How AI Search Is Recommending Window Replacement

The window replacement category is undergoing a structural shift in how homeowners and contractors discover and evaluate brands. Instead of relying solely on local dealer networks, search engine results, or brand awareness, buyers are increasingly asking AI platforms to recommend the best window brands, compare options, and provide pricing guidance. This changes where buyer shortlists are formed and which brands have the opportunity to be considered at all.

The LLM Authority Index benchmark for June 2026 reveals that AI systems are concentrating buyer shortlists around a small set of brands, with Pella dominating across all major buying stages while several well-known national brands appear frequently in AI responses but rarely earn ranked recommendations. This report interprets those benchmark findings to show which brands are winning AI-driven discovery, where the gaps between visibility and recommendation are widest, and what the category needs to address.

Methodology

1. Market studied: Window replacement brands and products in the United States residential construction and home improvement market.
2. Brands/entities included: Andersen, Champion Windows, JELD-WEN, Marvin, Milgard, Pella, ProVia, Renewal by Andersen, Simonton, and Window World. This universe is not a complete market census.
3. Data collection date/window: June 2026, snapshot-based measurement.
4. AI platforms tested: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity.
5. Number of prompts tested: Prompt count was not provided in the supplied dataset. A total of 1,280 observations were analyzed across all platforms and clusters.
6. Prompt categories: Three high-intent clusters were analyzed for this report: Best Windows and Doors Brands (consideration stage), Windows and Doors Brand Comparisons (evaluation stage), and Windows and Doors Pricing and Cost (decision stage). The full LLM Authority Index report includes 10 clusters. Findings from the remaining seven clusters are not reflected here.
7. Definition of a mention: A mention means the company appeared in an AI-generated response, regardless of sentiment, framing, 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. This distinction drives the analysis.
9. Ranking/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 modeled AI opportunity.
10. Limitations: This is a point-in-time benchmark. AI outputs can change with model updates, source shifts, and content changes. Modeled values are estimates based on commercial intent proxies and are not revenue. This report reflects 3 of 10 total prompt clusters. It is not a full audit or full market census.

Key Findings

Pella Leads the Category with Broad Recommendation Strength

Pella captured 21.1% of the modeled monthly AI opportunity value, equivalent to $2.11M, nearly double the share of its nearest competitor. Pella achieved a 50.9% valid recommendation coverage rate, meaning it received positive, ranked recommendations in more than half of all prompts where it appeared. Its average recommended rank of 2.45 across all platforms places it consistently in the top three positions when recommended. Pella also maintained a net sentiment score of 0.91, indicating strongly positive framing in AI responses.

Andersen Is the Strongest Challenger, Particularly in Decision-Stage Queries

Andersen captured the highest single-cluster AI Authority Value in the pricing and cost cluster at $1.12M, edging out Pella in that specific buying moment. Andersen also achieved the lowest average recommended rank among all brands at 1.77, suggesting that when AI systems recommend Andersen, they tend to place it first or second. On Google AI Mode specifically, Andersen achieved a 27.9% rank-one rate and a $546.7K AI Authority Value, its strongest platform performance across the dataset.

Several Brands Are Frequently Visible but Rarely Recommended

JELD-WEN appears in 31.6% of all prompts but earns a valid recommendation in only 13.3% of those appearances. Its net sentiment score of 0.65 is the lowest in the category, driven by a 10.9% neutral visibility rate, meaning AI systems frequently name JELD-WEN without advancing it to a shortlist. On Gemini, the gap is more severe: JELD-WEN appears in 24.5% of prompts but earns a valid recommendation in only 1.9% of them. Simonton and Champion Windows show similar patterns of presence without shortlist inclusion.

Recommendation Value Is Highly Concentrated

Pella and Andersen together captured 21.0% of the total modeled monthly AI opportunity value of $18.97M. The remaining eight brands in the dataset compete for the rest, and most earn less than 3% of the total opportunity individually. This concentration means buyers using AI for window replacement research are being presented with a narrow set of options, and brands outside the top three are effectively absent during critical discovery and evaluation phases.

Platform-Level Variation Creates Different Competitive Dynamics

Pella performs exceptionally well on Perplexity, with a 51.1% top-three rate and a 58.0% valid recommendation coverage rate on that platform. Andersen performs best on Google AI Mode, with a 27.9% rank-one rate. Renewal by Andersen holds notable strength on Google AI Overviews, achieving an 8.5% rank-one rate. These platform differences matter because buyers often use multiple AI platforms during a single research process, and brands winning on one platform may be largely absent on another.

What Changed in the Market

Window replacement buyers are no longer moving only from Google search results to brand websites. They are also asking AI systems to compare providers, explain reputation, summarize pricing, surface alternatives, and recommend shortlists. This shifts the competitive dynamic in ways that dealer relationships and traditional advertising cannot address directly.

For a category that has historically depended on local installer networks, showroom presence, and brand equity built through decades of advertising, AI-led discovery introduces a new structural requirement: the public evidence layer. AI platforms build their recommendations from publicly available sources, including review aggregators, comparison articles, official brand content, industry publications, and community discussions. Brands that have strong citation architecture across these source layers earn higher recommendation rates and better ranks. Brands that do not have that evidence layer are named but not shortlisted.

The benchmark data illustrates this shift clearly. Pella appears in 66.6% of all prompts but earns recommendation credit in 50.9% of them, a high conversion from visibility to recommendation. JELD-WEN appears in 31.6% of prompts but earns recommendation credit in only 13.3%. The difference is not primarily about brand awareness or advertising reach. It is about whether AI systems have sufficient trusted evidence to advance the brand into a ranked shortlist with positive framing.

Window replacement is a trust-heavy category. Purchases involve significant investment, long-term installation commitments, and warranty dependence. AI systems responding to buyer queries appear to reflect that trust threshold: brands with consistent positive evidence across multiple source types earn recommendation credit, while brands with mixed or sparse evidence layers appear with weaker framing or not at all. Dealer relationships and regional strength do not transfer directly into AI recommendation performance unless they are represented in the public evidence layer.

What the Benchmark Found

Recommendation Leaders

Pella is the clear recommendation leader in the window replacement category. With a monthly AI Authority Value of $2.11M and a 42.6% top-three rate, Pella appears in the top three positions more often than any other brand in the dataset. Its valid recommendation coverage of 50.9% means that when Pella appears in an AI response, it is more likely than not to receive positive shortlist credit. Pella leads in both the consideration-stage Best Windows and Doors Brands cluster and the evaluation-stage Windows and Doors Brand Comparisons cluster.

Andersen is the strongest challenger and the recommendation leader in the decision-stage pricing and cost cluster. Andersen captured $1.12M in that cluster alone, the highest single-cluster value in the dataset. Its average recommended rank of 1.77 is the best in the category, indicating that when Andersen is recommended, it is frequently placed first or second. Andersen achieved a 19.4% rank-one rate overall, the highest rank-one rate in the category.

Marvin holds a clear third position with a monthly AI Authority Value of $1.13M and a 26.8% top-three rate. Marvin achieved a net sentiment score of 0.92, the highest in the category alongside Pella. Its strongest platform was Gemini, where it achieved a 38.9% top-three rate and a $318.7K AI Authority Value.

Visible but Under-Recommended

JELD-WEN is the most significant example of visibility without recommendation strength. 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, and its average recommended rank of 4.01 means it typically appears at the bottom of shortlists when recommended at all. On Gemini, JELD-WEN's net sentiment score dropped to 0.20, indicating predominantly neutral or mixed framing.

Simonton appears in 21.4% of prompts but earns a valid recommendation in only 13.0% of them. Champion Windows appears in only 6.7% of prompts and earns a valid recommendation in 5.5%. Both brands have low top-three rates and average recommended ranks near the bottom of the field, indicating they are present in category conversations but rarely advancing to buyer shortlists.

Value-Weighted Winners

When measured by modeled monthly captured recommendation value, the ranking is: Pella at $2.11M, Andersen at $1.88M, Marvin at $1.13M, Milgard at $538.5K, Renewal by Andersen at $476.1K, and JELD-WEN at $424.8K. The remaining four brands each capture less than $400K in modeled monthly value. This concentration reflects how little of the modeled AI opportunity is being captured by the lower half of the competitive field.

Platform-Specific Patterns

On Perplexity, Pella achieved a 51.1% top-three rate and a 58.0% valid recommendation coverage rate, the strongest platform-specific performance in the dataset. On Google AI Mode, Andersen led with a 27.9% rank-one rate and $546.7K in AI Authority Value. On Google AI Overviews, Pella and Marvin both achieved top-three rates above 36%, while Renewal by Andersen held an 8.5% rank-one rate. On Gemini, Pella and Marvin both achieved top-three rates above 38%. On ChatGPT and Copilot, recommendation patterns were more distributed, with Pella and Andersen leading but with smaller gaps to the rest of the field.

Specialist and Challenger Positions

Renewal by Andersen functions as a specialist option within the dataset, with a distinct brand identity tied to replacement installation rather than new construction. Its platform-specific strength on Google AI Overviews suggests it may be benefiting from source types that platform favors. Milgard holds a mid-tier position with the fourth-highest AI Authority Value but a top-three rate of only 6.6%, indicating that its recommendation presence, while real, does not consistently reach the positions most visible to buyers. ProVia holds a similar pattern, present in shortlists but rarely in top-three positions.

Why Visibility Is Not Enough

A brand can appear in AI answers and still fail to win the buyer shortlist. This is the central distinction the benchmark reveals, and it is the reason raw mention counts are a poor measure of AI discovery performance.

Raw mention presence measures how often a brand is named in AI responses. Valid recommendation coverage measures how often a brand is actually recommended or shortlisted. These are different signals with different commercial implications. JELD-WEN appears in 31.6% of all prompts but earns a valid recommendation in only 13.3% of them. In nearly 60% of its appearances, JELD-WEN is named without being recommended. That appearance without recommendation credit is commercially weak: the buyer sees the name but is not being directed toward it.

Top-three placement matters more than raw mentions because buyers encounter the first few recommendations most directly. Pella achieves a 42.6% top-three rate. JELD-WEN achieves a 4.1% top-three rate. A brand that appears frequently but almost never in the top three positions is unlikely to influence the buyer decision in the way a top-three placement would.

Rank-one placement is the most commercially concentrated position. Andersen achieves a 19.4% rank-one rate. Champion Windows achieves a 0.3% rank-one rate. These brands may appear in the same AI response, but their commercial value from that response is not equivalent.

Net sentiment and framing matter because AI systems do not always mention brands positively. JELD-WEN's 10.9% neutral visibility rate reflects AI responses that name the brand without endorsing it, sometimes in comparison contexts or as a lower-tier option. A neutral mention is not a recommendation. A cautionary mention actively works against buyer conversion. These framing distinctions do not show up in raw visibility counts.

Modeled benchmark value is not revenue. The $18.97M total modeled monthly AI opportunity represents the estimated commercial weight of AI recommendations in this category, calculated from prompt volume, commercial intent, buyer stage, and rank position. It helps compare brands directionally. It should not be read as pipeline, booked demand, or projected sales.

The Citation Layer

AI systems build their recommendations from publicly available sources, and the source patterns in this category appear to favor brands with broad, consistent, and well-structured public evidence layers.

Official brand websites and product pages provide foundational material. Brands with well-organized content covering product lines, specifications, energy performance, warranty terms, and dealer or installer information give AI systems more structured material to retrieve and synthesize. Brands with fragmented or thin owned content are harder for AI systems to represent accurately.

Independent review sites and comparison articles appear to carry significant weight in this category. Pella and Andersen are frequently referenced across review aggregators, home improvement editorial content, and comparison guides, which may help explain their high valid recommendation coverage rates. Brands with sparse third-party review coverage have fewer independent signals for AI systems to draw on.

Industry publications and editorial content covering window replacement trends, energy efficiency, installation quality, and product comparisons also appear to influence AI responses. Brands cited in these sources benefit from third-party authority signals that AI systems can retrieve.

Community discussions, including forum threads and home improvement community content, may play a role particularly in consideration-stage queries where buyers are seeking peer perspectives on brand reliability and installer experience.

The traditional organic search footprint supports the public evidence layer. Brands with strong keyword rankings, healthy backlink profiles, and high-quality referring domains have more retrievable material available across the web. However, search visibility alone does not determine AI recommendation. The quality, consistency, and trustworthiness of the evidence across source types matter more than the volume of indexed pages. A brand with strong domain authority but poor third-party review coverage may still earn weak AI recommendation credit.

It is important to note that citation patterns observed in the benchmark data do not prove direct causal relationships between specific sources and AI outputs. The source layer appears to support AI recommendation outcomes, but the mechanism is one of evidence availability and synthesis, not guaranteed transmission.

What Brands Need to Fix

Weak valid recommendation coverage is the most widespread problem in the category. JELD-WEN, Simonton, and Champion Windows all have valid recommendation coverage rates below 14%. These brands appear in AI responses without being shortlisted, which suggests AI systems lack sufficient trusted evidence to advance them into ranked positions with positive framing.

Low top-three and rank-one presence limits commercial impact even for brands with moderate recommendation coverage. Milgard and ProVia both hold valid recommendation coverage rates near 15% but top-three rates of only 6.6%. Appearing on a shortlist at position five or six is unlikely to carry the same buyer influence as appearing at position one, two, or three.

Neutral or cautionary framing is a distinct problem from low visibility. JELD-WEN's high neutral visibility rate indicates that AI systems are naming the brand without endorsing it. This framing pattern may reflect mixed third-party reviews, inconsistent narrative across sources, or a lack of clearly positive validation signals. Brands with neutral framing need to address the underlying source quality issues, not simply increase content volume.

Thin source footprint affects brands with low recommendation coverage across the board. Building a stronger citation architecture requires investment across official content, independent reviews, comparison coverage, industry publications, and community presence. Each source type contributes to the evidence layer that AI systems retrieve.

Inconsistent entity information across the web can reduce AI recommendation confidence. Brands with fragmented or inconsistent representations of their name, product lines, service areas, and differentiators may be harder for AI systems to characterize accurately and recommend confidently.

Weak third-party validation is a structural gap for several brands in the dataset. Independent reviews, comparison coverage, and industry citations appear to carry weight in AI recommendations that owned content alone cannot replicate. Brands that lack strong third-party validation are dependent on a narrow evidence base that AI systems may not weight as heavily.

Prompt-cluster coverage gaps also exist. The three clusters analyzed here represent consideration, evaluation, and decision stages. Brands that perform well in one cluster but poorly in another miss buyers at different points in the research process. A brand strong in comparison queries but weak in pricing queries may lose buyers at the moment of decision.

How CiteWorks Studio Helps

1. Map AI Recommendation Visibility

Track prompts, platforms, company presence, valid recommendations, top-three and rank-one performance, framing, and citation sources across the window replacement category. Understand where your brand appears in AI responses, where competitors are recommended instead, and which prompt clusters carry the most commercial risk for your specific position.

2. Identify the Sources Shaping AI Answers

Find the editorial, review, forum, directory, owned, search-visible, and backlink-supported sources that influence brand framing in AI responses. Understand which source types are driving recommendations for category leaders and where your brand's evidence layer is thin or inconsistent.

3. Build the Citation Architecture Plan

Strengthen the public evidence layer so AI systems have more accurate, consistent, and persuasive source material to synthesize. Improve owned content structure, expand third-party validation, ensure consistent entity information across the web, and address the specific source gaps that appear to limit recommendation conversion.

Commercial Takeaway

AI-led discovery is changing where buyer shortlists are formed in the window replacement category. Homeowners and contractors who ask AI platforms for brand recommendations are being presented with a narrow set of options determined by AI recommendation logic, not advertising spend or dealer proximity. Brands outside the top three positions are effectively invisible during the moments when buyers are deciding which companies to contact.

Brands can lose recommendation-stage visibility even when they appear frequently in AI answers. JELD-WEN appears in nearly one-third of all prompts but is recommended in only one-eighth of them. Meanwhile, competitors are capturing the value concentrated in high-intent prompt clusters. Andersen captured the highest value in the pricing and cost cluster precisely because decision-stage buyers who are ready to act are being directed toward Andersen at that moment, not JELD-WEN or Simonton.

Traditional search and source visibility still matter because they contribute to the public evidence layer that AI systems retrieve and synthesize. A brand with no organic search footprint has fewer sources available for AI systems to draw on. But search visibility alone does not guarantee AI recommendation. The modeled monthly AI opportunity value of $18.97M is a directional indicator of where AI-driven buyer discovery is concentrating commercial attention in this category. It is modeled benchmark value, not revenue. The practical implication is that improving recommendation-stage visibility, not simply chasing more mentions, is the meaningful competitive objective in this market.

See How AI Is Recommending Your Brand

The benchmark shows the market shape. A company-specific analysis would show which prompts your brand wins or loses, which AI platforms are under-recognizing your brand, which source layers are shaping competitor recommendations, and what changes may improve your AI shortlist eligibility.

CiteWorks Studio can show where your brand appears in AI responses, where competitors are being recommended instead, which prompts carry the most commercial risk for your position, which sources are shaping AI answers in the window replacement category, and what needs to change to improve recommendation-stage visibility.

Request an AI Visibility Audit, an AI Company Discovery Report, or a Citation Architecture Review to understand your specific position in this benchmark category.

Benchmark Source

This analysis is based on the 2026 AI Market Discovery Index for Window Replacement, published by LLM Authority Index. Read the full benchmark report at the LLM Authority Index website.