IKO AI Market Strategy Report - Roofing Companies

Answer Capsule

IKO appears in 42% of AI responses across the roofing category but converts only 7.9% of those appearances into valid recommendations. The benchmark places IKO fifth among ten tracked companies, with a modeled monthly AI Authority Value of $373,799 against a total category opportunity of $18.2 million. IKO's clearest weakness is a 3.9% top-three rate and an average recommended rank of 2.68, the weakest figures among the top five brands. The clearest opportunity is the decision-stage pricing cluster, where IKO achieves its strongest recommendation performance with a 7.5% top-three rate and a 0.42 net sentiment score.

Who This Report Is For

This report is for IKO's marketing, digital strategy, and brand leadership teams evaluating how AI-generated recommendations are shaping buyer consideration in the roofing shingles category.

Report Card

• Report type: AI Company Market Strategy Report
• Target company: IKO
• Category / market studied: Roofing Companies
• Reporting month: June 2026
• AI platforms tracked: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity
• Public high-intent clusters: 3 (Best Roofing Shingles & Top Roofing Materials, Roofing Shingles Brand & Product Comparisons, Roofing Shingles Pricing & Cost)
• AI observations analyzed: 1,131
• Competitors tracked: 10 (GAF, Owens Corning, CertainTeed, Malarkey, IKO, TAMKO, Atlas Roofing, Power Home Remodeling, Erie Home, DECRA)

Executive Summary

IKO holds moderate visibility in the roofing category but faces a structural recommendation deficit. The company appears in 42% of all AI responses across 1,131 observations, yet only 7.9% of those appearances convert into valid recommendations. This gap between mention presence and recommendation credit is the central commercial risk for IKO in AI-led discovery.

The benchmark places IKO fifth among ten tracked companies, with a modeled monthly AI Authority Value of $373,799, representing 2.1% of the total $18.2 million monthly AI opportunity. GAF leads the category at $2.71 million, followed by Owens Corning at $2.01 million, CertainTeed at $1.97 million, and Malarkey at $432,598. IKO's monthly lost AI opportunity value is $17.8 million, the difference between the total category opportunity and IKO's captured share.

IKO's strongest cluster by recommendation behavior is Roofing Shingles Pricing & Cost, where it achieves a 7.5% top-three rate and a 0.42 net sentiment score. Its weakest cluster is Roofing Shingles Brand & Product Comparisons, where the top-three rate drops to 0.7% and the rank-one rate is 0%. The strongest platform signal is Google AI Mode, where IKO achieves a 7.1% rank-one rate and its highest platform-level AI Authority Value at $182,796. The clearest platform gap is Perplexity, where IKO's rank-one rate is only 0.7%.

IKO's overall net sentiment score of 0.33 is the lowest among the top five brands. When IKO is mentioned, it is more likely to appear in neutral or comparison-anchor contexts than in positive recommendation positions. Neutral mentions account for 66.3% of all IKO appearances in the dataset, indicating that AI systems are referencing IKO as a known brand rather than actively selecting it as a preferred choice.

What IKO Is Winning

IKO's strongest performance is in the decision-stage pricing cluster. In Roofing Shingles Pricing & Cost, IKO achieves a 7.5% top-three rate, a 6.6% rank-one rate, and an average recommended rank of 2.0. These are IKO's best figures by every recommendation metric across the three public clusters. The analysis suggests that when AI systems surface pricing information, IKO has more structured data available for retrieval than in other cluster types.

Google AI Mode is IKO's strongest platform. The benchmark shows a 7.1% rank-one rate, an 8.1% top-three rate, and an AI Authority Value of $182,796, nearly half of IKO's total captured value across all six platforms. Google AI Mode surfaces IKO in recommendation positions more consistently than any other tracked platform in this dataset.

IKO's net sentiment score of 0.42 in the pricing cluster is its strongest sentiment performance across all segments. When IKO appears in pricing contexts, the framing is more positive than in consideration or evaluation clusters, suggesting that pricing-related content is IKO's most effective point of entry into AI-generated shortlists.

Where IKO Has the Clearest AI Visibility Gaps

IKO's most significant gap is in the evaluation-stage comparison cluster. In Roofing Shingles Brand & Product Comparisons, IKO appears in 52% of responses but achieves only a 0.7% top-three rate and a 0% rank-one rate. The company is being named as a brand option in more than half of comparison responses but is almost never recommended as a top choice when AI systems rank manufacturers side by side. This cluster represents the most commercially decisive moment in the buyer journey, and IKO is effectively absent from recommendation positions within it.

The competitor displacement at this stage is sharp. GAF achieves a 38.3% top-three rate and a 27.3% rank-one rate in the same cluster. Owens Corning achieves a 41.2% top-three rate. IKO's 0.7% top-three rate means it is being displaced by GAF, Owens Corning, and CertainTeed in nearly every comparison prompt where it is present.

Perplexity represents IKO's weakest platform performance. The rank-one rate on Perplexity is 0.7% and the top-three rate is 1.3%, making it the least productive platform for recommendation-stage visibility. A buyer encountering IKO's category on Perplexity is very unlikely to see it as a recommended option.

IKO's average recommended rank of 2.68 is the weakest among the top five brands. When IKO earns a recommendation, it tends to appear in the third position or lower, which reduces the commercial impact of those appearances relative to brands that more consistently hold the first or second recommendation position.

Biggest Opportunity

IKO's clearest path to improvement is expanding recommendation coverage in the pricing cluster. This is already IKO's strongest cluster, with a 7.5% top-three rate and a 0.42 net sentiment score, but the gap between current and potential performance is large. The pricing cluster represents a $6.14 million monthly opportunity in the benchmark, and IKO currently captures only $155,921 of that value. Doubling the top-three rate in this cluster to approximately 15% would nearly double IKO's captured value in its best-performing segment without requiring improvements in the harder-to-crack comparison cluster.

The pricing cluster is also where IKO's sentiment framing is most favorable. Strengthening pricing-related content, warranty documentation, cost-per-square data, and product specification pages could deepen retrievability in this cluster and create a foundation for eventually building recommendation presence in the evaluation-stage comparison cluster, where IKO currently holds almost no recommendation credit.

Prompt Evidence

Google AI Mode / Roofing Shingles Pricing & Cost Prompt: "What are the best roofing shingles for the money?" Result: IKO appeared in a recommendation position on Google AI Mode with a 7.1% rank-one rate in this cluster, its strongest platform performance across the dataset.

Gemini / Roofing Shingles Brand & Product Comparisons Prompt: "Compare Owens Corning vs CertainTeed vs IKO roofing shingles" Result: IKO was present in 52% of responses in this cluster but achieved only a 0.7% top-three rate, indicating it was listed as a known brand but rarely selected as a top recommendation.

Perplexity / Best Roofing Shingles & Top Roofing Materials Prompt: "What are the top roofing shingle brands?" Result: IKO appeared in 22.4% of responses but achieved only a 1.3% top-three rate and a 0.7% rank-one rate, showing the weakest recommendation conversion of any platform in the dataset.

ChatGPT / Roofing Shingles Pricing & Cost Prompt: "How much do IKO roofing shingles cost?" Result: IKO achieved a 3.2% rank-one rate and a 3.2% top-three rate with an average recommended rank of 1.88, its second-strongest platform performance and consistent with stronger retrieval of pricing-related content.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map IKO's full recommendation footprint across all 10 buyer intent clusters and identify which prompts and platforms are producing the largest gap between mention presence and recommendation credit.

Phase 2: Recommendation Readiness Plan Diagnose the structural causes of IKO's 42% mention presence against 7.9% valid recommendation coverage, with priority focus on the comparison cluster where the displacement is most severe.

Phase 3: Owned Answer Layer Buildout Develop structured pricing content, warranty comparison pages, cost-per-square documentation, and product specification sheets that AI systems can reliably retrieve and synthesize when building shortlists.

Phase 4: Citation / Authority Layer Development Strengthen the public evidence layer through independent review signals, installer directory presence, and third-party comparison content that AI systems use to assign recommendation credit.

Phase 5: Monthly AI Visibility and Recommendation Tracking Monitor IKO's recommendation coverage, rank-one rate, and sentiment score across all platforms and clusters monthly to measure progress and identify emerging competitor displacement.

Why This Matters

IKO is visible in AI responses but is not being chosen. The benchmark shows that 42% mention presence translates into only 7.9% recommendation coverage, meaning the large majority of IKO's AI appearances carry no direct commercial weight. In a category where GAF, Owens Corning, and CertainTeed collectively hold dominant recommendation positions, IKO's current posture leaves it exposed to further displacement as AI-led discovery becomes a primary channel for buyer shortlisting.

The pricing cluster offers a defensible starting point, but the comparison cluster represents the real commercial risk. Buyers actively comparing manufacturers are not seeing IKO as a top recommendation, and the 0% rank-one rate in that cluster means the problem is not marginal. Until IKO builds the structured content and citation signals that produce recommendation credit in evaluation-stage prompts, its AI mention presence will continue to outpace its AI recommendation performance by a wide margin.

Core Metrics

• Mentions: 475
• Valid recommendations: 89
• Top 3 recommendation count: 44
• Rank #1 recommendation count: 36
• Average recommended rank: 2.68
• Positive mentions: 158
• Neutral mentions: 315
• Negative mentions: 2
• Raw mention presence rate: 42.0%
• Valid recommendation coverage: 7.9%
• Top 3 recommendation rate: 3.9%
• Rank #1 recommendation rate: 3.2%
• Strongest cluster by recommendation behavior: Roofing Shingles Pricing & Cost
• Strongest platform by recommendation behavior: Google AI Mode

Sentiment Score

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

IKO Sentiment Score = (158 x 1 + 315 x 0 + 2 x -1) / 475 = 156 / 475 = 0.33

This score indicates that IKO's AI mentions are moderately positive on balance, but the high proportion of neutral mentions (66.3% of all mentions) shows that IKO is most frequently referenced as a known brand name rather than actively recommended. That distinction matters commercially.

Unclassified mention counts are misleading because they treat a neutral listing and a positive shortlist recommendation as equivalent outcomes. 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 signals. Counting all appearances as wins produces a distorted picture of AI recommendation performance. Classified sentiment, separated into positive, neutral, and negative framing, is required before any meaningful interpretation of AI visibility data.

Sentiment by Platform

Methodology

1. Report orientation: This is a benchmark-based AI Company Market Strategy Report analyzing IKO's visibility and recommendation performance in the roofing category. It is not a client implementation case study and does not imply that CiteWorks Studio produced the benchmark outcomes.
2. Reporting window: June 2026, snapshot-based measurement reflecting AI system behavior at a single point in time.
3. Platforms tracked: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity.
4. Observation count: 1,131 total observations across the category; 475 observations involve IKO.
5. Competitor universe: GAF, Owens Corning, CertainTeed, Malarkey, IKO, TAMKO, Atlas Roofing, Power Home Remodeling, Erie Home, DECRA. This is not a full market census and additional brands may be active in the category.
6. Public clusters used: Best Roofing Shingles & Top Roofing Materials (consideration stage), Roofing Shingles Brand & Product Comparisons (evaluation stage), Roofing Shingles Pricing & Cost (decision stage). The full LLM Authority Index report covers 10 buyer intent clusters; this public analysis covers 3.
7. Stage 0 role: Raw AI observations were collected and classified before metric aggregation. Structured metrics in the aggregation file are the primary data source for this report.
8. Definition of a mention: A mention is recorded when the company appears in an AI-generated response in any context, regardless of sentiment, ranking, or recommendation quality.
9. Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality appearance that earns recommendation credit. Neutral references, cautionary mentions, and comparison-anchor appearances are not counted as valid recommendations.
10. Modeled value: AI Authority Value figures are modeled benchmark estimates based on commercial intent signals. They are not revenue, pipeline, or booked demand.
11. Limitations: AI outputs change with model updates and source layer changes. This report reflects a single reporting month and should not be treated as a stable or permanent measurement. The public version of this report covers 3 of 10 clusters available in the full benchmark. Unique prompt counts are not available in the public dataset version.

See How AI Is Recommending Your Brand

The benchmark shows where IKO appears in AI responses, which competitors are being recommended instead, which prompt types carry the most commercial risk, and which sources are shaping AI answers in the roofing category. CiteWorks Studio maps brand recommendation footprints across the platforms and clusters where buyer decisions are forming, and identifies the content, citation, and authority gaps that determine whether a brand is listed or chosen.