What is your understanding of IPS?

Most people define it as a ranking tool for competing car companies or a single data metric for predicting brand AI recommendations.

But in fact, IPS plays a dual role: on the offensive side, it acts as a lever to drive growth; on the defensive side, it serves as an early warning radar against the risk of AI hallucinations.

Below are three typical car company scenarios to illustrate how IPS solves the AI marketing challenges for different brands.

 

Scenario 1: The “AI Aphasia” of Traditional Car Companies

Traditional car companies face a harsh reality: AI may “not recognize” you anymore.

The car-buying journey of younger consumers is vastly different from that of their parents——they no longer learn about cars through 4S dealerships, TV ads, or web portals. Their first stop is to ask AI, “What car should I buy with a budget of 300,000 yuan?”

If your brand isn’t mentioned in the AI response, you won’t even enter the initial consideration set of younger consumers. This is the “AI Aphasia” facing traditional brands——not because product quality has declined, but because brand information has not been effectively indexed and understood by the AI knowledge base.

Addressing the pain points of transformation in the AI era, traditional car companies generally have three core needs, which IPS can precisely match and solve through exclusive data capabilities:

Core Challenge	Specific Manifestation	IPS Solution Path
Youth‑oriented transformation	Purchase demand among the 20‑30 age group is shifting away from traditional brands.	Use IPS to monitor the brand’s AI‑recommendation performance among young consumers and compare it with competitors.
Electrification narrative	Electric models of traditional brands are often “ignored” or “misclassified” in AI recommendations.	Conduct IPS factor analysis to verify whether electric models are correctly identified and recommended by AI systems.
Brand refresh	Brand identity, positioning, and product lines have been upgraded, but the AI’s “memory” still reflects the old version.	When a brand undergoes a youthful or premium repositioning, IPS can track whether AI perception updates accordingly.

 

Scenario 2: The “Cognitive Density” Battle of New Car Makers

The target customer group of new EV startups (ages 25-40) is precisely the demographic with the highest proportion of “ask AI first, then buy a car.” However, due to short brand heritage and thin AI knowledge base data, there is a general lack of cognitive density, along with development pain points such as difficulty verifying smart selling points and opaque overseas AI preferences.

IPS can comprehensively address these shortcomings: 

 

Scenario 3: The Battle for Luxury Brands' "AI Pricing Power"

Luxury brands face a unique dilemma in the AI era: Is AI's recommendation logic "cost-effectiveness first" or "quality first"?

If AI's recommendation algorithm is overly biased toward parameter comparison and price sensitivity, the "brand premium" of luxury brands will be severely diluted in AI recommendations—this is the last thing luxury brands want to see.

 

Luxury brands' IPS strategy should focus on the following aspects:

Core Demand	Specific Issues	IPS Solution Path
Dimension Focus	Should not pursue high scores in "cost-effectiveness," but should focus on high-end dimensions such as "quality," "driving experience," "safety," and "after-sales service."	IPS Element Analysis: AI Recommended Scores Under Different Keywords
Audience Precision	People aged 40-60 are the core purchasing power for luxury cars. What is AI's recommendation logic for this group?	IPS analysis by audience, continuously tracking AI recommendation performance for the 40-60 age group
Brand Story	Do AI recommendation reasons include luxury attributes such as brand history and craft heritage?	Deconstruct the sources of AI recommendations to verify whether the “Brand Story” is included in AI responses
Competitive Landscape	Do luxury brands compete with each other, or are they being squeezed out of recommendation lists by new players?	Track changes in IPS scores of luxury brands within the same class/type, and issue early warnings for “downgrade” risks

 

Deep Defense: How IPS Helps Automakers Mitigate the “AI Hallucination” Risk?

Examining the three types of automaker scenarios reveals that all brands face the same core issue: There is a gap between brand self-perception and AI's actual perception. This situation is also known as the “AI Hallucination”.

 

Compared to post-event public opinion remediation, IPS responds through the following methods:

• Anomaly Monitoring: Through monthly continuous tracking by IPS, abnormal fluctuations in brand AI recommendation data can be precisely captured, potential negative AI hallucination risks identified early, and risk warnings issued before impacting sales and reputation.
• Information Correction: GEO service providers can pinpoint where the AI obtained incorrect information based on IPS diagnostics, and then supplement the correct brand content accordingly.
• Rapid Response: When the AI describes the brand incorrectly, corrections can be made quickly through content optimization, generating and distributing suitable content to drive the AI knowledge base updates.

 

In the AI era, a brand is no longer just the sum of "user reputation," but the sum of "AI perception."

IPS truly appeals to automakers because it does not directly rely on user complaints or social media feedback, but systematically discovers "what the AI is saying about you" through data, and drives the brand to correct it at the fastest speed —— ensuring that the brand's "voice" and "health" in the AI world remain always controllable.

This is the fundamental reason why leading automakers have incorporated IPS monitoring into their brand risk management systems. Brand reputation is no longer solely guarded by the public relations department, but also needs to be protected by data systems, enabling mutual empowerment of brand growth and risk defense.