Bidding to Margin Improves ROAS 59% for A.M. Leonard
How Media Mix Modeling (MMM) insights, executed through margin-based bidding, grew revenue and profitability in the same season.
THE CLIENT – A.M. Leonard
A.M. Leonard is a leading supplier of horticultural tools and equipment. Their extensive product line includes tools for gardening, landscaping, and nursery operations, supporting the diverse requirements of their customers.
Our Results
21%
Peak season revenue year-over-year
59%
Improvement in ROAS year-over-year
54%
Increase in Meta revenue year-over-year
Challenge
Rebuild a media program around profitable revenue when platform metrics said everything was already working.
Advertising platforms optimize toward the signals they can see: clicks, conversions, and revenue. What they cannot see is what each sale is worth to the business. Margin varies widely across a large product catalog, and a platform bidding purely toward revenue will happily pour budget into products that add to the top line while contributing almost nothing to the bottom.
That was the position A.M. Leonard was in. On paper, the prior year looked like a success: media programs generated significant revenue, and headline ROAS held up well. Once A.M. Leonard factored in total business costs, the picture changed. Campaigns across Google and Meta were spending heavily on low-margin products, and the margins had nearly disappeared.
We’ve seen this pattern before. Platform metrics look strong on their own, then tell a different story when the full profit and loss comes into view. Fixing it meant rebuilding the program around a different objective, profitable revenue rather than revenue alone, and it had to be done ahead of a compressed peak season when the majority of the year’s opportunity would arrive in a matter of weeks.
Approach
Rebuild the bidding and budget architecture around gross profit, guided by Media Mix Modeling and executed with discipline at the campaign level.
From Revenue to Profitable Revenue
Revenue doesn’t fund a business. Margin does. Our first step was to align with A.M. Leonard on what the program actually needed to deliver in terms of efficiency, profitability, and scale, and then to make gross profit on ad spend the metric campaigns were bid against.
Operationalizing that required restructuring the catalog itself. We rebalanced the A.M. Leonard’s product margin groupings to concentrate more of the catalog in the highest-margin tier, because larger, well-organized product sets consistently perform better in Shopping and social catalog campaigns. Low-margin products were removed from advertising almost entirely, retained only where a product signaled a large professional buyer or had historically been the entry point to bigger transactions.
Letting the Model Point the Budget
Our Media Mix Model was built on A.M. Leonard’s actual CRM and sales data rather than platform-reported metrics, giving an economics-level view of what was genuinely driving revenue. For months, the model had pointed toward the same conclusion: the program had significant untapped runway in paid social, and budget concentrated in lower-performing shopping and search campaigns would work harder there.
The strength of the data underneath the model is what made that signal trustworthy enough to act on. Rather than waiting for the next profit and loss cycle to confirm what was working, the team could see where budget was and wasn’t performing in near real time.
Executing With Discipline
A model recommendation is only as good as its execution in the accounts. Our Digital Advertising team translated the reallocation into campaign-level moves: redeploying a high-margin catalog campaign on Meta with a substantially expanded product set, dedicating budget to a curated set of the best-selling products that stronger advertised performers had been crowding out of Shopping auctions, and narrowing Amazon to a hand-picked set of high-margin SKUs.
Throughout peak season, the team treated A.M. Leonard’s efficiency targets as a hard constraint rather than a preference, turning off campaigns that couldn’t clear the bar even when it meant leaving volume on the table. Digital Advertising and Marketing Sciences worked in continuous coordination, combining fresh model signals with live account data so every allocation decision was grounded in both strategic direction and ground-level performance.
Outcomes
The rebuilt program grew peak season revenue 21% while improving ROAS 59%.
Growing revenue and improving efficiency at the same time is the hard result in digital advertising. Most programs trade one for the other. This program delivered both because it was optimizing for the right objective from the start:
- 21% peak-season revenue year over year: growth concentrated in the products that fund the business, driven by a catalog and campaign structure built around margin.
- 59% improvement in ROAS year over year: the direct return on bidding to gross profit rather than gross revenue.
- 54% increase in Meta revenue year over year: the compounding payoff of the reallocation the model had been pointing toward, with a rebuilt high-margin catalog campaign leading the way.
The program now runs on a durable advantage. Every season of sales data sharpens the model, and the model sharpens the next season’s plan. For A.M. Leonard, media investment has become a system that gets smarter with every cycle, built around the number the business actually keeps.
next steps
Want to know what bidding to margin could do for your media program?
Our team will walk you through what it takes: the data foundation, the modeling, and the campaign structure that turn margin into a bidding signal. No sales pressure, just a real conversation about whether your program is optimizing for the right thing.
