Our client, a growing regional chain of convenience stores, faced a classic retail paradox. Many of their most profitable locations were situated in dense urban centers, often just a stone's throw from major sports stadiums, concert venues, and city parks. While these locations benefited from steady foot traffic, they were completely failing to capitalize on the massive, temporary surges in demand driven by local events. Their pricing strategy was static, meaning a bottle of water cost the same on a quiet Tuesday afternoon as it did an hour before a championship baseball game let out. They were leaving an astronomical amount of money on the table, and they knew it.The initial attempts to solve this were manual, inconsistent, and ultimately ineffective. A regional manager might hear about a major concert and email a few store managers to 'raise prices on drinks and snacks.' This resulted in chaos. One store might raise a price by 25%, while another a mile away raised it by 100%, creating customer confusion and brand inconsistency. More importantly, there was no data backing these decisions. They had no visibility into what competitors were charging, nor did they have a systematic way of even knowing which events were happening. They recounted a painful anecdote about a marathon finish line that was set up two blocks from one of their flagship stores. It was a sweltering day, and they sold out of their entire stock of bottled water at the standard $1.99 price within 90 minutes. Meanwhile, a competitor across the street was selling the same item for $4.00 all afternoon, capturing thousands in pure margin that our client missed.That's when they contacted us at Iceberg Data. They didn't just need a list of events; they needed an automated intelligence engine that could translate real-world happenings into profitable, real-time business decisions. Our solution was to build a comprehensive data collection and analysis platform focused on what we called 'hyper-local demand triggers.' First, we deployed a fleet of web scrapers targeting a wide array of sources. We continuously monitored major ticketing platforms, city event calendars, and the official websites for every key venue within a 5-kilometer radius of their stores. This gave us a forward-looking view of any event with significant expected attendance.Second, we targeted their local competition. We knew we couldn't just walk into their competitors' stores to check prices. Instead, we scraped the publicly available data from popular food and grocery delivery platforms like DoorDash, Uber Eats, and Instacart. This allowed us to monitor, in near real-time, the pricing of a specific basket of high-volume goods (like 'SKU-WATER500ML' and 'SKU-ENERGYDRINK-RED') at competing stores in the immediate vicinity. Finally, we integrated a live weather forecast API, because a hot, sunny day for a street festival has a profoundly different impact on beverage sales than a cold, rainy one.The data was then fed into a central rules engine. Using geospatial analysis, the system automatically flagged any upcoming event with, for example, over 10,000 expected attendees within a 1.5km radius of a client store. It would then cross-reference the event type with the weather forecast to predict which product categories would be most impacted. If all conditions were met, it would trigger a pricing analysis, pulling the latest competitor data. The final output, as seen in our example, was a clear, actionable recommendation delivered directly to the store's POS system: 'Raise the price of SKU-WATER500ML to $3.49 for the duration of the event.' The justification was transparent, citing the event, the weather, and competitor pricing.We piloted the system at five of their highest-opportunity stores. The results were immediate and staggering. During the first month, one store located near a concert hall saw its gross margin on beverages and snacks increase by 42% on event nights. After a full quarter, the pilot stores were showing an average revenue lift of over 15% compared to control stores. Armed with this data, the client initiated a full rollout. Today, this dynamic, event-driven pricing strategy has become a core part of their revenue management, delivering a sustained 35% increase in gross margin on promotional categories and proving that the most valuable data often lies just outside a company's own four walls.