“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 Weather-Driven Sales Patterns
One of the most revealing moments in my consulting work with a large omnichannel retailer came when an AI-powered analytics platform surfaced a customer behavior pattern that had eluded our traditional reporting. During an assessment of their digital transformation strategy, we deployed a solution that could analyze granular clickstream data alongside purchase histories and external signals, such as weather and local events.
Unexpectedly, the AI identified a recurring spike in online searches for specific home appliances on warm weekday evenings, correlating not with planned campaigns, but with sudden local temperature increases. The data showed that conversion rates for these products were significantly higher within a narrow three-hour window. This insight was not visible through standard dashboards, which aggregated data too broadly. As a result, we adjusted the promotional calendar and began running targeted, time-sensitive offers only when the model predicted these micro-spikes. This led to a measurable increase in both conversion rate and average order value, and it fundamentally changed how the retailer approached micro-segmentation and event-driven marketing.
The practical lesson from this experience is clear: AI analytics are most valuable when they are allowed to interrogate data at a level of detail and context that humans typically ignore, and when you are ready to act quickly on what is discovered. Too often, companies implement AI as a layer atop existing dashboards without changing how they operationalize insights. My advice is to ensure that your analytics tools are integrated into a workflow where insights can translate immediately into action, whether that is campaign adjustments, merchandising, or supply chain decisions.
For any business leader considering AI-driven analytics, focus on use cases where uncovering non-obvious patterns or correlations can drive specific business outcomes. Maintain a tight feedback loop between what the analytics uncover and how your team can respond. The value is rarely in the volume of data, but in the specificity and timeliness of the actionable insight. This approach has consistently yielded the most meaningful results in my own practice and in the work we lead at ECDMA.
Eugene Mischenko, President, E-Commerce & Digital Marketing Association
AI Uncovers Shipping Algorithm Flaw, Cuts Costs 23%
One of our most eye-opening AI-driven discoveries came while analyzing geographic distribution patterns for a rapidly growing DTC brand. Our AI analytics platform identified an unexpected correlation between their West Coast fulfillment delays and specific product categories – something that wasn’t obvious in standard reports.
The AI flagged that their heaviest items were causing disproportionate delays when shipped from their California 3PL, despite that facility handling similar volumes as their other locations. Diving deeper, we discovered their West Coast partner was using a different cartonization algorithm that wasn’t optimized for their product mix, creating inefficiencies across their entire distribution network.
By reconfiguring their inventory allocation based on this AI insight, we helped them reduce shipping costs by 23% while improving delivery times by nearly two days. What’s remarkable is this wasn’t a problem they were actively trying to solve – the AI surfaced it by recognizing patterns humans simply couldn’t see in the data.
For businesses looking to leverage AI in their analytics, my advice is threefold:
First, focus on clean data inputs. Even the most sophisticated AI can’t deliver reliable insights from poor-quality data. Establish rigorous standards for data collection across your logistics operations.
Second, maintain the human element. AI excels at identifying patterns, but industry experts need to contextualize those findings. At Fulfill.com, our team’s 3PL expertise helps translate AI-generated insights into practical fulfillment strategies.
Finally, start with specific business challenges rather than implementing AI for its own sake. The most valuable applications often address pain points like inventory forecasting, carrier performance optimization, or fulfillment network design – areas where small improvements drive significant ROI.
Joe Spisak, CEO, Fulfill.com
Mobile-to-Desktop Pattern Reduces Cart Abandonment Rates
I once used an AI-powered analytics tool to analyze customer behavior patterns in an e-commerce business. The tool revealed that a significant portion of abandoned cart activity came from customers who were browsing on mobile devices but completing purchases on desktop. This insight was unexpected but helped us shift our marketing efforts, optimizing the mobile checkout experience and targeting these users with tailored retargeting ads. The result was a measurable reduction in cart abandonment rates and an increase in conversions. My advice to others looking to leverage AI in analytics is to let the data guide your decisions, even when the insights are unexpected. AI tools are powerful at uncovering hidden patterns that human analysis might miss. It’s important to use the tool’s findings to adjust strategies quickly and experiment with new approaches based on the insights AI provides. It’s not just about the numbers but understanding the story behind them.
Georgi Petrov, CMO, Entrepreneur, and Content Creator, AIG MARKETER
Small Forum Drives Triple Conversion Rates
A while ago, I ran Google Analytics data through an AI tool called PaveAI. I expected the usual—bounce rates, top pages, traffic sources. But it flagged something I hadn’t noticed. A specific referral link from a small industry forum was driving a surprising amount of high-converting traffic. People coming from that thread were converting at over three times the average and spending way more time on the site.
So that insight changed how I allocated budget. Instead of pushing more into paid channels that were getting less efficient, I doubled down on content tailored to that niche audience. I also started sharing it in similar communities. Over the next two months, CPC dropped by around 40 percent and CAC improved a lot. And all of that happened without touching the product or offer.
What really stood out wasn’t just the traffic source. It was the mindset shift. A lot of people use analytics to back up what they already think. But AI tools can do more than just summarize. They can surface stuff you didn’t even know to look for. That said, they need clean data and a clear focus.
So if you’re using AI in analytics, stick to one or two KPIs that actually matter for your stage. Set the tool up to flag anomalies instead of just giving you a dashboard full of averages. Because that’s where the interesting stuff usually lives.
Josiah Roche, Fractional CMO, JRR Marketing
AI Analysis Reveals Critical Timing in Affiliate Funnel
As a Director of Marketing in an affiliate network, I’ve seen how AI-powered analytics significantly enhance our strategies. By implementing a machine learning tool to analyze clicks and conversions, we identified critical conversion drop-off points in the affiliate funnel. A surprising insight was that timing affected performance, providing us with valuable information to optimize our affiliate strategies effectively.
Michael Kazula, Director of Marketing, Olavivo
Low-Value Customers Generate Unexpected Referral Growth
AI-powered analytics tools can enhance decision-making by revealing hidden insights from customer data. For instance, one company discovered that a group of customers, previously deemed low-value, actually generated a significant number of referrals due to their high engagement within a community. This unexpected finding presented a new opportunity for growth, demonstrating the power of AI in identifying overlooked market potential.
Mohammed Kamal, Business Development Manager, Olavivo
Small Customer Segment Drives Disproportionate Revenue Growth
One specific instance where an AI-powered analytics tool uncovered an unexpected insight was during a sales performance review last year. We used an AI platform to analyze customer behavior patterns, and it revealed that a small segment of repeat customers was responsible for a disproportionately high percentage of revenue—far more than our traditional reports showed. This insight prompted us to tailor marketing campaigns specifically for that segment, focusing on loyalty rewards and personalized offers. The result was a 20% increase in repeat purchases within three months. My advice to others looking to leverage AI in analytics is to stay open-minded and dig deeper into the data AI surfaces. AI can highlight trends you might never have spotted otherwise, but it’s essential to combine those insights with human judgment to develop actionable strategies. Don’t just rely on AI to present data—use it as a starting point to ask better questions and drive smarter decisions.
Nikita Sherbina, Co-Founder & CEO, AIScreen
