Electric Vehicle Market Surge: How Automotive Researchers Can Navigate the 2024–2030 Landscape

The Automotive Industry at a Crossroads

The global automotive market is undergoing one of the most profound transformations in its century-long history. Driven by electrification, connectivity, autonomous driving, and shared mobility — collectively known as the CASE megatrends — the industry is forcing market researchers to completely rethink how they gather, interpret, and present intelligence to decision-makers. According to a 2024 report by BloombergNEF, the global electric vehicle (EV) market is projected to reach $1.3 trillion by 2030, growing at a compound annual growth rate (CAGR) of 18.2% from 2024 onwards. For automotive market researchers, this is not just a headline — it is a mandate for methodological evolution.

Understanding this shift requires more than tracking sales figures. It demands deep consumer behavior analysis, competitive landscape mapping across traditional OEMs and new entrants like Tesla, BYD, and Rivian, and a granular appreciation of regulatory environments in different geographies. This article explores the key research dimensions automotive market researchers must prioritize in the years ahead.

Consumer Sentiment and Behavioral Shifts Around EVs

One of the most revealing research frontiers is the evolving consumer attitude toward EV adoption. A 2023 Deloitte Global Automotive Consumer Study, which surveyed over 26,000 consumers across 25 countries, found that 69% of respondents in China expressed a preference for battery electric vehicles (BEVs) as their next purchase, compared to just 38% in the United States and 34% in Germany. These regional disparities are not merely cultural — they reflect deeply embedded differences in charging infrastructure maturity, fuel price volatility, government incentive structures, and brand trust.

For researchers, this means segmentation strategies must go far beyond traditional demographic splits. Psychographic profiling — particularly around environmental identity, technology adoption propensity, and risk tolerance — is becoming a cornerstone of automotive consumer research. Tools like Qualtrics XM and SurveyMonkey Audience are being deployed alongside qualitative methods such as ethnographic in-home interviews to capture authentic friction points in the EV ownership journey, from range anxiety to home charging installation complexity.

Key Takeaway: Automotive market researchers who combine large-scale quantitative surveys with ethnographic observation will develop the most accurate picture of real-world EV adoption barriers — data that is invaluable for product development, marketing strategy, and dealer network planning.

Competitive Intelligence in a Fragmented OEM Landscape

The competitive dynamics of the automotive sector have never been more complex. Traditional OEMs such as Volkswagen Group, General Motors, and Stellantis are investing tens of billions of dollars in EV platforms while simultaneously defending ICE (internal combustion engine) revenue streams. Meanwhile, Chinese manufacturers — led by BYD, NIO, and SAIC — are aggressively expanding into European and Southeast Asian markets, fundamentally disrupting established competitive hierarchies.

Effective competitive intelligence (CI) in this environment requires a multi-source approach. Researchers should be synthesizing data from:

Patent filings: Tools like Derwent Innovation and PatSnap allow analysts to track R&D investment trends in battery technology, autonomous driving systems, and software-defined vehicle architectures.
Regulatory submissions: NHTSA in the US, UNECE in Europe, and MIIT in China publish vehicle homologation and safety recall data that serves as a leading indicator of product pipeline activity.
Social listening platforms: Brandwatch and Sprinklr can surface real-time consumer perception shifts around competing brands, often preceding formal sales data by several quarters.
Dealer channel research: Mystery shopping programs and dealer sentiment surveys, often overlooked, provide ground-level intelligence on pricing pressure and inventory dynamics.

The rise of software-defined vehicles (SDVs) also means that competitive analysis must now encompass technology partnerships — such as GM's collaboration with Cruise for autonomous driving and Ford's investment in Argo AI — as well as software platform ecosystems, creating an entirely new CI dimension for researchers.

Regulatory Mapping: A Non-Negotiable Research Discipline

Regulatory forces are reshaping automotive market structures with unprecedented speed. The European Union's landmark decision to ban the sale of new ICE vehicles by 2035, the US Inflation Reduction Act's EV tax credit provisions under Section 30D, and China's NEV mandate are all creating differentiated market trajectories that researchers must track meticulously.

Researchers working in automotive should establish a regulatory monitoring cadence that includes:

Monthly reviews of European Commission transport legislation updates
Quarterly analysis of US EPA and DOT rulemakings
Bi-annual mapping of incentive structures across key APAC markets

Industry associations such as the International Organization of Motor Vehicle Manufacturers (OICA), the Alliance for Automotive Innovation, and the European Automobile Manufacturers' Association (ACEA) publish position papers and lobbying disclosures that are rich secondary research resources often underutilized by market analysts.

Actionable Recommendations for Automotive Market Researchers

Given the pace of change in the automotive sector, researchers who want to deliver genuinely decision-grade intelligence should consider the following priorities:

Build longitudinal panel infrastructure: Tracking the same cohort of potential EV buyers over 12–24 months provides incomparably richer behavioral data than cross-sectional surveys. Panel management platforms like Dynata and Toluna can support this at scale.
Invest in conjoint analysis for pricing research: With EV price parity relative to ICE vehicles expected by 2026 in several segments (per BNEF modeling), understanding consumer willingness-to-pay across feature bundles — battery range, charging speed, software subscriptions — is critical. Adaptive choice-based conjoint (ACBC) methods are particularly well-suited to this complexity.
Integrate AI-powered trend analysis: Platforms like Crayon and Klue now incorporate machine learning to surface competitive signals at a speed and scale impossible for human analysts alone. For the automotive sector, where product cycles are compressing and new entrants emerge rapidly, AI-augmented CI is shifting from optional to essential.
Prioritize supply chain intelligence: Post-COVID disruptions and the geopolitical sensitivity of battery mineral supply chains (lithium, cobalt, nickel) mean that upstream market dynamics directly impact downstream product availability and pricing. Researchers should develop sourcing intelligence as a core competency.

Conclusion: Research as a Strategic Advantage

The automotive industry's transformation is creating extraordinary opportunity for market researchers who are willing to evolve their methodologies alongside the market itself. The companies that will win in the 2024–2030 EV era — whether they are OEMs, Tier 1 suppliers, charging infrastructure providers, or mobility service operators — will be those backed by the most rigorous, multi-dimensional market intelligence. For research professionals, the mandate is clear: broaden your data sources, deepen your consumer understanding, and sharpen your regulatory literacy. The road ahead is complex, but for well-equipped researchers, it represents the most intellectually rich environment the automotive sector has ever produced.