“Can you describe a creative way you’ve combined multiple AI analytics tools to gain deeper insights? What unexpected synergies did you discover?”
Here is what 8 thought leaders had to say.
Zendesk AI and Looker Reveal Hidden Customer Frustrations
We combined Zendesk AI with Looker Studio and OpenAI to analyse support ticket trends, customer sentiment, and agent performance in one view. Zendesk AI tagged intent and urgency, Looker tracked patterns over time, and OpenAI summarised qualitative feedback into key themes. The unexpected synergy? We spotted a recurring issue that wasn’t flagged in standard CSAT—customers were frustrated by timing, not answers. Fixing that improved both satisfaction and resolution time.
Paul Bichsel, CEO, SuccessCX
Triple AI Integration Uncovers Emotional User Conversion Barriers
One of the more creative and unexpectedly powerful combinations I’ve used involved layering data from three different AI analytics tools—Hotjar for behavioral heatmapping, OpenAI’s API for natural language processing, and Google Analytics 4 for quantitative traffic data. This trio helped us understand not just what users were doing, but why they were doing it—and where we were unintentionally losing them.
We started with Hotjar to visually see where users were dropping off or getting stuck on key landing pages. The scroll and click maps gave us surface-level insights—great for identifying friction points. But that alone didn’t tell us the motivation or sentiment behind the behavior.
So we pulled in OpenAI’s language model to analyze thousands of open-form survey responses and customer chat transcripts. We built a pipeline that tagged and categorized recurring themes and emotional sentiment in real-time. That was the real breakthrough—seeing users not just as data points, but understanding their frustrations, confusions, or motivations in context.
Then we synced these insights with GA4, segmenting by acquisition channel and user intent. That triangulation let us identify high-value users who intended to convert but didn’t—and more importantly, why.
The synergy came from combining emotional data (from AI NLP), visual data (from Hotjar), and performance data (from GA4). For example, we discovered that users from a specific paid campaign were getting confused not by UX layout, but by language—a single phrase on our pricing page that triggered distrust. That insight alone helped us reframe our copy and led to a 17% boost in conversion.
Lesson learned: when AI tools are used not in silos but as parts of a wider ecosystem, they become exponentially more valuable. It’s about context, not just data.
AI Tools Merge Behavior Data with Emotional Context
I recently combined AI-powered tools like Google Analytics, Hotjar, and a machine learning platform for customer sentiment analysis to gain deeper insights into user behavior on our website. By integrating these, I was able to see not only where users were clicking but also how they felt about specific content or features. Hotjar’s heatmaps showed me the exact areas where users interacted most, while Google Analytics tracked conversion paths. When I combined these with sentiment analysis, I found unexpected synergies—users who clicked on certain products were also expressing frustration in their comments about the checkout process. This helped me pinpoint both a usability issue and an emotional barrier, allowing us to tweak the user flow to increase conversions. The blend of behavioral data with emotional context offered a more holistic view, something I hadn’t anticipated. It was a powerful combination that informed smarter, more empathetic design decisions.
Nikita Sherbina, Co-Founder & CEO, AIScreen
Data Trio Predicts Intent Before Pricing Page
I combined Clearbit, Mutiny, and Mixpanel to understand which types of people were converting after seeing different versions of our messaging. Clearbit gave real-time firmographic data. Mutiny adjusted the landing page based on that data. Mixpanel tracked what happened after the click — scroll depth, button hovers, and time spent on important sections.
The insight came from connecting them. Alone, each tool was helpful. But together they gave context. So for example, knowing a visitor was a fintech founder from a five-person team explained why a short, benefit-driven headline performed better than a longer narrative for that segment. Mid-market marketing leads reacted differently. They needed more proof and more testimonials.
What stood out was how consistent some of these patterns became. Because after enough data, it got easier to predict who would bounce early and who was likely to book a demo. Even before they reached the pricing page. That made retargeting more focused. And CAC dropped because we weren’t wasting effort on low-intent visits.
This setup wasn’t plug and play. It took a lot of upfront testing to clean the signals and filter out noise. But once it was dialed in, it gave a much clearer picture of what messaging actually moved people. Not just what got clicks. So the real value came from how the tools worked together to surface intent earlier and more sharply than expected.
Josiah Roche, Fractional CMO, JRR Marketing
Hybrid Analytics Bridge the What and Why
One creative way that teams are combining various AI analytics tools is through “hybrid funnel analytics” — integrating behavioral analytics (like Mixpanel or Amplitude) with AI-powered customer sentiment analysis (like MonkeyLearn or OpenAI embeddings) and predictive churn modeling (via custom ML or tools like ChurnZero).
The Use Case:
1SaaS product wanting to reduce churn and enhance user activation.
Tools Combined:
1. Amplitude – to monitor user actions and product path (e.g., where the drop-offs occur).
2. MonkeyLearn or OpenAI Embedding API – to group and analyze free-form user feedback from surveys, chats, and NPS feedback.
3. Churn prediction model – trained on combined usage and sentiment data to forecast high-risk accounts pre-churn.
Surprising Synergy:
When behavioral data alone were analyzed, drop-offs were visible but hard to explain.
But when sentiment clusters were superimposed on top (i.e., users talking about “confusion,” “too slow,” or “love feature X”), it bridged the “what” (quant) and the “why” (qual).
Later, by having both datasets fed into a churn prediction model, the team learned:
– Users who struggled week 1 AND talked about “overwhelming UI” were 60% more likely to churn — even after they’d completed key onboarding steps.
– Customers who used 2+ core features but left neutral feedback were more likely to convert if nudged with customized onboarding.
Key Insight:
Consume does not equal delight. Combining usage analytics and natural language processing revealed hidden friction and conversion points that would have gone unnoticed.
Xi He, CEO, BoostVision
Behavior Data Plus Intent Prediction Boosts Conversions
At DesignRush, we used Hotjar’s AI-driven user behavior analytics and our own machine learning system’s predictive modeling to figure out where leads were dropping off. We had heatmaps, scroll data, and session replays from Hotjar that showed us what people were doing, but not why they were departing. We employed predictive scoring models that looked at past lead behavior, time on site, and navigation patterns to guess how likely it was that they would convert in real time.
We started stacking both datasets to find times when visitors were browsing, clicking, or hovering and looked like they were interested but were marked as unlikely to convert. That was when the synergy happened. In the beginning, it didn’t make sense. Then we figured out that a lot of these individuals were interacting a lot because they were confused, not because they were interested. That knowledge led to changes in the way we choose an agency and set prices.
After making those changes, we noticed a 17% increase in the number of qualifying leads that turned into customers over the course of six weeks. The surprising win was that by integrating surface-level behavior data with intent prediction, we were able to find blind spots that neither technology could find on its own. It showed us that great engagement doesn’t always guarantee high clarity, and that AI works best when it helps us understand the little differences between people.
Sergio Oliveira, Director of Development, DesignRush
AI Models Reveal Distinct Personalities Through Collaboration
In our experience working with multiple AI models, we’ve developed various approaches to leverage their unique characteristics and combine their outputs effectively. We frequently apply identical prompts across different models, then use meta prompts to synthesize their responses, either in comprehensive comparison tables or through voting systems where models express their stance on specific issues. These exercises have revealed fascinating insights into each model’s distinct “personality”:
Some models are like that super-direct uncle who tells you exactly what he thinks about your new haircut. Others are more like your diplomatic aunt who smoothly changes the subject when someone brings up politics at Thanksgiving. Then there’s the “creative” cousin who starts every story with “Imagine this”, and the professor-type who turns “what’s for lunch?” into a 5-page essay.
We’ve even conducted interesting experiments, such as having models collaborate on organizational structures for a 12-person team, where each produced uniquely different proposals, or implementing voting systems where one model tallies the yes/no responses from others. And more questions that don’t have single answers like forecasting, managerial decisions, opinions around which is better out of two, pros and cons of something, etc.
Fady S. Ghatas, CEO, Codenteam
LLM Integration Transforms Technical Support into Storytelling
In my current role as a Technical Services Engineer, I have been combining multiple AI analytics tools to deepen how we investigate and resolve customer data issues. The goal was to move from reactive support to more proactive, insight-driven solutions.
One creative example involved integrating LLM-based summarization (using Ollama) with SQL query automation and GitHub workflows. I built a lightweight internal system that interprets data anomalies, generates SQL queries from predefined templates, and produces summaries that non-technical stakeholders can easily understand. Instead of manually parsing logs or repeating diagnostics, the system suggests likely root causes, drafts responses, and flags similar issues from historical tickets.
To refine and validate the model’s output, I incorporated LangChain for structured prompt flows, vector database lookups to identify related past resolutions, and Lightdash to visualize trends across customer SDK versions, event attribute mismatches, and integration errors. What began as a tool to speed up investigation became a powerful storytelling engine across teams.
An unexpected synergy emerged when I combined low-code automation through GitHub Actions with LLM-powered analysis of logs and metadata. This pairing not only improved speed but also surfaced patterns we had not explicitly tracked. For example, we discovered that a spike in dropped events was tied to a specific browser SDK version only after AI summaries were aligned with telemetry and support case metadata.
This system improved response times and enabled our Engineering and Product teams to prioritize fixes based on real data patterns. It also empowered our Customer Success team with clear, technical narratives they could confidently share with clients.
Through this process, I realized that the real value of AI tools often comes not from using them individually, but from how they interact. When connected with the right context, structure, and human oversight, they amplify each other’s strengths and unlock insights that would be difficult to catch otherwise.
Prachi Tomar, Technical Services Engineer
