Inside the EV Research Revolution: A Conversation with Automotive Market Intelligence Expert Dr. Priya Nair
Introduction
The automotive industry is in the throes of its most significant structural transformation since the invention of the internal combustion engine. The global electric vehicle (EV) market, valued at $388.1 billion in 2023, is projected to surge to over $1.58 trillion by 2030, representing a CAGR of approximately 22.5% (Fortune Business Insights). Against this backdrop, the demands placed on automotive market researchers have never been more acute.
We sat down with Dr. Priya Nair, Director of Automotive Consumer Intelligence at a leading global research consultancy and a former senior analyst at J.D. Power's automotive practice, to discuss how market research is evolving to keep pace with the EV transition, what new methodologies are proving most valuable, and where she sees the biggest knowledge gaps in the industry today.
On the Changing Nature of Automotive Research
Research Community: Dr. Nair, you've been working in automotive market research for nearly 15 years. How has the discipline changed, particularly with the rise of EVs?
Dr. Priya Nair: It's been a genuinely seismic shift. When I started out, automotive research was largely about optimizing the showroom experience, understanding brand loyalty dynamics between legacy OEMs, and tracking powertrain preference between petrol and diesel. Those questions haven't disappeared, but they've been completely overshadowed by a new set of challenges that are frankly much more complex.
The EV transition is forcing researchers to grapple with a consumer who is making purchasing decisions about a product category they have very limited lived experience with. Range anxiety, charging infrastructure uncertainty, total cost of ownership calculations — these are genuinely novel cognitive tasks for most car buyers, and our research instruments have to be designed with that in mind. You can't just swap the word 'petrol' for 'electric' in a 2010-era purchase funnel survey and expect meaningful data.
"The EV consumer is making decisions under conditions of genuine uncertainty — about technology, infrastructure, policy, and residual value. Our research has to honor that complexity rather than flatten it."
— Dr. Priya Nair
Research Community: What specific methodological adaptations have you found most valuable for EV consumer research?
Dr. Priya Nair: Several things have proven transformative. First, we've moved heavily toward scenario-based conjoint analysis, where we present respondents with realistic purchase scenarios that vary systematically across range, charging speed, charging network availability, price, and government incentive levels. This is infinitely more revealing than direct preference questions because it forces real trade-off thinking.
We ran a large-scale conjoint study for a major European OEM in 2023 — over 4,000 respondents across five markets — and the findings genuinely surprised the client. Range anxiety, which the industry had assumed was the dominant barrier, was actually ranked third behind charging network convenience and upfront cost uncertainty in most markets. That insight directly shaped how the client structured its communication strategy and dealer training program.
Second, we've invested heavily in longitudinal panel studies tracking actual EV owners over time. Real-world experience data is priceless in this space. The satisfaction drivers for EV owners at three months post-purchase look very different from those at 18 months, when the novelty has worn off and the practical realities of home charging logistics, long-distance trip planning, and software update experiences start to dominate. J.D. Power's Electric Vehicle Experience (EVX) Ownership Study is the industry benchmark here, and it's been fascinating to see how the satisfaction landscape shifts year over year as the population of EV owners becomes less early-adopter dominated.
On the Role of Data Analytics and AI in Automotive Research
Research Community: How is the explosion of connected vehicle data changing what automotive researchers can know about consumers?
Dr. Priya Nair: It's both exciting and ethically complex. Modern vehicles — particularly EVs from Tesla, Rivian, and the legacy OEMs' newer platforms — are generating extraordinary volumes of behavioral data: driving patterns, charging behavior, feature utilization, software interaction logs. This telemetric data, when researchers can access it through appropriate consent frameworks, provides a revealed preference dataset of unprecedented richness.
We partnered with a premium OEM last year on a study that combined telematics data from a consented owner panel with attitudinal surveys. The correlations were revealing. For example, owners who reported high satisfaction with their vehicle's OTA (over-the-air) software update process in surveys also showed measurably different driving engagement patterns in the telematics data — more consistent use of advanced driver assistance features, longer average trip distances, lower rates of charging anxiety behaviors. That kind of triangulation between what people say and what they do is the gold standard of automotive research, and telematics is making it possible at scale.
On the AI side, we're using large language model-assisted analysis for open-ended verbatim coding in owner satisfaction studies. When you're processing 50,000 open text responses about vehicle ownership experience, manual thematic coding is simply not viable. Tools built on GPT-4 and similar architectures, with careful human validation loops, have cut our analysis turnaround time by over 40% while maintaining coding reliability.
On Industry Gaps and Emerging Research Priorities
Research Community: Where do you see the biggest knowledge gaps in automotive market research right now?
Dr. Priya Nair: Three areas stand out. First, the used EV market is massively under-researched. The first wave of EVs — Nissan Leafs from 2011-2015, early Tesla Model Ses — are now entering the used car market at scale, and we know almost nothing about the consumer psychology around buying a used EV. Residual value uncertainty, battery degradation anxiety, lack of OEM warranty coverage — these are all novel barriers that don't have equivalents in the internal combustion used car market.
Second, fleet and commercial EV adoption is moving faster than most retail forecasts anticipated, driven by corporate sustainability commitments and favourable lease economics, but the research base is thin. Fleet managers make purchasing decisions through a completely different calculus than retail consumers, and most of our existing research frameworks are designed for the latter.
Third, and perhaps most critically, we need much better research on EV adoption in emerging markets. The narrative has been dominated by US, European, and Chinese data. But markets like India, Brazil, and Southeast Asia are seeing rapidly accelerating EV adoption with consumer profiles, infrastructure realities, and value equation dynamics that are fundamentally different. Applying Western research frameworks uncritically to these markets is producing seriously misleading insights.
"We know a great deal about how a German or Californian consumer thinks about EV adoption. We know almost nothing about how a middle-income consumer in Bangalore or Jakarta makes the same decision. That's a massive strategic blind spot for global OEMs."
— Dr. Priya Nair
Advice for Automotive Market Researchers
Research Community: What advice would you give to researchers newer to the automotive space?
Dr. Priya Nair: Three things. First, get technically literate. You don't need to be an engineer, but you do need to understand how EVs work, what the real constraints are around battery chemistry and charging physics, and how the dealer and direct-to-consumer distribution models differ. Respondents and clients will respect you more, and your questionnaire design will be better for it. The SAE International and the International Energy Agency both publish accessible technical primers that I'd recommend to any automotive researcher.
Second, befriend the data science team. The future of automotive research is at the intersection of survey-based attitudinal research and behavioral data from telematics, digital retailing platforms, and service center records. Researchers who can bridge those worlds will be invaluable. Those who can't will be disintermediated.
Third, challenge your sample assumptions relentlessly. EV research panels still skew heavily toward tech-savvy, high-income, urban, and male respondents. If your sample doesn't reflect the actual diversity of the emerging EV buyer — which increasingly includes suburban families, lower-income lease customers, and fleet drivers — your insights will be systematically biased, and your clients' strategies will suffer as a result.
Closing Thoughts
The automotive market research landscape of 2024 is a genuinely exciting place to work — intellectually demanding, commercially consequential, and rapidly evolving. Dr. Nair's insights underscore that the researchers best positioned to add value in this environment are those who combine methodological rigor with deep sector knowledge, a willingness to integrate diverse data sources, and the intellectual humility to question assumptions that worked perfectly well in a pre-EV world.
As the industry navigates the twin challenges of electrification and digitalization, the quality of its market intelligence will be a critical determinant of which OEMs, suppliers, and new entrants emerge as the long-term winners.