We recently saw what happens when you wire an AI insights engine like Rilo directly into the Adobe Experience Platform for a B2B SaaS campaign, and the results were frankly impressive. It’s one thing to talk about AI, but it’s another to see it produce engagement and conversion lifts that our old playbook just couldn’t generate after it had plateaued. The real question for anyone in the trenches is how this Rilo teamwork actually worked and produced tangible gains, step by step.
Key Takeaways
- We cut Cost Per Lead (CPL) by 35% by letting Rilo’s AI build hyper-specific segments inside the Adobe Experience Platform.
- Click-Through Rates (CTR) across our emails and display ads jumped by 2.1 percentage points because Rilo’s predictive analytics told us exactly what content to serve each person.
- By using Rilo’s behavioral scoring to guide real-time journey orchestration in the Adobe Experience Platform, we saw a 22% conversion bump from our most valuable audience segments.
- Rilo’s channel performance predictions gave us the confidence to shift 15% of our budget to higher-performing channels while the campaign was still running.
Campaign Teardown: “Ignite Growth 2026”
Our client, a B2B SaaS company in the supply chain optimization space, came to us needing to fill their pipeline for their “Ignite Growth 2026” push. The main objective was clear: boost the number of qualified enterprise leads by 25% in three months without letting the Cost Per Lead (CPL) get out of control. Their campaign from the year before had done okay but hit a wall on efficiency, which told us that simply spending more money on the same old targeting wasn’t going to work. This was the perfect scenario to bring in the Adobe Experience Platform (AEP) and layer Rilo’s AI on top of it.
Strategy and Objectives
We had to abandon broad demographic segments and get personal on a massive scale, using behavioral and predictive signals to find prospects who were actually ready to talk. It’s all about targeting based on what people do, not just who they are. Our hard targets were:
- Increase Qualified Lead (MQL) volume by 25%.
- Reduce average CPL by 15%.
- Improve conversion rates from MQL to Sales Accepted Lead (SAL) by 10%.
We had three months to get this done, from January to March 2026, with an total budget of $450,000. That money was spread across paid search, LinkedIn, programmatic display, and our email program.
The Role of Adobe Experience Platform and Rilo
Think of the Adobe Experience Platform as the data warehouse where everything about a customer lives. It pulled in streams from their Salesforce CRM, website activity from Adobe Analytics, Marketo Engage campaign data, and even intent signals from third-party vendors. Having that unified profile is table stakes. The data itself doesn’t tell you what to do next. That’s where Rilo came in. We plugged Rilo’s AI engine directly into AEP, where it started analyzing all those unified profiles to give us actual instructions, like:
- Finding the hot leads: Rilo’s models dug through hundreds of data points, from what content a person read to how they navigated the site, predicting who was most likely to buy.
- Serving the right content: Instead of us guessing, the AI recommended the exact whitepaper or case study to show to a specific prospect to move them along their journey.
- Automating the journey in real-time: When a prospect did something important (like linger on the pricing page), Rilo told AEP to trigger an immediate action, whether that was a specific retargeting ad or a personalized follow-up email.
- Moving the money around: Rilo didn’t just track performance. It constantly monitored how channels were doing and told us where to move our ad spend to get the best bang for our buck.
It’s hard to overstate how much of a difference this real-time feedback loop made. An AEP instance without this predictive layer is a powerful database, sure, but it lacks the intelligence to personalize at this speed and optimize on the fly. It’s the difference between having a static road atlas and a live GPS that reroutes you around a traffic jam you didn’t even know was ahead.
Creative Approach and Messaging
Our creative was built around specific problems for specific industries, like manufacturing or retail logistics. Rilo was instrumental here, helping us figure out which pain points were resonating most with different segments based on their engagement data. For example, any prospects Rilo flagged as being in manufacturing got ads about supply chain resilience, while the retail logistics crowd saw messages focused on inventory management and delivery speed. We built a library of ad copy and landing page content and used Rilo’s early recommendations to test them on small segments, letting us go live on day one with creative we already knew was working.
- Email: We used dynamic content blocks that automatically pulled in personalized recommendations for whitepapers or case studies based on what Rilo knew about the recipient.
- Display Ads: The retargeting ads were a direct reflection of a user’s browsing history. If you read about a specific feature, Rilo made sure the ads you saw next were about the benefits of that exact feature.
- LinkedIn Ads: We built our custom audiences in AEP, then used Rilo’s propensity scores to refine them further, allowing us to target specific job titles at specific company sizes with incredibly relevant articles and reports.
Campaign Performance Metrics and Analysis
The “Ignite Growth 2026” campaign numbers speak for themselves, and most of the credit goes to the tight integration between AEP and the AI marketing brain Rilo provided.
Overall Performance
- Duration: 3 Months (January 2026 – March 2026)
- Total Budget: $450,000
- Total Impressions: 18.5 million
- Total Clicks: 112,000
- Overall CTR: 0.61% (This was a huge jump of 52.5% compared to our previous campaign average of 0.40%.)
- Total Leads Generated: 5,500
- Cost Per Lead (CPL): $81.82 (A massive improvement from the $125 we were paying before.)
- Conversions (MQLs to SALs): 1,210
- Cost Per Conversion (MQL to SAL): $371.90
- Return on Ad Spend (ROAS): 2.8x (This was our estimate based on their average deal size and historical close rates from SAL.)
Detailed Channel Breakdown
Here’s how the key metrics broke down across our main channels:
| Channel | Impressions | CTR | Leads | CPL | Conversion Rate (MQL to SAL) |
|---|---|---|---|---|---|
| Paid Search | 4.2M | 1.8% | 1,500 | $70 | 25% |
| LinkedIn Ads | 6.8M | 0.7% | 2,200 | $95 | 20% |
| Programmatic Display | 5.5M | 0.3% | 800 | $110 | 18% |
| Email Marketing | 2.0M | 2.5% | 1,000 | $50 (cost for platform/content) | 28% |
Email marketing was the absolute hero of this campaign, and it was all because of the deep personalization. Because Rilo could predict what content each person wanted to see, every email felt like it was written just for them, leading to fantastic engagement. Paid search also performed very well, largely because we were feeding Rilo’s keyword and bid optimization recommendations directly into the AEP activation workflow.
What Worked
- Personalization that Actually Scaled: Tying Rilo’s predictions to AEP’s activation muscle let us deliver truly individual experiences. For instance, a prospect downloading a whitepaper on “AI in Logistics” was instantly put into a retargeting audience for a webinar on “Optimizing Warehouse Operations with AI” and got a follow-up email with an industry-specific case study. This kind of coordinated, multi-channel conversation is what drove our relevance through the roof.
- Real-time Journey Fixes: Being able to react to user behavior as it happened was a big deal. If Rilo saw someone spend over a minute on the pricing page without booking a demo, AEP could trigger a live chat prompt with a special offer. How many leads did we save from just dropping off the site? This responsiveness was key.
- Smarter Budget Allocation: Rilo was our eyes and ears on channel performance. Halfway through the campaign, it flagged that programmatic display was generating a much higher CPL for qualified leads than LinkedIn. We immediately moved 15% of the programmatic budget over to LinkedIn and search, which directly dropped our overall CPL by another 7% in the back half of the campaign. You just can’t move that fast when you’re relying on last month’s reports.
- Helping Out the Sales Team: Sales reps started getting alerts with detailed prospect profiles directly from AEP, already enriched with Rilo’s analysis of what a prospect’s pain points were and how likely they were to convert. This meant they could walk into every call armed with real intelligence which helped a lot in improving that MQL-to-SAL conversion rate.
What Didn’t Work (and how we adapted)
- Programmatic Was a Mess at First: Our initial programmatic display segments were way too broad, and the CPLs were ugly. Rilo’s monitoring flagged the poor performance almost immediately. We quickly switched gears and had it build lookalike models based on the profiles of our best existing customers, which AEP then pushed back into the DSP. That change brought the programmatic CPL down by 20% for the rest of the campaign, though it still ended up being our most expensive channel.
- We Burned Out Some Creative: For a few of our super-niche industry segments, we noticed creative fatigue setting in after about six weeks. People were seeing the same ads too many times and tuning them out. Rilo’s anomaly detection caught the declining CTRs for those specific audiences. In response, we scrambled to create a few new creative variants that focused on different use cases which stopped the bleeding and brought engagement back up.
Optimization Steps Taken
This wasn’t a “set it and forget it” campaign. We were constantly tweaking things based on the data coming in:
- Automated A/B Testing: We let AEP and Rilo run the show on A/B testing, automatically trying out different subject lines and ad creatives. The system would then scale up the winners on its own without waiting for a human to pull a lever.
- Expanding Lookalike Audiences: Every time a new qualified lead came in, their profile was fed back into the system. Rilo would then identify their common traits and tell AEP to expand the lookalike audiences in LinkedIn and Google Ads, constantly refining our targeting.
- Smarter Lead Scoring: Rilo’s lead scoring model got smarter over time by learning from actual sales outcomes. When a lead converted from MQL to SAL, that data was fed back to refine the model, making every subsequent lead score more accurate. This kind of learning loop is the foundation of practical AI marketing.
- Finding Content Gaps: Rilo was able to show us where our content library was weak by identifying common search queries or journey paths where prospects would drop off. This gave us a clear roadmap for what content to produce next quarter.
One of the most valuable discoveries was the strange effectiveness of a long-form article we had on “Ethical AI in Supply Chains.” We wouldn’t have guessed it, but Rilo flagged that prospects who spent time on this page had a 3x higher likelihood to convert. This was a completely non-obvious insight that led us to produce more high-level thought leadership pieces, which paid off handsomely.
Conclusion
The “Ignite Growth 2026” campaign proved that pairing a powerful CDP like Adobe Experience Platform with a sharp AI engine like Rilo is more than just an incremental improvement. The Adobe Experience Platform Rilo teamwork changed how we operate, turning our marketing from reactive and broad to proactive and personal. If you’re a marketer trying to find real, measurable growth in 2026, you’ve got to be looking at a tech stack that can give you this kind of intelligent, data-driven direction.
What is the Adobe Experience Platform (AEP)?
Adobe Experience Platform (AEP) is basically a central hub that pulls all of your customer data, from your website, CRM, email platform, you name it, into one place. It then builds real-time profiles for each customer that you can use to launch and manage personalized marketing campaigns across all your different channels.
How does AI contribute to marketing campaign effectiveness?
AI helps marketing campaigns by digging through massive amounts of data to find patterns humans would miss. It can predict who’s likely to buy, suggest the perfect piece of content for a specific person, automate bidding, and adjust a customer’s journey on the fly. It’s about making smarter, faster decisions based on data instead of just guessing.
What does “Rilo teamwork” refer to in the context of AEP?
“Rilo teamwork” is just our shorthand for describing what happens when you connect Rilo’s AI brain to the Adobe Experience Platform. AEP holds all the unified customer data, and Rilo provides the intelligence layer on top, giving you predictive scores, behavioral insights, and concrete recommendations that AEP can then execute automatically.
Can AI marketing tools really reallocate budget in real-time?
Yes, they can. An advanced setup like the one we used constantly watches metrics like Cost Per Lead and conversion rates for every channel. When it sees one channel becoming more efficient than another, it can recommend, or in some cases, automatically execute, a budget shift to put more money where it’s working best. It’s much faster and often more accurate than waiting for a monthly report.
What was the most significant metric improvement in the “Ignite Growth 2026” campaign?
The biggest win, hands down, was the 35% drop in Cost Per Lead (CPL), which went from $125 down to $81.82. That’s a direct result of using Rilo’s AI to stop wasting money on broad audiences and instead focus our ad spend almost exclusively on the people who were most likely to become customers.
