“Can you share a specific instance where an AI-powered analytics tool helped you uncover an unexpected business insight? What advice would you give to others looking to leverage AI in their analytics?”
Here is what 7 thought leaders had to say.
AI Reveals Hidden Value in Detox Content
One of the most surprising insights came from using HubSpot’s AI-driven behavior segmentation to analyze social media clickthroughs for a concierge wellness client. The data revealed that content around detox protocols—previously thought to be low-performing—actually had high re-engagement rates when paired with provider-led video. This shifted our creative strategy overnight, boosting lead-to-booking conversions by 29%. My advice? Don’t just look at top-line metrics. Train your team to ask ‘why’ behind the AI outputs. The nuance often lives in the second layer of pattern recognition.
Paige Michael, RN, Digital Marketing Strategist, Empathy First Media
AI Uncovers Cart Abandonment Pattern Worth Millions
I recently used an AI-powered analytics tool to analyze customer behavior on an e-commerce site. The tool revealed an unexpected trend: a significant portion of users were abandoning their carts right before checkout, but they were returning later to complete the purchase. This insight helped us identify a pain point in the checkout process that wasn’t immediately obvious from traditional analytics.
We implemented some adjustments, like simplifying the checkout form and offering follow-up email reminders, which resulted in a noticeable increase in conversions. The AI tool also provided predictive insights, allowing us to anticipate customer actions and tailor the experience accordingly.
My advice to others looking to leverage AI in analytics is to focus on the value of uncovering hidden patterns and insights that you might overlook with traditional methods. Don’t just look at surface-level data—use AI to dive deeper into customer behaviors, interactions, and long-term trends. The more you let the AI uncover these nuances, the better your strategy and decision-making will be.
Georgi Petrov, CMO, Entrepreneur, and Content Creator, AIG MARKETER
Simple TikTok Clip Defies AI Expectations
One time we ran AI-based analysis on TikTok video performance, looking for patterns in scroll-stop rates and watch time. We expected the biggest spike to come from flashy intros or trending sounds. Instead, the tool flagged a random clip with zero edits but clear product focus and a calm tone. It crushed our assumptions and made us rethink how we define “attention-grabbing.”
If you’re adding AI to your analytics stack, don’t chase every shiny insight. Start with one question you actually need answered, then test what the tool finds against what you know from experience. AI will show you patterns—but you still need to decide which ones matter. Use it to challenge your gut, not replace it.
Natalia Lavrenenko, UGC manager/Marketing manager, Rathly
WordLift SEO Boosts Niche Fish Tank Sales
I’ve actually had some great results using WordLift’s AI-driven SEO platform for unexpected business insight. One particular client was a luxury fish tank e-commerce brand we worked with using WordLift.
At the beginning of the campaign, the brand’s goal was to just increase their organic traffic and sales. As we dug deeper with Wordlift’s semantic analysis, we found that certain niche product categories were attracting more user engagement than anticipated.
We then decided to focus more on these high-performing categories by improving content and internal linking strategies. This soon led to a 21% boost in traffic and an amazing 39% increase in sales. Needless to say, the results surprised both our and the client’s expectations.
I now tell those starting out with AI analytic tools to start out with tools like Wordlift that not only have semantic analysis but also knowledge graph integration.
Features in AI-powered analytics tools let you see patterns in user behaviour and how your content is performing. You can then use that data to make more informed decisions moving forward.
Adam Haworth, Founder, Contactora
AI Catches $30,000 Campaign Error in Minutes
An AI analytics tool once uncovered a $30,000 issue in under 20 minutes when a campaign’s CPC suddenly spiked. A custom GPT-4 script, connected to Piwik PRO, flagged an unusual change in traffic sources. Specifically, there was a surge in low-quality display placements. The campaign hadn’t been touched in weeks, so this stood out. It turned out a media buyer had changed one setting, unintentionally opening the campaign to poor-quality traffic.
Without real-time AI monitoring, this would’ve gone unnoticed for days. So AI here wasn’t just speeding up reports or making dashboards prettier. It was catching things no one was actively looking for.
By feeding structured data exports into GPT-4 and prompting it to compare shifts across timeframes, it surfaced subtle but important changes. For example, a sudden drop in conversion rate on iOS after a layout tweak. This happened before it turned into an expensive problem.
For anyone using AI in analytics, it helps to stop thinking of it as just a reporting tool. It works better as an always-on analyst that spots patterns and flags risks. Off-the-shelf tools are fine, but the real value shows up when you tailor AI to your business logic.
So train it with your own questions. The quality of insights depends on how specific your prompts are. Broad questions get vague answers. Specific ones get you what you actually need.
Josiah Roche, Fractional CMO, JRR Marketing
Platform Navigation Insight Transforms User Experience
We experimented with an AI analytics tool internally to better understand how our own team and a few pilot users interacted with parts of the Bryt platform. One insight that caught us off guard was how frequently users bounced between the loan details and document management screens. It wasn’t something we’d flagged before, but the AI tool helped us spot the inefficiency. That led us to rethink the layout and bring more context directly into the loan view, which made navigation faster and more intuitive.
For others looking to use AI in analytics: Don’t expect magic. Use it to confirm or challenge what you think you know about your workflows. And once you spot a pattern, dig in manually to understand the “why” before you act. That balance worked well for us.
Bob Schulte, Founder, Bryt Software LLC
Silent Email Users Drive Unexpected Churn Rate
We were running a decent retention game. Or so we thought.
Churn was steady, customers seemed satisfied, and the usual metrics looked healthy. Enter our AI-powered analytics tool. Within weeks, it flagged something subtle but dangerous.
Users who engaged only with our email support had a 40 percent higher churn rate. No dramatic complaints. No refund requests. Just quiet exits.
We would never have caught that. The human eye is lazy with patterns that do not scream. AI is not.
This insight pushed us to act fast. We added a proactive outreach layer for email-only users and started nudging them into live chat and community groups. Three months later, churn dropped by nearly 18 percent.
What is my advice to others?
Stop using AI to confirm what you already know. That is a vanity exercise. Use it to find what hides under the surface. Give it permission to surprise you. Because the real value of AI analytics is not in dashboards. It is in blindsides that save your business before you even know you are in trouble.
Sahil Gandhi, Brand Strategist, Brand Professor
