing incremental budget. Incremental ROAS remained stable as total spend grew, confirming the additional investment was profitable rather than merely additive. For full methodology details, please see the downloadable case study below.
Business Challenge
Quicken faced the challenge of justifying a significant year-end increase in its marketing budget without a data-driven forecast to support it internally. In late 2023, a competitor’s exit provided a temporary sales boost that the team recognized would not recur. For late 2024 and 2025, Quicken needed to achieve ambitious targets independently and present a compelling forecast to secure executive approval for increased spending. The main difficulty was not obtaining additional funds but demonstrating in advance that further investment would be efficient rather than merely incremental.
Objectives
Develop a data-driven forecast that provides sufficient precision to support a budget increase request to senior leadership.
Determine which channels can scale efficiently while maintaining incremental return on ad spend (iROAS).
Sustain iROAS profitability as total investment increases, demonstrating efficient growth rather than only increased volume.
How LiftLab’s Agile MMM Platform Approached It
LiftLab’s marketing measurement platform gave Quicken four levers to work with:
Predictive budgeting: Weekly, model-driven allocations that adjusted as new data came in
Geo experimentation: Controlled tests to pressure-test which channels could actually scale
Scenario modeling: Multiple budget levels tested against projected outcomes to build the case for more investment
Predictive media buying: Pricing models applied to buy placements at the most efficient times
The complete methodology, including geo-test design, channel-level allocation shifts, and modeling logic for each planning decision, is detailed in the downloadable case study at the bottom of this page.
Results & Business Impact
Gross revenue grew 19% during the critical year-end period, driven by smarter allocation rather than increased spending.
Leadership approved an additional budget based on a data-backed, model-driven forecast instead of a confidence-based pitch.
Spending was reallocated to channels best positioned for growth, guided by iROAS signals from the Incrementality Testing Suite.
Incremental ROAS remained steady despite higher total spending, confirming that each additional dollar was genuinely profitable rather than capturing conversions that would have occurred regardless.
In addition to meeting the revenue target, Quicken’s leadership gained greater confidence in the marketing team’s analytical process, establishing data-driven forecasting as a core pillar of the company’s growth strategy.
Testimonials
“Liftlab’s solution gave us the confidence to make significant changes to our media mix.
Our business results proved we made the right choice.”
“LiftLab’s model-based forecasting was a necessary tool to tie our suggested media flighting to anticipated business impact.”
Key Takeaways
Building a forecast before spending earns internal trust more quickly than reporting results afterward. Model-first planning helps convert skeptics.
Reallocating budget to effective initiatives is more impactful than increasing spend across the board. Incremental efficiency is as important as total volume.
The ability to adjust spending during a campaign is as important as the initial plan. Daily signals from an always-on model enable effective in-flight optimization.
Demonstrating incrementality, not just revenue growth, secures long-term leadership support. iROAS is the metric that distinguishes correlation from causation.
Would you like a detailed overview of how LiftLab supported Quicken in modeling scenarios, restructuring its channel mix, and validating results, including full geo-testing data and channel-level iROAS changes?
FAQs about Quicken Case Study
What results did Quicken achieve with LiftLab?
Quicken increased gross revenue by 19% during a key year-end sales period while maintaining incremental ROAS at higher spending levels. This was achieved through forecast-driven planning, including geo experiments to test channel scalability, modeling various budget scenarios before allocating funds, and daily reallocations to higher-iROAS channels based on actual performance. Leadership approved an additional budget based on the model’s results rather than intuition.
What marketing challenge was Quicken facing?
Quicken needed to meet ambitious year-end revenue targets and support its request for additional marketing budget with solid data. The exit of a competitor had boosted 2023 results, an advantage that would not continue in 2024. The main challenge was to demonstrate that increased spending would generate efficient, causal revenue growth rather than simply benefit from existing demand. The core issue was forecasting accurately enough to secure executive approval before allocating the budget.
What is incremental ROAS (iROAS) and why does it matter?
Incremental ROAS (iROAS) measures only the revenue directly attributable to marketing spend, excluding sales that would have occurred without advertising. In contrast, standard ROAS may overstate channel effectiveness by including existing demand. iROAS isolates true causal impact, making it the preferred metric for marketing budget optimization. Sustaining iROAS at higher spend levels demonstrates genuine efficiency.
How does LiftLab help brands plan marketing budgets?
LiftLab’s Agile MMM platform integrates predictive scenario modeling, geo-based incrementality testing, and daily optimization signals to forecast outcomes across various spend levels before any funds are committed. The Scenario Planner enables teams to model budget allocations against projected revenue. The Incrementality Testing Suite conducts controlled geo experiments to identify which channels can scale effectively. PlatformSense delivers daily signals to support in-flight budget reallocation. Together, these tools provide a finance-auditable, causal view of where the next marketing dollar will generate the highest return.
Can LiftLab help my brand achieve similar results?
LiftLab’s Agile MMM and Incrementality Testing Suite helps brands investing in multiple channels justify marketing budgets through causal, finance-auditable measurement rather than platform-reported attribution. If your team faces CFO scrutiny, needs to reallocate budgets to proven channels, or requires a model that updates weekly instead of quarterly; LiftLab addresses these needs. Request a demo to see how the Scenario Planner and Trust Engine can optimize your media mix.
