Pinpointing Prime Markets for EV Infrastructure Expansion Using Geospatial & Sentiment Data

A leading Electric Vehicle (EV) charging installation company, a pioneer in the Pacific Northwest, approached our team at Iceberg Data with a classic growth challenge. Having effectively saturated their home market, they were setting their sights on the booming Sun Belt states for expansion. The problem? They were flying blind. Their executive team was debating between Phoenix, Dallas, and Austin, but their decisions were based on intuition and high-level market reports that only provided generic, state-wide EV registration numbers. For a multi-million dollar investment in facilities, staff, and marketing, they needed granular, neighborhood-level intelligence to avoid a catastrophic misstep.

We knew that traditional market research wouldn't suffice. We proposed a data-driven solution: a custom 'EV Readiness Score' that would evaluate and rank individual zip codes within their target metropolitan areas. This wasn't just about finding where EV owners lived; it was about finding where future EV owners would live and what would motivate their purchase of a home charging solution.

Our first target was real estate data. We deployed a fleet of web scrapers across major real estate portals, but we weren't just looking at home prices. We analyzed millions of property descriptions for key phrases like 'EV ready,' 'Tesla charger,' '240V garage outlet,' and 'solar panels.' This provided a powerful proxy for current adoption among affluent, tech-forward homeowners. Simultaneously, we gathered data on property types, calculating the ratio of single-family homes (ideal for easy charger installation) to multi-unit dwellings, which represent a more complex but potentially lucrative B2B opportunity.

Next, we meticulously mapped the competitive landscape. Our scrapers canvassed local service directories like Yelp and Angi, as well as Google Maps, to identify every single 'electrician' and 'EV charger installer' in the target cities. We collected their listed services, hours, service areas, and, crucially, thousands of public customer reviews. Using Natural Language Processing (NLP), we analyzed this feedback to uncover recurring customer pain points. Phrases like 'long wait times,' 'unprofessional work,' 'hidden fees,' and 'confusing quotes' were cataloged and tagged by location. This intelligence was invaluable; it told our client not just where to expand, but how to differentiate their service by addressing these specific frustrations.

Then, we turned our attention to the public charging infrastructure. By scraping data from network maps like PlugShare and ChargePoint, we visualized the density, type (Level 2 vs. DC Fast), and user-reported operational status of every public charger. By overlaying this with our real estate data, we identified 'charging deserts': affluent neighborhoods with strong indicators for EV adoption but a glaring lack of reliable public charging. We hypothesized that residents in these areas would be highly motivated to invest in the convenience of a home charger, making these zip codes prime targets.

Finally, we brought in the voice of the local community. We monitored local city subreddits and EV-focused forums for discussions related to charging. We found threads where potential customers were actively seeking installer recommendations or venting about the lack of charging options at their workplaces or apartment buildings. This qualitative data provided the essential human context behind the numbers, confirming the market gaps our quantitative analysis had pinpointed.

The result was a transformative interactive dashboard. We synthesized these disparate datasets—real estate indicators, competitor weaknesses, infrastructure gaps, and public sentiment—into a single, unified view. Our client could now click on any zip code and instantly see its 'EV Readiness Score,' the number and average rating of local competitors, and a summary of the most common customer complaints in that area. The data was clear and compelling. The client had initially favored Austin, drawn by its reputation as a tech hub. Our analysis revealed that while Austin was a viable market, it was also becoming saturated with competitors who were already highly rated. In contrast, several affluent suburbs of Phoenix showed a 20% higher demand score, 35% less competition, and significantly more vocal consumer frustration with existing options. This data-driven insight completely reshaped their expansion strategy. They confidently launched in two high-potential Phoenix suburbs, armed with a marketing campaign that directly addressed the pain points we uncovered ('Tired of waiting? Get a guaranteed 2-week installation!'). The results were immediate and impactful, delivering a 40% higher lead conversion rate than their most optimistic projections and securing a powerful foothold in a vital new region.