Window Replacement: 2026 AI Market Discovery Index

Answer Capsule

In the window replacement category for June 2026, AI systems are concentrating buyer shortlists around a small set of brands, with Pella leading across all major buying stages. Andersen emerges as the strongest challenger, particularly in pricing and cost-related queries. Several national brands including JELD-WEN and Simonton appear frequently in AI responses but rarely earn ranked recommendations, exposing a consequential gap between visibility and shortlist eligibility.

Executive Summary

The window replacement category is undergoing a structural shift in how AI systems build buyer shortlists. Across 1,280 observations from six major AI platforms, Pella captured 21.1% of the modeled monthly AI opportunity value, nearly double the share of its nearest competitor. Pella achieved a 50.9% valid recommendation coverage rate, earning positive, ranked recommendations in more than half of all prompts where it appeared, and maintained an average recommended rank of 2.45, placing it consistently in the top three positions across platforms.

Andersen presents the most credible challenge, particularly in high-intent decision-stage queries. Andersen captured the highest single-cluster AI Authority Value in the pricing and cost segment at $1.12M, edging out Pella in that specific buying moment. Its average recommended rank of 1.77 across all platforms is the lowest in the category, indicating that when AI systems recommend Andersen, they tend to place it first or second.

Marvin holds a solid third position with a 34.5% valid recommendation coverage rate and a 26.8% top-three rate, but the gap between the top two brands and the rest of the field is substantial. Brands like JELD-WEN, Simonton, and Champion Windows show meaningful presence in AI responses but convert that presence into recommendations at very low rates. AI systems mention them factually without advancing them into buyer shortlists, a pattern with direct commercial consequences.

The AI Discovery Shift in Window Replacement

Traditional window replacement marketing has relied on brand awareness, dealer networks, and local installer relationships. AI search changes this dynamic fundamentally. When a homeowner or contractor asks an AI platform for the best window replacement brands or queries pricing and cost guidance, the AI does not return every available option. It returns a curated shortlist, typically three to five brands, based on the evidence it can retrieve, compare, and trust.

Being a well-known brand in the physical market is no longer sufficient for buyer discovery. AI systems build recommendations from publicly available sources: review aggregators, comparison articles, official brand content, industry publications, and community discussions. Brands with strong citation architecture across these source layers earn higher recommendation rates and better ranks. Brands that rely on dealer relationships or local presence without corresponding digital evidence are increasingly invisible to AI-driven discovery.

The data makes this gap concrete. Pella appears in 66.6% of all prompts and earns recommendation credit in 50.9% of them. JELD-WEN appears in 31.6% of prompts but earns recommendation credit in only 13.3%. The difference is not about awareness. It is about whether AI systems have sufficient trusted evidence to advance a brand into a ranked shortlist position.

Directional Category Leaders

1. Pella

Pella leads the window replacement category with a monthly AI Authority Value of $2.11M, capturing 11.1% of the total modeled opportunity. Pella achieved a 42.6% top-three recommendation rate and a 48.3% top-ten rate across all platforms. Its strongest performance came in the decision-stage pricing and cost cluster, where it captured $1.07M in AI Authority Value. Pella also maintained a net sentiment score of 0.91, indicating overwhelmingly positive framing across AI responses. On Perplexity specifically, Pella achieved a 51.1% top-three rate and a 58.0% valid recommendation coverage rate, its strongest single-platform result.

The public interpretation: Pella has built the most comprehensive AI evidence architecture in the category, earning consistent top-three placement across all major buying stages and platforms.

2. Andersen

Andersen holds the second position with a monthly AI Authority Value of $1.88M and a 9.9% captured share of opportunity. Its average recommended rank of 1.77 is the lowest in the category, meaning it is frequently placed first or second when recommended. Andersen's strongest cluster was pricing and cost, where it captured $1.12M in AI Authority Value, the highest single-cluster value in the dataset. On Google AI Mode, Andersen led with a 27.9% rank-one rate and a $546.7K AI Authority Value on that platform alone.

The public interpretation: Andersen is the most trusted brand for high-intent pricing and cost queries, often recommended first when buyers are closest to a purchase decision.

3. Marvin

Marvin holds a clear third position with a monthly AI Authority Value of $1.13M and a 5.95% captured share. Marvin achieved a 26.8% top-three rate and a 33.2% top-ten rate, with an average recommended rank of 2.70 that is competitive with the top two brands. Its strongest platform was Gemini, where it achieved a 38.9% top-three rate and a $318.7K AI Authority Value. The brand maintained a net sentiment score of 0.92, the highest in the category alongside Pella.

The public interpretation: Marvin is a consistent third-choice recommendation across platforms, with strong sentiment but a narrower recommendation footprint than the top two brands.

4. Milgard

Milgard holds the fourth position with a monthly AI Authority Value of $538.5K and a 2.84% captured share. Milgard achieved a 6.6% top-three rate and an 18.8% top-ten rate, with an average recommended rank of 3.90 indicating it is typically placed lower in shortlists when it appears. Milgard performed best on Perplexity, capturing $137.5K in AI Authority Value, and on ChatGPT, where it achieved a 26.6% top-ten rate.

The public interpretation: Milgard has moderate recommendation coverage but is rarely placed in the top three, limiting its ability to influence buyer decisions at the shortlist stage.

5. Renewal by Andersen

Renewal by Andersen captured a monthly AI Authority Value of $476.1K with a 2.51% share. Despite a relatively low 14.5% valid recommendation coverage rate, it achieved the second-lowest average recommended rank in the category at 1.53. When the brand is recommended, it is often placed first or second. Its strongest platform was Google AI Overviews, where it captured $153.9K in AI Authority Value with an 8.5% rank-one rate.

The public interpretation: Renewal by Andersen has a narrow but high-quality recommendation profile, appearing less frequently but ranking very well when it does appear.

6. JELD-WEN

JELD-WEN captured a monthly AI Authority Value of $424.8K with a 2.24% share. Despite appearing in 31.6% of all prompts, JELD-WEN earned a valid recommendation coverage rate of only 13.3%. Its net sentiment score of 0.65 was the lowest in the category, driven by a high neutral visibility rate of 10.9%. JELD-WEN performed best on Perplexity, capturing $182.4K in AI Authority Value, but struggled significantly on Gemini, where its valid recommendation rate dropped to 1.9%.

The public interpretation: JELD-WEN has strong brand awareness in AI responses but consistently fails to convert that presence into positive ranked recommendations.

7. ProVia

ProVia captured a monthly AI Authority Value of $394.9K with a 2.08% share. ProVia achieved a 6.6% top-three rate and a 13.4% top-ten rate, with an average recommended rank of 3.52. Its strongest platform was Google AI Overviews, where it captured $166.6K in AI Authority Value with a 14.3% top-three rate. The brand maintained a net sentiment score of 0.91, but its low top-three rate limits shortlist influence.

The public interpretation: ProVia earns positive framing when it appears, but its recommendation footprint is too narrow to materially influence category shortlists.

8. Window World

Window World captured a monthly AI Authority Value of $309.5K with a 1.63% share. Window World achieved a 9.9% top-three rate and a 16.8% top-ten rate, with an average recommended rank of 3.14 placing it in the mid-tier of the field. Its strongest platform was Google AI Overviews, capturing $101.0K in AI Authority Value with a 13.8% top-three rate.

The public interpretation: Window World has consistent but modest recommendation coverage, appearing in shortlists at lower ranks than its broader market presence would suggest.

9. Simonton

Simonton captured a monthly AI Authority Value of $131.4K with a 0.69% share. Simonton achieved a 5.4% top-three rate and a 12.7% top-ten rate, with an average recommended rank of 3.69. Its strongest platform was Gemini, capturing $50.0K in AI Authority Value.

The public interpretation: Simonton has limited recommendation coverage and low top-three rates, making it a marginal presence in AI-driven buyer discovery.

10. Champion Windows

Champion Windows captured a monthly AI Authority Value of $69.8K with a 0.37% share, the smallest in the dataset. Champion Windows achieved a 3.8% top-three rate and a 5.3% top-ten rate, earning only four rank-one recommendations across all platforms. Its strongest platform was Perplexity, capturing $25.1K in AI Authority Value.

The public interpretation: Champion Windows has minimal AI recommendation presence, appearing in fewer than 7% of prompts and rarely earning shortlist positions on any platform.

The Buying Moments That Now Decide the Category

The public dataset covers three high-intent prompt clusters representing the most common ways buyers discover and evaluate window replacement brands through AI.

Best Windows & Doors Brands

This consideration-stage cluster represents buyers beginning their research and generated a total monthly AI opportunity value of $5.84M. Pella led with a 40.96% top-three rate and a $393.5K AI Authority Value. Andersen followed with a 24.58% top-three rate and a $290.8K AI Authority Value. Marvin held third with a 28.25% top-three rate and a $256.4K AI Authority Value. This cluster shows the widest recommendation distribution, with more brands earning mentions, but Pella and Andersen capturing the majority of top-three positions.

Windows & Doors Brand Comparisons

This evaluation-stage cluster represents buyers actively narrowing their options and generated a total monthly AI opportunity value of $6.45M. Pella led with a 41.3% top-three rate and a $645.3K AI Authority Value. Andersen followed with a 27.94% top-three rate and a $471.9K AI Authority Value. Marvin held third with a 25.51% top-three rate and a $317.9K AI Authority Value. This cluster carries a 1.25x buyer stage multiplier, reflecting higher commercial intent as buyers move toward a decision.

Windows & Doors Pricing & Cost

This decision-stage cluster represents buyers closest to purchase and generated the highest total monthly AI opportunity value of the three public clusters at $6.69M. Andersen led with a 32.87% top-three rate and a $1.12M AI Authority Value. Pella followed with a 45.37% top-three rate and a $1.07M AI Authority Value. This cluster carries a 1.5x buyer stage multiplier, reflecting the highest commercial intent in the dataset. Together, Andersen and Pella captured 32.6% of the total opportunity in pricing and cost queries, a concentration that leaves little room for competitors at the decisive moment.

Why Recommendation Power Is Concentrating

The concentration of AI recommendation power around Pella and Andersen reflects a structural advantage in how AI systems build trust in brand recommendations. AI platforms retrieve information from publicly available sources and evaluate that information for relevance, recency, authority, and consistency. Brands that appear across multiple high-quality source types, including official brand content, independent review sites, comparison articles, industry publications, and community discussions, provide more evidence for AI systems to cite and trust.

Pella and Andersen have the strongest citation architecture in the window replacement category. They appear consistently across review aggregators, comparison content, and industry publications. Their official content is well-structured for AI retrieval. They are frequently referenced in third-party articles that compare window replacement options. This breadth of citable, positive evidence is what separates them from brands that are merely mentioned.

Platform-level variation adds another layer of complexity. Pella performs exceptionally well on Perplexity, which favors detailed, citation-rich responses. Andersen performs best on Google AI Mode, which prioritizes structured, authoritative content. Renewal by Andersen performs well on Google AI Overviews, which surfaces brand-specific content in summary formats. Because buyers often use multiple AI platforms during their research process, platform-specific authority gaps can quietly erode a brand's total shortlist presence.

For brands ranked fourth through tenth, the challenge is not building awareness. AI systems already know these brands exist. The challenge is providing enough trusted, positive, consistently structured evidence across source layers that AI systems feel confident advancing them into ranked recommendations.

The Category's Most Visible Warning Sign

The most striking warning sign in the window replacement category is JELD-WEN. The brand appears in 31.6% of all AI prompts, making it one of the most visible brands in the dataset. Yet it earns a valid recommendation in only 13.3% of those appearances, and its net sentiment score of 0.65 is the lowest in the category. A 10.9% neutral visibility rate means AI systems frequently reference JELD-WEN as a factual data point without recommending it.

On Gemini, the problem is acute. JELD-WEN appears in 24.5% of prompts but earns a valid recommendation in only 1.9% of them, with a net sentiment score of 0.20. AI responses on Gemini are overwhelmingly neutral or mixed when discussing the brand, suggesting a specific gap in the trusted sources Gemini draws upon.

This visibility-to-recommendation gap is commercially dangerous in a way that traditional brand metrics do not capture. JELD-WEN is generating impressions in AI responses without generating shortlist positions. Buyers who ask AI platforms for window replacement guidance are unlikely to encounter JELD-WEN in the top three, regardless of how familiar the brand name may be from other channels. Awareness without recommendation credit does not drive consideration in an AI-mediated discovery environment.

What This Means for the Category

The window replacement category is experiencing shortlist compression at a pace that will increasingly shape how buyers choose brands before they ever contact a dealer or installer. Pella and Andersen dominate AI recommendations across all major buying stages. Marvin holds a consistent but distant third position. The remaining seven brands compete for a shrinking share of AI recommendation credit, with most capturing less than 3% of the total modeled opportunity.

This compression has direct commercial consequences. Buyers who use AI for window replacement research are being presented with a narrow set of options. Brands outside that shortlist are effectively invisible during the discovery and evaluation phases of the buyer journey, regardless of their physical market presence or installer network strength.

Competitor displacement is accelerating as AI systems become more sophisticated in distinguishing between brands that are mentioned and brands that are trusted enough to recommend. Brands that invest in structured content, review management, comparison coverage, and citation depth across independent sources will strengthen their recommendation profiles. Brands that rely on traditional channels without corresponding digital evidence will continue to lose ground in AI-mediated discovery.

For underperforming brands, the path forward requires stronger entity architecture, better content structuring for AI retrieval, deeper citation coverage across independent sources, and a systematic approach to building the evidence layers that AI systems use when making ranked recommendations. The window replacement category is not in early transition. The shortlist compression visible in this data suggests it has already arrived.

What This Public Benchmark Does Not Include

- Full cluster dataset covering all 10 prompt clusters

- Prompt-level response tables showing exact AI outputs by platform

- Citation-source failure maps identifying which sources are missing or weak for each brand

- Platform-by-platform recovery priorities for each company

- Entity and schema diagnostics for AI discoverability

- Source-layer gap analysis comparing brand citation profiles across platforms

- Company-specific content recommendations

- Exact competitor threat profiles by prompt cluster

- Full paid opportunity model with platform-level valuation

This page shows the market shape. The paid report shows the repair map.

Methodology and Disclaimers

1. Market studied: Window replacement brands and products in the United States residential construction and home improvement market.

2. Brands and entities included: Andersen, Champion Windows, JELD-WEN, Marvin, Milgard, Pella, ProVia, Renewal by Andersen, Simonton, and Window World. This is not a complete market census.

3. Data collection date and window: June 2026, snapshot-based measurement.

4. AI platforms tested: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity.

5. Observations analyzed: A total of 1,280 observations were analyzed across all platforms and clusters.

6. Prompt categories: Three public high-intent clusters were analyzed: Best Windows & Doors Brands (consideration stage), Windows & Doors Brand Comparisons (evaluation stage), and Windows & Doors Pricing & Cost (decision stage). The full report includes 10 clusters.

7. Definition of a mention: A mention means the company appeared in an AI-generated response, regardless of sentiment or rank.

8. Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality recommendation or ranked recommendation that earns recommendation credit. Visibility is not the same as recommendation credit.

9. Ranking and scoring metrics used: Valid recommendation coverage, top-three rate, rank-one rate, top-ten rate, average recommended rank, net sentiment score, monthly AI Authority Value, monthly AI Recommendation Value, monthly AI Visibility Assist Value, and captured share of AI opportunity.

10. Limitations: This is a point-in-time benchmark. AI outputs can change with model updates, source changes, and content shifts. Modeled values are estimates based on commercial intent proxies and are not revenue. This report is not a full audit or complete market census. The public dataset includes 3 of 10 total clusters.

For a company-specific Authority Index report, the deeper analysis would show which prompts each company wins or loses, which AI platforms are under-recognizing the brand, which source layers are shaping recommendations, and what changes may improve AI shortlist eligibility.