In the past, automakers focused on "what words users search for" — search engines were the only entry point for users to obtain information, and capturing keywords meant capturing minds.

But the reality in 2026 is that more and more users are turning to large language models for questions before making car purchase decisions. AI has become the "first filter" between users and brands.

Does your brand appear in AI's responses? At what position is it recommended? What is the reason for the recommendation? — The answers to these questions are determining next month's test drive volume and delivery volume.

Based on the AI-restructured new scenario of automotive consumption, this article will deconstruct the core advantages of the IPS Intelligent Recommendation Index from three perspectives.

 

Three Mechanisms Ensure the Stability of IPS

Large language models inherently exhibit randomness in responses, with the same question yielding different results when asked multiple times. For brand monitoring metrics, data stability and accuracy are the core foundation. If model random errors cannot be mitigated, all subsequent data analysis and brand assessments become worthless.

To this end, IPS employs a three-tier verification mechanism to ensure the reliability of each data point.

 

Mechanism 1: Repeated Verification — Eliminating Random Fluctuations

We recommend asking each core question to the large language model at least 30 times, and taking the average as the final result. A sample size of 30 conforms to statistical standards and A/B testing logic, effectively offsetting random deviations and restoring the true data distribution.

 

Example: For the user need of "selecting an SUV with a budget of 300,000 RMB," the team queried the three major models—ChatGPT, Claude, and Gemini—30 times each, took the average, and used this data for the IPS index calculation.

Significance for automakers: To avoid the incidental bias of a single AI output, ultimately presenting the model's long-term, stable, and genuine preference for the brand.

 

Mechanism Two: Standardized Prompts—Not "Asking Casually"

Car users' questions vary widely; if test questions are not unified, the data will lose value for horizontal and vertical comparison.

IPS establishes a fixed standardized questioning system, standardizing test conditions from three dimensions: user persona, vehicle model category, and scenario tag.

Dimension	Category	Example
Persona	Age	20, 30, 40, 50, 60
Vehicle Model Category	Model/Energy Type	Sedan, SUV, MPV; Gasoline, New Energy, Hybrid
Scenario Tag	Use Case	Family, Commuting, Long-distance Travel

 

Key Principle: The question system is fixed and will not be temporarily adjusted based on “What do you want to test today?”. The question templates are consistent for the same market, same category, and same Persona, ensuring that the IPS has time continuity and vertical comparability.

Significance for Automakers: The IPS changes you see reflect the real shift of the brand’s position in the AI mind, not an illusion caused by “changes in the way questions are asked.”

 

Mechanism 3: Multi-Model Cross-Validation—Not Betting on Any Single Large Model

Different large models have their own training data, algorithmic preferences, and “personalities.” ChatGPT may focus more on technical parameters, Claude on user experience descriptions, and domestic models are more adapted to local contexts. Evaluation based on a single model is one-sided and cannot replicate the scenario of real users consulting with multiple tools.

IPSmoto simultaneously connects to major global large models, adapting to the consultation habits of users in different regional markets. Not only can you see the performance preferences of the brand across different large models, but you can also view the comprehensive IPS scores of multiple models.

Significance for automakers: This mechanism recreates the real-world scenario where users switch between multiple types of AI consultations, making the IPS score a collective objective assessment of brands by the world's leading large models.

 

Global Data Coverage: A Dynamic Monitoring System Grounded in Real Markets

PS differs from the static laboratory calculations of traditional metrics. It is a living data system based on real global markets, continuously and dynamically updated, achieving full coverage across three dimensions—market, category, and time—to meet automakers' needs for globalization and refined operations.

Geographic coverage: 16 major automotive markets

Region	Countries Covered
Asia	China, Japan, Thailand, Indonesia, Saudi Arabia
Europe	Germany, France, UK, Russia
Americas	United States, Brazil, Mexico
Others	Australia, UAE, Turkey, South Africa

In each market, tests are conducted using the local language (Chinese, English, Japanese, German, French, Portuguese, Thai, Arabic, etc.), rather than simply translating Chinese questions into the target language.

 

Significance for automakers: For brands going global, IPS provides a global AI recommendation map—showing at a glance which countries see your brand via AI and which ones overlook it.

 

IPS's Unique Value for the Automotive Industry: 5 Irreplaceable Reasons

If the first two sections answered “Is IPS credible?”, then this section answers “What is IPS good for?”.

Below are 5 irreplaceable value dimensions of IPS for automotive brands:

 

Value #1: Advance Prediction—Intervening Before the Consumer Speaks

Traditional research measures post-purchase satisfaction of “existing buyers”—the evaluation comes out after the car is sold. That is a “rearview mirror”.

IPS measures the probability that AI recommends a brand during the consumer's early decision-making stage—before the user has even walked into a 4S store, or even decided which brands to look up, AI has already pushed the recommendation list to them. This is a “forward-looking radar”.

 

IPS focuses on the pre-decision scenario of car purchases, capturing the brand's AI performance during the stages of user consultation and car comparison. It predicts trends before users visit stores or place orders, serving as a "forward radar" for market analysis.

It is not only a brand health check tool but also a forward-looking forecast for automakers' terminal sales.

 

Value #2: Competitive Benchmarking — See the True Rankings on the "AI Shelf"

In the era of traditional search, competitor rankings could be optimized through paid placements, failing to reflect true brand mental competitiveness. However, AI recommendation rankings and exposure frequency cannot be influenced by payment; they are entirely determined by brand knowledge base accumulation, reputation building, and overall trustworthiness, truly reflecting brand strength.

With IPS, automakers can achieve multi-dimensional precise benchmarking:

Horizontal comparison: In the same market and category, what are your overall IPS (Recommendation Value), VS (Visibility), and RS (Priority) compared to competitors?

Vertical tracking: Over the past weeks and months, is your AI ranking rising or falling? What about your competitors?

Attribution analysis: In which dimension (smart driving? after-sales service? cost-effectiveness?) have competitors received higher AI evaluations?

 

Value #3: Dimensional Diagnosis — Know Why AI Recommends or Does Not Recommend You

IPS is not just a total score but a decomposable diagnostic system.

By analyzing the reasoning chain of AI recommendations, we can answer the following questions:

Diagnostic Dimension	What Problems Are Solved
VS/RS Power Analysis	Are you “forgotten by AI” (low VS) or “remembered by AI but ranked low” (low RS)?
Element Importance Analysis	When AI recommends you, what reasons are most frequently mentioned? Is it cost-effectiveness, smart driving, or brand reputation?
Demographic Difference Analysis	Does AI give the same recommendation reasons for users aged 25-30 and 40-45? Which group does your brand perform better in terms of recommendations?
Source Attribution Analysis	When AI cites your brand information, the main sources are the official website, vertical media, or self-media? Which sources have insufficient ratings?

This capability allows brand optimization to move beyond vague judgments and precisely pinpoint the root cause of the problem—is the content not understood? Is the source not trusted? Or does the product positioning lack differentiation among peers?

 

Value #4: Overseas Navigation—Using AI Data to Guide Localization Strategies.

Domestic automakers' overseas expansion has entered a phase of refined cultivation, but user preferences, competitive landscapes, and AI recommendation logic vary greatly across different markets. A one-size-fits-all operational strategy cannot meet localized needs.

IPS's coverage system across 16 countries provides Chinese brands with a visual map of global AI recommendation power:

Scenario examples:

In the Southeast Asian market, AI places more emphasis on cost-effectiveness and after-sales service networks——if your IPS in Thailand is lower than competitors', you may need to strengthen content around “maintenance convenience”;

In the European market, AI places more emphasis on safety ratings——if your IPS lags behind in Germany, you may need to reinforce authoritative certification information in your information sources.

IPS is not a post-hoc review tool used after going overseas, but a pre-navigation tool deployed before overseas expansion, providing precise guidance for localized content, product promotion, and market strategy.

 

Value #5: Internal KPI——Driving organizational transformation toward “AI-native” marketing

The long-term core value of IPS is to drive the fundamental upgrade of automakers' marketing from traditional traffic-based thinking to AI-native thinking.

Traditional KPI System	KPI System with IPS
Focuses on “how many pieces of content we published”	Focuses on “how many of our content pieces are cited by AI”
Focuses on “what rank our keywords are”	Focuses on “what priority rank AI assigns to us”
Focuses on “vertical media exposure”	Focuses on “frequency of brand mentions in AI responses”
Focuses on “official website traffic”	Focuses on “authority weight of our official website as a source cited by AI”

 

The goal of the marketing team has shifted from “producing more content” to “producing content that AI can understand and is willing to recommend.” This is not just a replacement of KPIs, but a fundamental upgrade in marketing thinking—moving from the “human-reading” era to the “machine-reading” era.

AI Era, IPS as the Core Metric of Automotive Brand Mindshare

Back to the title’s question: Why is IPS, covering mainstream AI models across 16 countries, trustworthy for automakers?

Because its data collection methodology has withstood the triple tests of repeatability, standardization, and cross-validation; because its coverage scope is sufficient to support effective benchmarking in global markets; and above all, because its five unique values for the automotive industry—ex-ante prediction, competitive benchmarking, dimensional diagnosis, overseas navigation, and internal KPI—directly address automakers’ core anxiety in the AI era:

“I don’t know what AI says about me when users ask it.”

IPS is the ruler that answers this question—it is not the only standard, but it might be the one you need most right now.