Material Advantage Campaign: 1.8x ROAS in 2026

Hedging against the wild swings of commodity prices is tough, especially in marketing where every budget dollar has to prove its ROI. When your raw material costs are all over the place, it’s nearly impossible to forecast expenses or protect margins, and that pressure flows directly downstream to the funds you have for marketing. So how can business intelligence (BI) help marketing turn this financial risk into a real strategic edge?

The “Material Advantage” Campaign: A Case Study in BI-Driven Hedging

In mid-2025, we started working with a large industrial manufacturing client that was getting hammered by volatile steel and aluminum prices. The client’s biggest headache was that they couldn’t keep their end-product prices stable, which meant they couldn’t commit to a consistent marketing investment. They had traditional hedging strategies going, but nothing was connected in real time to what they were spending on marketing. Our job was to build a campaign that could adjust on the fly to commodity price changes, essentially using BI as a hedge against market volatility.

The campaign had a $850,000 budget spread over six months, from July to December 2025. Our main goal was to prove that marketing could get more agile, using commodity data to drive lead gen efficiency and help the client’s bottom line. We also wanted to get their name out there with procurement pros and test our new BI integration framework.

Strategy: Connecting Commodity Data to Ad Spend

Our strategy was all about creating a feedback loop between commodity market data and our digital ad settings. This meant getting away from static, set-it-and-forget-it budgets. We piped real-time data feeds for steel and aluminum futures from the CME Group straight into the client’s BI dashboard, right alongside their regular marketing KPIs. The concept was simple. When commodity prices fell, signaling cheaper production, we’d boost ad spend in specific campaigns, pushing offers that talked up better pricing or faster delivery. And when prices shot up, we’d pull back on expensive campaigns and switch to content about brand reliability or thought leadership to save cash.

Getting this set up took a lot of work upfront. We spent three weeks just mapping data points, making sure the API connections were solid, and building out custom visuals in their Microsoft Power BI platform. The engineering team had to build a custom connector just to pull daily settlement prices and weekly forecasts, which would then flag certain thresholds to alert the marketing team.

Creative Approach: Agility in Messaging

The creative had to be agile. We came up with two completely different sets of ad creatives for every audience segment: one for “favorable pricing” times and another for “neutral/unfavorable pricing” times.

• Favorable Pricing Creatives: These ads went straight for the wallet, talking about direct cost savings, immediate availability, or special intro offers. Headlines like “Lock in Today’s Low Steel Prices” or “Aluminum Savings: Limited Time” did really well, paired with visuals of raw materials and humming production lines.
• Neutral/Unfavorable Pricing Creatives: When prices were high, the ads shifted to focus on the client’s dependability, their strong supply chain resilience, and the value of a long-term partnership. The messaging here was more like “Consistent Quality, Uninterrupted Supply” or “Your Trusted Partner in Volatile Markets,” using visuals that suggested stability and expertise.

Building dynamic creative optimization (DCO) templates in Google Ads and Meta Business Suite was a make-or-break part of the plan. This setup let us automatically swap headlines and body copy based on price triggers we defined, which cut down on a ton of manual work. We built out a whole library of 50 different pre-approved headlines and 30 body copy versions, all ready to go at a moment’s notice.

Targeting: Precision in a Volatile Market

We targeted procurement managers, supply chain directors, and manufacturing executives in specific sectors like automotive, construction, and heavy machinery. Our channel mix was LinkedIn Ads for job titles, Google Search Ads for high-intent queries (“bulk steel supplier,” “aluminum sheet pricing”), and display retargeting to bring back website visitors. Geographically, we zeroed in on industrial hubs in Georgia, especially around Atlanta’s manufacturing corridors like the South Fulton Parkway area and the I-75/I-85 interchange, where we knew a lot of the client’s targets were located.

We also built a lookalike audience from their existing customer data which made sure we were reaching people who looked just like their most profitable customers. This worked especially well on LinkedIn, where the lookalikes gave us a 15% higher engagement rate than our broader, interest-based targets.

What Worked: Data-Driven Responsiveness

The campaign’s biggest win was how fast it reacted to market shifts. Take the two-week period in September 2025 when steel prices suddenly fell 8%. Our BI system saw it, triggered an automatic 20% budget increase for the right Google Search and LinkedIn campaigns, and the DCO system immediately flipped on the “favorable pricing” creatives. The results from that quick reaction were great:

• Cost Per Lead (CPL): During that two-week window, the CPL for our steel-related keywords fell to $45.20. That’s a 22% drop from the campaign’s average CPL of $58.00.
• Click-Through Rate (CTR): The favorable pricing ads hit an average CTR of 1.8%, which was 0.5 percentage points better than the neutral ads.
• Conversion Rate: Landing pages that pushed immediate savings saw their conversion rate climb by 1.3 percentage points during these spending bursts, hitting 4.1%.
• Return On Ad Spend (ROAS): Our overall campaign ROAS was 3.1x, but it peaked at 4.8x during the most aggressive spending cycles tied to those price dips. This blew past the client’s previous campaign baseline of 2.0x.

Over the six months, we clocked 18.5 million impressions and 2.3 million clicks. This generated 1,820 qualified leads, for a cost per conversion of around $467. Being able to shift budget based on these commodity signals was huge. It let us capture demand right when the client’s offer was strongest.

“The real-time dashboards became indispensable,” the client’s Head of Procurement told us in a review. “We could see the direct correlation between material costs, our marketing spend, and lead volume. That level of transparency was something we hadn’t achieved before.”

What Didn’t Work: Over-Reliance on Automation and Latency Issues

The campaign worked, but we definitely hit some bumps. At first, our automation thresholds were too aggressive. We set the system to pull back ad spend very quickly if prices went up. In late October, a small, temporary price jump triggered a huge cut in ad impressions, and our lead volume tanked for almost 48 hours before the market corrected and the system scaled back up. It showed us we needed more sophisticated, tiered thresholds instead of a simple on/off switch.

Data latency was another headache. The CME Group data is close to real-time, but there was a lag in getting it into our BI dashboard and then pushing those signals out to the ad platforms. In a market that moves this fast, a few hours of delay can mean you either miss a great opportunity or waste money. We measured a 3-hour average latency from price update to ad adjustment. That was fine for daily trends, but it was too slow for any intra-day swings.

Finally, some of our “unfavorable pricing” ads just didn’t land. We designed them to maintain brand presence, but the engagement was lower than we hoped. The messaging was probably too safe. The takeaway was that even when prices were high, the value prop had to be about more than just “we’re reliable.”

Optimization Steps Taken

1. Tiered Automation Thresholds: We changed the BI system to use three tiers for budget changes (minor, moderate, significant) for both price drops and spikes, getting rid of the old binary trigger. This gave us a much smoother, smarter way to react to the market.
2. Reduced Data Latency: We worked with the client’s IT department to clean up the data pipeline, and we got the average latency down from 3 hours to about 45 minutes by simplifying API calls and improving server processing. We had to set up dedicated data replication instances to make it happen.
3. Enhanced “Unfavorable” Creatives: We reworked the ‘unfavorable’ ads to talk about long-term cost efficiency, new material options, and the client’s engineering chops. A new headline like “Optimize Your Supply Chain: Expert Consultation” got a 10% CTR bump over the old ones.
4. A/B Testing on Landing Pages: We were constantly A/B testing the landing pages to make sure the message lined up with the ad that brought them there. For example, pages that pushed quick quotes during favorable price periods converted 15% higher than the static versions.
5. Weekly Performance Reviews: On top of the automation, we put in mandatory weekly reviews to look at the marketing data alongside briefings from commodity market analysts. Having people in the loop (every Tuesday morning, like clockwork) provided essential context and let us step in and manually override the system when it looked like it was misreading a short-term market blip.

What the “Material Advantage” campaign proved is that tying BI to commodity price data gives marketing a serious hedging tool. The goal is to position your marketing spend to grab opportunities and shield yourself from bad market moves. We saw a clear line between market intel and campaign performance, which gave us a solid framework for our future BI growth strategy.