The Software-Defined Vehicle Will Reshape Automotive Market Research — Whether the Industry Is Ready or Not
A Pivotal Moment for Automotive Intelligence
The automotive industry is in the middle of what may be the most consequential product and business model transformation in its 130-year history. The convergence of electrification, software-defined vehicle (SDV) architecture, autonomous driving technology, and mobility-as-a-service is not merely changing what cars are — it is fundamentally restructuring who makes them, who buys them, and what 'customer value' means in the category. For market researchers serving the automotive vertical, the implications are profound and, frankly, uncomfortable: many of the methodological assumptions that have underpinned automotive consumer research for decades are now actively misleading.
The global automotive market generated approximately $2.9 trillion in revenue in 2023, with electric vehicles (EVs) reaching a 18% share of new car sales globally — up from just 4% in 2020 (IEA). The EV market alone is projected to grow at a CAGR of 17.8% through 2030 (BloombergNEF). Meanwhile, the software and services revenue layer — covering over-the-air (OTA) updates, subscription features, in-vehicle apps, and connected services — is forecast to represent a $650 billion annual opportunity by 2030 (McKinsey & Company). These are not incremental changes to be captured through legacy survey instruments and dealership intercept studies. They require a fundamental reimagination of how automotive market research is designed, fielded, and interpreted.
The Limitations of Legacy Automotive Research Frameworks
Let me be direct: the automotive research methodologies that dominated the industry from the 1970s through the 2010s were built for a world that no longer exists. Vehicle choice studies anchored on powertrain type, exterior styling, and dealer experience are increasingly inadequate for capturing the actual decision-making calculus of a modern EV buyer comparing a Tesla Model Y, a BYD Atto 3, and a Volkswagen ID.4. These buyers are evaluating software ecosystems, charging network reliability, OTA update cadence, and data privacy policies alongside traditional attributes — and standard conjoint designs rarely capture this complexity.
The J.D. Power Initial Quality Study — long the gold standard of automotive quality measurement — has itself had to substantially restructure its methodology to account for the growing volume of software-related problems that now dominate complaint data. In 2023, technology and infotainment issues accounted for four of the top ten problem categories in new vehicle ownership research. This is not a marginal adjustment; it signals that automotive quality, satisfaction, and brand perception are now substantially mediated by software experience — a domain that traditional automotive researchers are underequipped to measure.
Opinion: The automotive research firms that will lead this decade are those investing now in UX research capabilities, digital behavioral analytics, and software experience measurement — not those doubling down on optimizing legacy survey instruments designed for an ICE-era market.
The Software-Defined Vehicle Changes Everything About Customer Experience Research
The SDV paradigm — championed most aggressively by Tesla and now being pursued by virtually every major OEM through platforms like Volkswagen Group's E3 architecture, GM's Ultifi platform, and Stellantis's STLA Brain — means that a vehicle's capabilities, and therefore the customer experience it delivers, can change continuously post-purchase through software updates. This creates a research challenge with no direct precedent in automotive history: how do you measure brand loyalty, product satisfaction, or feature utility when the product itself is a moving target?
Traditional longitudinal ownership satisfaction studies — conducted at 90-day, 6-month, and 12-month post-purchase intervals — were designed for a world where the vehicle delivered on day one was the vehicle the customer would own for the life of the product. In an SDV world, Tesla owners may receive new features — Full Self-Driving capability updates, new entertainment applications, battery management improvements — months or years into ownership. Satisfaction at 12 months may be structurally different from satisfaction at 24 months not because of any change in owner behavior but because the manufacturer has continuously modified the product. Our research models need to account for this.
Electrification and the Collapse of Traditional Consideration Funnel Research
The automotive consideration funnel — awareness, consideration, preference, purchase intent — has been a research workhorse for decades. In the EV era, it is breaking down in at least three important ways. First, consideration sets are becoming dramatically more international. Chinese OEMs including BYD, NIO, and SAIC Motor are now active competitors in European and emerging markets, requiring competitive tracking studies to expand their brand universe significantly. Second, the purchase channel is being disrupted: Tesla's direct-to-consumer model has been followed by Rivian, Lucid, and elements of Ford's EV go-to-market strategy, meaning dealership-mediated purchase experience research captures a shrinking portion of the relevant transaction landscape.
Third — and most importantly for researchers — range anxiety, charging infrastructure confidence, and total cost of ownership calculations now feature prominently in the consideration process, requiring new attitudinal measurement constructs that most legacy automotive tracking studies have not yet integrated. Researchers at firms like Ipsos Automotive, Escalent, and AutoPacific are actively developing new EV-specific consideration frameworks, but the industry has yet to converge on a standard approach.
What Automotive Researchers Must Do Differently Now
Addressing the methodological gaps outlined above requires concrete action across several dimensions:
- Integrate behavioral data with attitudinal surveys: Connected vehicle data, app usage analytics, and charging behavior logs provide behavioral ground truth that corrects for the well-documented gap between what automotive consumers say and what they actually do. OEMs are beginning to share anonymized behavioral datasets with research partners — researchers who develop the technical capability to work with these data streams will have a durable competitive advantage.
- Build UX research into core automotive offerings: Software experience evaluation requires human factors expertise, usability testing protocols, and digital journey mapping — disciplines traditionally housed in tech sector research. Automotive research practices that build or acquire these capabilities will be positioned to serve the emerging SDV intelligence needs of OEM clients.
- Redesign tracking studies for continuous product evolution: Longitudinal satisfaction research in the SDV era should include software version tracking, OTA update exposure flags, and feature adoption metrics alongside traditional satisfaction dimensions.
- Expand competitive tracking to include non-traditional entrants: Any automotive competitive intelligence product that does not include BYD, CATL's OEM supply dynamics, and the software platform strategies of major Chinese OEMs is providing an incomplete picture of the competitive landscape.
- Apply AI-augmented analysis to large-scale verbatim data: The volume of unstructured feedback from connected vehicle ecosystems — app reviews, owner forum posts, social media, call center transcripts — is now far too large for manual thematic analysis. AI-powered text analytics platforms such as Luminoso, Medallia, and Qualtrics iQ are essential for extracting signal from this data at scale.
"The automotive researchers who treat electrification as a product variant to be added to existing survey frameworks are making a category error. EVs are not just differently powered cars — they are the leading edge of a fundamentally different relationship between automakers, software, and customers. Our research infrastructure must reflect that reality."
The Opportunity Ahead
Despite the methodological disruption, the automotive sector offers extraordinary research opportunity for practitioners willing to adapt. OEMs are investing heavily in consumer intelligence as they navigate the riskiest product portfolio transitions in their histories. Tier 1 suppliers are commissioning research to understand where value will accrue in the SDV stack. Private equity firms are funding hundreds of mobility startups that need market sizing, competitive positioning, and consumer validation research. Regulators — including NHTSA, the European Commission, and China's MIIT — are demanding evidence-based safety and consumer protection research on autonomous and connected vehicle systems.
The global automotive market research segment is itself forecast to grow from approximately $4.1 billion in 2023 to $6.8 billion by 2030. But that growth will not be distributed evenly. It will concentrate in firms that have invested in the capabilities — technical, methodological, and domain-specific — to serve an industry in genuine transformation. The window to build those capabilities is now. Researchers who wait until the methodological consensus has settled will find themselves competing on price in a commoditized market. Those who lead the methodological innovation will define the field.