DeepScan’s 2.8x ROAS: AI Marketing in 2026

Everyone’s excited about AI speeding up scientific discovery, but getting those complex findings out of the lab and into a business solution is where things usually fall apart. Our “DeepScan” campaign was all about fixing that problem for a consulting firm that specializes in AI research translation. We had to prove that smart digital marketing could connect these big AI ideas with industries that could actually use them. The whole project boiled down to one question: how do you sell intellectual capital when the customer needs a PhD to understand the product and you need one to understand their business?

Campaign Overview: DeepScan for AI Research Translation

We ran the “DeepScan” campaign from January to April 2026 to bring in qualified leads for a niche consulting client. This firm’s entire business is helping big companies take the latest AI models from academic papers and turn them into something that makes money. Our job was to get them in the room with R&D directors and innovation leads at enterprises in pharma, advanced manufacturing, and finance. That was the primary goal: book initial consultations.

We had a $75,000 budget to work with over the four-month duration. The main things we tracked were Cost Per Lead (CPL), Return on Ad Spend (ROAS), Click-Through Rate (CTR), and, of course, the number of actual consultation bookings. Our bet was that for a super-niche B2B service like this, a multi-channel plan built on solid content marketing and a heavy presence on professional networks would work best.

Initial Strategy: Content as the Core

Our whole strategy was built on making the client look like the smartest person in the room on AI research translation. That meant creating deep, valuable content that spoke directly to the struggles R&D departments have when trying to get AI projects off the ground. We organized it all around three content pillars:

• “From Lab to Ledger: Monetizing AI Breakthroughs” (for the finance guys)
• “Accelerating Drug Discovery with Explainable AI” (aimed at pharma)
• “Predictive Maintenance 4.0: The AI-Driven Factory Floor” (for advanced manufacturing)

For each of these, we wrote a massive foundational article (3,000+ words), then broke it down into smaller blog posts, infographics, and a downloadable whitepaper behind a lead form. The content wasn’t just informational. It was supposed to be a little provocative and push back on the lazy ways companies try to integrate AI. We had to get past the generic AI hype and talk specifics, like how a new generative adversarial network (GAN) from some university lab could actually be used to speed up drug compound synthesis.

Creative Approach: Demonstrating Expertise, Not Just Explaining It

Our creative had to scream expertise. We banned stock photos from the start and instead paid for custom illustrations and data visualizations that made complex AI concepts feel sophisticated but understandable. For the pharma pillar, for example, we had a visual of an AI rapidly iterating on a molecular structure, with insights popping out. Using a generic picture of a scientist would have completely undermined the message that this client has specialized knowledge.

The ad copy was all about problem-solution. We didn’t say, “We offer AI consulting.” We said, “Struggling to translate your AI research into tangible ROI? Discover how our specialists bridge the gap between academic innovation and industrial application.” The tone had to be authoritative but also acknowledge the mess R&D leaders are often in. We also made a bunch of short 30-60 second videos for LinkedIn and YouTube. They featured the firm’s lead consultants talking (sometimes unscripted) about a common problem and pointing people to the whitepaper. I think that raw passion for the subject connected way better than a slick corporate video ever could.

Targeting and Channel Mix: Precision Over Volume

With an audience this specific, our targeting had to be surgical. We put most of our chips on LinkedIn and Google Ads, with a small programmatic display budget set aside just for retargeting. Here’s how we set up the channels:

LinkedIn Campaign Breakdown

• Audience Targeting: We went deep into LinkedIn’s targeting, zeroing in on job titles like “Head of R&D,” “Chief Innovation Officer,” and “VP of Product Development” at companies with 500+ employees in our target sectors. Then we added skill layers like “Machine Learning” and “Deep Learning.”
• Ad Formats: We used a mix of everything: Sponsored Content to push our blog posts and whitepaper, Message Ads (InMail) for hitting up key people directly, and Conversation Ads to walk prospects through a little Q&A.
• Budget Allocation: 60% of the total budget went to LinkedIn. It’s simply the best platform for B2B lead generation like this.
• Key Metrics (Initial 2 Months):
  • Impressions: 1.2 million
  • CTR (Sponsored Content): 0.85%
  • CPL (Overall LinkedIn): $150
  • Conversion Rate (Whitepaper Download): 4.2%

Google Ads Strategy

• Keyword Targeting: Forget broad terms. We went after long-tail keywords like “AI research commercialization pharma” or “financial services AI model deployment.” We also did some careful bidding on competitor names and used extremely tight negative keyword lists to avoid wasting money.
• Ad Formats: Mostly Responsive Search Ads (RSAs) so we could test lots of headlines and descriptions, plus Display Ads for retargeting anyone who visited the site but didn’t convert.
• Bid Strategy: We started with “Maximize Conversions” to get some data, then quickly switched to “Target CPA” with a $100 target once we had enough volume.
• Budget Allocation: 30% of the budget was for Google.
• Key Metrics (Initial 2 Months):
  • Impressions: 850,000
  • CTR (Search Ads): 3.1%
  • CPL (Search Ads): $110
  • Conversion Rate (Contact Form Submission): 2.8%

Programmatic Display Retargeting

• Audience: Just one group: people who landed on a content page, stayed for more than a minute, but didn’t download the whitepaper or fill out a form.
• Creatives: We used dynamic ads that would show them the exact piece of content they were just looking at, nagging them to book a consultation.
• Budget Allocation: The remaining 10% of the budget went here.

What Worked: Data-Driven Successes

The absolute home run of the campaign was LinkedIn Message Ads (InMail). Yes, they’re more expensive per send, but sending a personalized message straight to a decision-maker’s inbox got us a 45% open rate and an 8% conversion rate for booking a discovery call. This single channel was responsible for 35% of all our qualified bookings. The trick was writing messages that actually sounded human and referenced a recent trend or challenge in their specific industry. It showed we did our homework.

Our long-form content was another huge winner. The “Accelerating Drug Discovery with Explainable AI” whitepaper, for instance, had a download-to-consultation rate of 12%. This proves that if someone is willing to read 15 pages of dense material, they’re a very serious lead. The deep content acted as a filter, pre-qualifying them so the sales team had better conversations. When you’re selling complex services, you can’t be afraid to get into the weeds. It’s a signal of your own expertise.

On the Google Ads side, the combination of Responsive Search Ads (RSAs) and dynamic keyword insertion was incredibly effective. We could match our ad copy to the user’s exact search query, which pushed our CTR up. Our best-performing RSA had a CTR of 4.5% and a 3.5% conversion rate, which crushed the campaign average.

What Didn’t Work: Learning from Setbacks

Our first attempt at programmatic display (the part that wasn’t retargeting) was a complete waste of money. We tried building lookalike audiences from our website visitors, but the seed audience was just too small and the targeting ended up being way too broad. We were looking at a CPL of $300+ with almost no qualified leads. It was a classic case of trying to make a channel work when it’s not the right fit for a niche B2B campaign. We killed those ads after the first month and moved the money back into LinkedIn and Google Search.

We also completely misjudged the length of the conversion cycle. We figured it would take about two weeks for someone to go from downloading a whitepaper to booking a call. The reality was closer to 30 days. This messed up our early CPL numbers and forced us to change our attribution windows in Google Analytics 4 (GA4) to a 90-day lookback just to get an accurate picture of what was happening.

Optimization Steps Taken: Iteration is Key

1. A/B Testing Landing Pages: We were always testing our landing pages. We tried different headlines, CTAs, and form lengths. Just by cutting the lead form from 7 fields down to 4 (Name, Email, Company, Role), we got a 15% bump in conversions. We also learned that “Schedule a Discovery Session” worked 20% better than the generic “Contact Us.”
2. Bid Strategy Adjustments: Once we had about 50 conversions in Google Ads, we had enough data to get smarter. We switched the bid strategy from “Target CPA” to “Target ROAS” and set a goal of 250%. This let the machine do the work, and our cost per conversion dropped another 18% over the next two months without hurting lead quality. This kind of AI-driven bidding is perfect for maximizing value.
3. Creative Refresh: We swapped out our ad creative on LinkedIn and Google Display every 4-6 weeks to fight off ad fatigue, which is a real killer for retargeting audiences. We saw that creatives with specific numbers (e.g., “Reduced R&D cycle time by 20% for a Fortune 500 pharma firm”) did way better than vague, benefit-focused ads.
4. Negative Keyword Expansion: Every single week, we went through the search query reports in Google Ads and added junk terms to our negative keyword list. This probably saved us 10% of our budget over the course of the campaign. Terms like “AI research jobs” or “AI research papers free” got blocked immediately.
5. LinkedIn Audience Refinement: After the first month, we tightened our LinkedIn targeting even more. We started excluding job titles like “AI Researcher” who weren’t managers, because they’d click but never convert. We put more money on the senior leadership roles. We also used LinkedIn’s “Matched Audiences” to upload a list of target companies, which gave us a 1.5x higher conversion rate than just using interest targeting.

Overall Campaign Performance and ROAS

So after four months and $75,000, here’s where we landed:

• Total Impressions: 2.8 million
• Overall CTR: 1.9%
• Total Leads Generated: 520 (qualified whitepaper downloads/contact form submissions)
• Total Qualified Consultation Bookings: 85
• Average CPL (Final): $78
• Average Cost Per Qualified Consultation: $882
• Client Acquisition Value (estimated): $2,500 per initial project (conservative)
• Total Attributed Revenue: $212,500 (85 consultations x $2,500)
• Total Ad Spend: $75,000
• Campaign ROAS: 2.8x ($212,500 / $75,000)

We beat our 2.0x ROAS target, which proved that you can market these incredibly complex AI services if you’re focused. The big lesson was that for high-value B2B, the sales cycle isn’t quick. You have to build trust and prove your expertise with really good content. You can’t just throw budget at a channel and hope for the best. You have to know the industry inside and out. For this kind of work, that means you have to speak the language of a research scientist and a CFO at the same time.

Looking back, the campaign’s success wasn’t just about the final numbers. It was about the strategic shift from trying to reach everyone to engaging a very small, very specific group of people on LinkedIn. The constant tweaking of landing pages and Google Ads bid strategies was also critical for making the budget work as hard as it did. If we were to do this again, the next step would be building out more personalized email nurture sequences for everyone who downloaded a whitepaper, to shorten that 30-day journey to a booked consultation.