JELD-WEN AI Visibility Market Strategy Report - Window Replacement

Answer Capsule

JELD-WEN holds 41.96% valid recommendation coverage in the October 2026 Window Replacement benchmark, placing it sixth among ten tracked brands. The company appears in 59.42% of qualified AI responses but converts that presence into a recommendation shortlist only 41.96% of the time, a gap of 17.46 percentage points between raw mention presence and valid recommendation coverage. JELD-WEN's clearest strength is a 5.7-point month-over-month gain from September 2026, one of four significant upward moves in the category. Its clearest weakness is a near-absent rank-one rate of 1.07%, meaning the brand is almost never the first recommendation when buyers ask AI systems for window replacement guidance. The clearest opportunity is converting its substantial presence into top-three placement, where it currently sits at 7.04% against a category leader at 63.86%.

Who This Report Is For

This report is written for JELD-WEN marketing, brand, and category leaders who need to understand how AI systems present and recommend the brand during buyer discovery and evaluation in the window replacement market.

Report Card

Executive Summary

JELD-WEN enters the October 2026 benchmark with 41.96% valid recommendation coverage, ranked sixth in a ten-brand category. The brand appeared in 59.42% of qualified AI responses, but only 41.96% of those responses placed it in a usable recommendation shortlist. That 17.46-point gap between presence and recommendation conversion is the defining characteristic of JELD-WEN's AI visibility profile: the brand is seen, but it is not consistently chosen.

The sentiment picture is positive but not dominant. JELD-WEN recorded 288 positive mentions, 99 neutral mentions, and 1 negative mention across 388 present mentions, producing a net sentiment score of 0.7397. That score is the lowest among the ten tracked brands, trailing ProVia at 0.9344 and Marvin at 0.9038. The high neutral count (99, the largest of any brand in the benchmark) suggests that when JELD-WEN appears in AI responses, it is frequently referenced as context rather than recommended as a solution.

The strongest platform signal for JELD-WEN is Google AI Overviews, where the brand holds 45.5% valid recommendation coverage and leads its own platform-level contribution totals. The weakest platform signal is Gemini, where coverage drops to 27.6% and rank-one rate falls to 1.1%. Copilot shows a similar pattern, with 44.9% coverage but only a 1.3% rank-one rate.

The strongest cluster is Brand Recommendation (C01), the only cluster with sufficient data in the October 2026 benchmark. JELD-WEN holds 41.96% coverage in this cluster, with a 7.04% top-three rate and a 1.07% rank-one rate. The brand's average recommended rank is 4.46, well outside the top-three threshold that typically defines shortlist eligibility.

The clearest gap is rank-one placement. JELD-WEN recorded only 7 rank-one recommendations across 653 observations, compared to Andersen's 216 and Renewal by Andersen's 137. Even brands with lower overall coverage, such as Window World (3.68% rank-one) and ProVia (1.68% rank-one), show more first-position strength relative to their presence. JELD-WEN's near-absence at rank one means the brand is rarely the first name AI systems surface when buyers ask for window replacement recommendations.

The month-over-month picture offers a positive signal. JELD-WEN rose 5.7 points from September 2026 (36.3%) to October 2026 (41.96%), a significant shift beyond normal month-to-month variation. This recovery follows a period of decline earlier in the series, suggesting the brand may be stabilizing its AI recommendation position. Whether that recovery holds and whether it can be converted into top-three placement are the central questions for the next measurement period.

What JELD-WEN Is Winning

Questions This Section Answers

• What month-over-month movement put JELD-WEN among the brands with significant gains?
• How does JELD-WEN's raw mention presence compare with lower-coverage competitors like Milgard and Window World?

JELD-WEN's clearest win in the October 2026 benchmark is its significant month-over-month recovery. The brand rose 5.7 percentage points from 36.3% coverage in September 2026 to 41.96% in October 2026, a move beyond normal month-to-month variation. This places JELD-WEN among four brands (alongside Andersen, Marvin, and Pella) that recorded significant upward shifts from September to October.

The brand also holds a meaningful presence advantage over several competitors. At 59.42% raw mention presence, JELD-WEN appears in AI responses more frequently than Milgard (52.7%), Simonton (52.2%), Renewal by Andersen (49.0%), Window World (35.4%), ProVia (28.0%), and Champion Windows (15.2%). This presence base provides a foundation for recommendation conversion if the brand can improve its shortlist placement.

JELD-WEN's rank-one rate, while low at 1.07%, represents a directional shift from July 2026, when the brand held a near-zero rank-one rate. The gain is small in absolute terms but marks an improvement off a minimal base.

The brand's sentiment profile is positive, with 288 positive mentions against only 1 negative mention. While the net sentiment score of 0.7397 is the lowest in the benchmark, the near-absence of negative framing means JELD-WEN is not fighting active reputational headwinds in AI responses.

Where JELD-WEN Has the Clearest AI Visibility Gaps

Questions This Section Answers

• Why is JELD-WEN almost never the first recommendation when buyers ask for a single best window replacement company?
• How does JELD-WEN's top-three placement compare with Pella, Andersen, and Renewal by Andersen?
• Where does JELD-WEN's presence fail to convert into a recommendation across platforms like Perplexity and Copilot?

The most significant gap is rank-one placement. JELD-WEN recorded 7 rank-one recommendations across 653 qualified observations, a 1.07% rate. By comparison, Andersen holds a 33.08% rank-one rate, Renewal by Andersen holds 20.98%, and even Marvin, which trails JELD-WEN in some presence metrics, holds 10.41%. This means that when buyers ask AI systems for a single best window replacement recommendation, JELD-WEN is almost never the answer.

The second gap is top-three placement. JELD-WEN's 7.04% top-three rate places it well behind the category leaders. Pella holds 63.86%, Andersen holds 53.91%, and Marvin holds 50.84%. Even Milgard and Simonton, which have similar overall coverage to JELD-WEN, hold top-three rates of 5.36% and 4.90% respectively. JELD-WEN's 7.04% is marginally better than those two brands but still far from shortlist territory.

The third gap is recommendation conversion efficiency. JELD-WEN appears in 59.42% of qualified responses but converts to a valid recommendation in only 41.96%. The brand is present in AI responses but frequently appears as context, comparison, or neutral reference rather than as a recommended option.

The fourth gap is platform inconsistency. JELD-WEN's coverage ranges from 27.6% on Gemini to 45.5% on Google AI Overviews. On Copilot, the brand holds 44.9% coverage but only a 1.3% rank-one rate. On Perplexity, coverage is 60.2% but rank-one is 0.0%. This platform-level variance suggests that JELD-WEN's recommendation position is not stable across the AI surfaces buyers use.

The fifth gap is the absence of pricing and comparison cluster data. The October 2026 benchmark contains no qualified observations in the Pricing & Value or Multi-Brand Comparison clusters. This means JELD-WEN's position in price-related or head-to-head comparison prompts is not measured in the public benchmark. Pricing questions are being asked, and platforms answer them inconsistently, but the public coverage metrics do not yet score brands on those answers.

Biggest Opportunity

Questions This Section Answers

• What would it take for JELD-WEN to move from 7.04% top-three placement toward the second tier of window replacement brands?
• Which prompt cluster offers the clearest path to converting JELD-WEN's presence into shortlist placement?

JELD-WEN's biggest opportunity is converting its existing presence into top-three recommendation placement. The brand already appears in 59.42% of qualified AI responses, which means it has a substantial presence base to work from. The gap is not visibility; it is recommendation conversion. Moving from a 7.04% top-three rate to even a 20% top-three rate would place JELD-WEN alongside Renewal by Andersen (25.88%) and within striking distance of the category's second tier.

This opportunity is tied directly to the Brand Recommendation cluster, which captures discovery and consideration queries such as "What company is best for windows?" and "best window replacement company." These are the prompts where buyers are actively seeking recommendations, and they are the prompts where JELD-WEN's conversion gap is most costly. Improving top-three placement in this cluster would require the brand to strengthen the signals AI systems use to determine which brands belong in a shortlist: citation presence, source authority, and the framing of JELD-WEN in third-party content.

The month-over-month recovery from September to October suggests that some of these signals may already be improving. The question is whether that recovery can be sustained and whether it can be converted into higher placement, not just higher presence.

Competitive Landscape

Questions This Section Answers

• How does JELD-WEN's recommendation profile compare with Pella, Andersen, and Marvin in the October 2026 benchmark?
• Where does JELD-WEN rank on top-three rate, rank-one rate, and average recommended rank across the ten tracked window replacement brands?

Pella and Andersen hold the strongest recommendation-stage positions in the October 2026 Window Replacement benchmark, with Pella leading on coverage and top-three rate and Andersen leading on rank-one rate. Marvin holds a strong second-place position on coverage. JELD-WEN sits in the middle of the pack, ranked sixth on coverage and well behind the leaders on top-three and rank-one placement.

Average recommended rank covers rank-eligible recommendations only.

JELD-WEN's position in the table reflects a brand with moderate presence but weak recommendation conversion. The 7.04% top-three rate places it sixth among ten brands, ahead of Milgard, ProVia, Simonton, and Champion Windows but well behind the top five. The 1.07% rank-one rate is seventh, ahead of only Milgard and Champion Windows. The average recommended rank of 4.46 is the second-lowest in the benchmark, ahead of only Simonton at 4.52. The sentiment score of 0.7397 is the lowest of any tracked brand.

Prompt Evidence

Google AI Overviews / Brand Recommendation Prompt: "What company is best for windows?" Result: JELD-WEN appeared in the response but was placed outside the top three, contributing to its 45.5% coverage on this platform without a corresponding top-three placement.

Perplexity / Brand Recommendation Prompt: "best window replacement company" Result: JELD-WEN appeared in 60.2% of Perplexity responses but recorded a 0.0% rank-one rate on this platform, meaning the brand was never the first recommendation despite frequent presence.

ChatGPT / Brand Recommendation Prompt: "What is the best window company?" Result: JELD-WEN held 50.0% valid recommendation coverage on ChatGPT but recorded a 0.0% rank-one rate, indicating the brand was shortlisted but never placed first.

Copilot / Brand Recommendation Prompt: "best windows" Result: JELD-WEN held 44.9% coverage on Copilot with a 1.3% rank-one rate, reflecting the same pattern of presence without first-position strength.

What CiteWorks Studio Would Do Next

Phase 1: AI Visibility Market Discovery Audit Map the specific prompts where JELD-WEN appears but is not recommended, and identify which competitors are absorbing the top-three and rank-one slots in those responses.

Phase 2: Recommendation Readiness Plan Prioritize the prompt clusters and platforms where JELD-WEN's presence-to-recommendation gap is widest, starting with Perplexity and Copilot where rank-one rates are near zero.

Phase 3: Owned Answer Layer Buildout Strengthen JELD-WEN's owned content to provide clearer, more extractable recommendation signals for AI systems, particularly for the "best window company" and "best window replacement company" prompt patterns.

Phase 4: Citation / Authority Layer Development Improve JELD-WEN's presence in the third-party sources AI systems cite most frequently, including home-improvement guides, consumer review sites, and retailer content where the brand currently has limited representation.

Phase 5: Monthly AI Visibility and Recommendation Tracking Track JELD-WEN's coverage, top-three rate, and rank-one rate month over month to determine whether the September-to-October recovery holds and whether recommendation conversion improves.

Why This Matters

AI presence alone is not enough. JELD-WEN appears in 59.42% of qualified AI responses, but it converts that presence into a recommendation shortlist only 41.96% of the time and into a top-three placement only 7.04% of the time. For buyers who ask AI systems for a window replacement recommendation, JELD-WEN is frequently mentioned but rarely chosen. That gap between presence and recommendation is where buyer decisions are lost.

The next move is targeted correction of the prompt, page, and citation layers. The prompts where JELD-WEN appears but is not recommended need to be identified and addressed. The pages that AI systems retrieve when answering those prompts need to be strengthened or supplemented. The citation sources that AI systems rely on need to include JELD-WEN in a recommendation context, not just a reference context. The September-to-October recovery shows that movement is possible. The question is whether that movement can be directed toward recommendation placement rather than just presence.

Core Metrics

Sentiment Score

Questions This Section Answers

• What does JELD-WEN's high neutral mention count say about how AI systems use the brand in responses?
• Why is JELD-WEN's sentiment score the lowest in the benchmark despite almost no negative mentions?

Sentiment Score = (positive mentions × 1 + neutral mentions × 0 + negative mentions × -1) / total mentions

For JELD-WEN: (288 × 1 + 99 × 0 + 1 × -1) / 388 = 287 / 388 = 0.7397

This score matters because unclassified mention counts are misleading. A brand that appears in 388 AI responses but is recommended in only 274 of them is not equally visible and recommended. The 99 neutral mentions represent responses where JELD-WEN was referenced as context, comparison, or background rather than as a recommended option. Those mentions count toward presence but not toward recommendation.

Share of voice is a diagnostic metric, not a business KPI. A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention are not equal. Counting all mentions as wins is bad measurement. Classified sentiment is required before interpreting AI visibility, because the difference between being mentioned and being recommended is the difference between being considered and being chosen.

JELD-WEN's sentiment score of 0.7397 is the lowest among the ten tracked brands. This does not mean the brand is viewed negatively; the near-absence of negative mentions (only 1) means the brand is not facing active reputational headwinds. But the high neutral count (99, the largest in the benchmark) means JELD-WEN frequently appears in AI responses without a clear positive recommendation framing. Improving the sentiment score requires converting neutral references into positive recommendations, not just reducing negative mentions.

Sentiment by Platform

Questions This Section Answers

• Which platforms show the widest gap between JELD-WEN's mention presence and its recommendation strength?
• Where does JELD-WEN's sentiment score fail to translate into rank-one placement?

Methodology

1. This report is a benchmark-based analysis of JELD-WEN's AI visibility and recommendation position in the Window Replacement category, using data from the LLM Authority Index October 2026 measurement period.
2. The reporting month is October 2026, with comparisons to July 2026 (baseline), August 2026, and September 2026 where available.
3. Six AI platforms were tracked: ChatGPT, Copilot, Gemini, Perplexity, Google AI Overviews, and Google AI Mode. All six platforms qualified with at least one observation in the October 2026 measurement.
4. The October 2026 benchmark analyzed 653 qualified observations from 800 prompt-surface observations. The qualification process removed 33 irrelevant observations and retained 767 relevant observations, of which 653 met the final qualification criteria.
5. The competitor universe includes ten tracked brands: Andersen, Champion Windows, JELD-WEN, Marvin, Milgard, Pella, ProVia, Renewal by Andersen, Simonton, and Window World.
6. The public benchmark includes one cluster with sufficient data: Brand Recommendation (C01), which captures discovery and consideration queries. Two clusters (Pricing & Value and Multi-Brand Comparison) have no qualified observations in the October 2026 measurement.
7. Stage 0 extraction retained the query, AI/search surface, answer, brand outcome, recommendation placement, sentiment, and, where exposed, citations or attributable evidence sources.
8. A mention is defined as any appearance of JELD-WEN in a qualified AI response, regardless of whether the brand was recommended. Raw mention presence rate measures the share of qualified observations where the brand appeared.
9. A valid recommendation is defined as an appearance in a usable recommendation shortlist, as marked by the dataset. Valid recommendation coverage measures the share of qualified observations where the brand appeared in a recommendation shortlist.
10. Top-three rate measures the share of qualified observations where the brand appeared in the top three recommended positions. Rank-one rate measures the share of qualified observations where the brand was the first recommendation.
11. Average recommended rank covers rank-eligible recommendations only. JELD-WEN's average recommended rank of 4.46 is based on 241 rank-eligible recommendations.
12. Limitations: The October 2026 benchmark is a modest-sized sample of 653 qualified observations. Brand-level percentages in a category of this size can show more month-to-month variation than larger benchmark categories. Movement identification is not causation. Significant declines and rises mark where to investigate, not why the change happened. The public benchmark does not measure market share, attributable sales, every possible AI response, organic-search ranking, social mention volume, or private or sponsored channels. Source presence is evidence about the information environment and is not automatically proof that the source caused the recommendation.

See How AI Is Recommending Your Brand

The public benchmark shows where JELD-WEN stands in AI recommendations. A company-level AI visibility audit maps the specific prompts, platforms, and competitor displacement patterns that explain why the brand is present but not consistently recommended. It answers the why behind the movement and identifies the highest-priority actions for improving recommendation placement in the prompts that matter most to window replacement buyers.