Improvado’s AI PR: 45% More Mentions in 2026

AI’s role in marketing isn’t just theory anymore. We’re seeing real, measurable outcomes, especially in how brands build and tell their stories. This AI case study breaks down how the marketing software company Improvado used AI to completely overhaul its PR storytelling, leading to a major boost in visibility and engagement. So how did this shift actually affect the bottom line?

Deconstructing Improvado’s AI-Powered PR Narrative

Back in Q1 2026, Improvado kicked off a big PR push to show off its new AI features for marketing data aggregation. The goal was simple: stand out in a very crowded martech field by focusing on predictive analytics and automated insights. We wanted to weave a compelling story about how AI practically helps marketers every day. We focused on selling solutions to their biggest headaches, not just listing product features.

The campaign, which we called “Intelligent Insights, Real-World Impact,” was a three-month sprint from January 1st to March 31st, 2026. The budget for all outside PR work, agency fees, software subscriptions, content tools, was $75,000. That number didn’t include our internal team’s salaries, but it covered everything else.

Strategy: Beyond the Press Release

We knew from the start that just blasting out press releases wasn’t going to work. You just can’t cut through the noise that way anymore. We needed to integrate AI into every single step, from coming up with ideas to pitching journalists and tracking results. The strategy rested on a few key pillars:

1. AI-Driven Topic Cluster Identification: Instead of just guessing what to write about, we used natural language processing (NLP) tools to scan industry trends, see what competitors were getting coverage for, and find pain points people were complaining about on forums and social media. This process surfaced some underserved angles where our AI tools could offer a fresh take. For instance, the AI found a growing interest in “attribution modeling for emerging channels” and “predictive budget allocation,” so we made those our central themes.
2. Personalized Content Generation Frameworks: Our team still wrote the final copy, but AI gave us the outlines, talking points, and even rough first drafts for articles and pitches. This seriously sped up our content pipeline. We trained the AI on Improvado’s own tech docs and our best-performing articles to keep the brand voice consistent.
3. Hyper-Targeted Media Outreach: Our media list went way beyond job titles. We used AI-based influencer platforms that analyzed a journalist’s recent articles, their social media chatter, and professional networks to find people who were genuinely covering AI and marketing analytics. This allowed for personalized pitches where we could reference specific articles they’d already written, making the outreach feel much more like a relevant conversation.
4. Data-Backed Storytelling: Every single claim was backed by data. We used our own Improvado platform to pull anonymized, aggregated client data (with permission, of course) to prove the impact of AI. For example, we could show that AI-powered anomaly detection cut ad spend waste by an average of 15% for our clients.

Creative Approach: The Human-AI Partnership

Our creative work was all about showing a partnership between human marketers and efficient AI. We made a conscious effort to position AI as an indispensable co-pilot, not a replacement for a human job. Our main creative assets were:

• Thought Leadership Articles: We published long-form articles on sites like MarTech Series and MarketingProfs that got into the weeds of our AI applications. One piece, “The AI-Powered CMO: Working through Budget Shifts with Predictive Precision,” did especially well.
• Data Visualization Infographics: We turned our platform’s data into sharp infographics showing the before-and-after of using AI on campaign performance. These were great for embedding in articles or just sharing on LinkedIn.
• Executive Interviews and Bylines: Our CEO and Head of Product gave expert commentary for various outlets, with AI helping to draft and refine the key messages. We landed bylines in places like Forbes and Harvard Business Review, where we focused on the bigger business strategy behind using AI.

One of the best little things we did was create a small graphic for our visuals. It showed an “AI brain” icon working alongside a human analyst icon. It sounds simple, but it was a really effective visual shorthand for the partnership story we were telling, and it resonated with people.

Targeting: Precision Over Volume

We were extremely specific with our targeting. We zeroed in on:

• Tier-1 Marketing and Technology Publications: Places we knew were read by CMOs, VPs of marketing, and data analysts.
• Vertical-Specific Trade Publications: For industries like e-commerce and SaaS where we had a lot of customers, we went after the niche publications those pros read.
• Influential Industry Bloggers and Podcasters: We used our AI tools to find people with high “authority scores” and engaged audiences who were already talking about martech and AI.

We deliberately avoided blasting our news out to generic wire services. The entire goal was getting quality placements in front of actual decision-makers, not just racking up a high number of mentions in irrelevant blogs. That means you have to get good at saying “no” to opportunities that aren’t a perfect fit, which is a tough discipline.

Performance Metrics and Analysis

The campaign got great results and really proved that an AI-integrated PR approach works. We tracked everything from media mentions to direct business impact.

Media Coverage & Sentiment

• Total Media Mentions: We got 128 unique mentions in online publications, podcasts, and newsletters. That’s a 45% increase over the previous quarter when we were doing things mostly by hand.
• Tier-1 Placements: We landed 18 articles in top-tier outlets like Forbes, TechCrunch, and the Wall Street Journal’s CIO Journal. This was a 3.5x improvement on our benchmark.
• Sentiment Score: Using AI sentiment analysis, our media coverage scored an 8.2 out of 10, which was predominantly positive and neutral. This score was 28% higher than our baseline, telling us the nuanced story was landing correctly.
• Share of Voice: According to our Cision monitoring reports, Improvado’s share of voice for the term “AI in marketing analytics” jumped by 15 percentage points among our competitors during the campaign.

Website Traffic & Lead Generation

Connecting PR work to actual business results was maybe the most important part of this whole thing. We set up specific tracking URLs for every piece of PR content to watch the referral traffic.

• Website Referral Traffic from PR: We tracked 18,500 unique visitors coming directly from links in our media placements. That traffic had a 2.1% conversion rate to a demo request or content download.
• Marketing Qualified Leads (MQLs) from PR: The campaign generated 390 MQLs. These were people who downloaded a guide, signed up for a webinar, or asked for a demo after coming from an article we’d placed.
• Cost Per Lead (CPL): With a $75,000 budget and 390 MQLs, our cost per qualified lead came out to $192.31. For the leads that sales accepted (SALs), the CPL was $384.62, which is a perfectly healthy number for enterprise software.

I’ve always believed the real value of PR is the quality of the leads it generates, not just the vanity metrics. A lead that comes from an article in a publication they already trust has a built-in credibility that you just can’t get from cold outreach.

Engagement & Efficiency Metrics

• Journalist Engagement Rate: Our personalized pitches, which were informed by AI insights, got a 62% average open rate and a 28% response rate. That was a huge jump from our old averages of 45% open and 15% response.
• Content Ideation Time: Using AI for topic generation and outlining cut our content team’s average ideation time for a big article by 30%. This meant they could produce more good content in the same amount of time.
• Pitch Personalization Time: The AI tools automated a lot of the research for personalizing pitches, slashing the time it took from about 30 minutes per pitch down to 10. That let our PR team scale up their outreach without hiring more people.

What Worked Well

• AI-Powered Topic Discovery: This was the most impactful part of the whole campaign. Finding those niche, high-interest topics that were a perfect match for Improvado’s expertise let us create content that really stood out. For example, we wrote a piece on “Ethical AI in Marketing Data Management” that got a ton of traction because it hit on a real concern that our competitors weren’t talking about.
• Hyper-Personalized Outreach: Being able to reference a specific article a journalist wrote or connect our story to their known interests completely changed the game. It shifted the dynamic from a cold pitch to an informed conversation.
• Data-Driven Storytelling: Every claim we made was backed up with real, anonymized data from our own platform. This gave us instant credibility. Journalists love hard numbers and concrete examples, and our AI made it easy to find them.
• Executive Buy-in: Our leadership was all in. They made themselves available for interviews and approved content quickly which is often a major bottleneck in campaigns like this.

What Didn’t Work as Expected & Optimizations

Of course, not everything went perfectly. We learned a few things the hard way:

• Over-reliance on AI for Drafts: We got a little ahead of ourselves and tried letting the AI write full first drafts. It saved time, but the writing was often generic and just didn’t have the expert voice we needed. We quickly changed course and started using the AI just for outlines and initial research, with our human writers doing all the real creative work. The quality went way up after that.
• Initial Media Monitoring Setup: At first, our AI monitoring was set up too broadly and we were getting buried in irrelevant mentions. About halfway through, we had to pause and really tighten up our keyword lists and Boolean search strings to focus on specific product names, executives, and key industry phrases. This cut the noise by about 40% and made our analysis much cleaner.
• Attribution Complexity: Even with UTMs and referral tracking, fully attributing every lead to PR was tough. We saw lots of people interact with a PR article early on but then convert through a different channel later. We ended up adjusting our CRM’s attribution model to better account for “first touch” PR influence, which gave us a more realistic picture of its impact. Honestly, this is a constant challenge in multi-channel marketing and requires you to always be tweaking your attribution.

For our next campaigns, we’re looking at integrating AI more into our crisis communication planning, using it to spot negative sentiment shifts before they blow up. We also want to use AI to predict what kinds of stories will perform best with specific audiences based on past data, moving us from reactive to more predictive storytelling.

Conclusion

Improvado’s experience shows that AI in PR isn’t just an automation tool. It’s a catalyst for smarter, more targeted communication. When you strategically use AI for ideation, content, and outreach, you can get much higher media engagement and contribute directly to generating leads, which is fundamentally changing how public relations operates in 2026.