Vasque AI Market Strategy Report - Hiking Boots, Trail Shoes, and Outdoor Footwear

Answer Capsule

Vasque is losing ground in AI-generated recommendations for hiking boots, trail shoes, and outdoor footwear, with valid recommendation coverage falling to 2.02% in September 2026, down from 4.4% in July. The brand's raw mention presence has contracted to 4.31%, meaning AI systems are not surfacing Vasque at sufficient scale to be considered for recommendation. Vasque recorded zero rank-one placements in September, and its strongest platform signal comes from Perplexity, where it appears in 14.43% of observations but still converts weakly into recommendations. The clearest opportunity lies in rebuilding presence at the mention stage across ChatGPT, Copilot, Gemini, and Google AI Mode, where the brand is nearly absent.

Who This Report Is For

This report is for brand, marketing, and ecommerce leaders at Vasque responsible for understanding how AI search systems discover and recommend the brand in the hiking footwear category.

Report Card

Executive Summary

Vasque holds the weakest position in the tracked competitive set for AI-driven hiking footwear discovery. The benchmark shows valid recommendation coverage of 2.02% in September 2026, down from 4.4% in July, a decline the LLM Authority Index classifies as beyond normal variation. Raw mention presence fell to 4.31%, meaning Vasque appeared in only 32 of 742 qualified observations, and produced just 15 valid recommendations.

The brand's strongest cluster is the only one measured in this public series: Best Hiking Boots and Trail Shoes, which captures direct requests for brand recommendations. Within that cluster, Vasque's weakness is concentrated at the presence stage rather than the placement stage. When Vasque is mentioned at all, it is often framed positively, with 18 positive mentions and no negative mentions, but the volume is too small to generate meaningful recommendation coverage.

Across platforms, Perplexity is the only surface where Vasque shows any meaningful footprint, appearing in 14.43% of observations. ChatGPT, Copilot, Gemini, and Google AI Mode show near-total absence, with presence rates below 6.5%. Google AI Overviews surfaces Vasque in only 2.11% of observations. The clearest platform gap is the combination of ChatGPT and Copilot, where Vasque appears in just three observations each, effectively removing the brand from two major AI discovery surfaces.

What Vasque Is Winning

Vasque has no negative sentiment in the September dataset. Across 32 mentions, the brand recorded 18 positive and 14 neutral mentions, with zero negative framing. This suggests that when AI systems do reference Vasque, the brand is not being characterized negatively.

Perplexity represents the only meaningful pocket of presence. Vasque appeared in 14 of 97 Perplexity observations, a 14.43% presence rate, with 13 positive mentions. This is the single platform where the brand retains any visible footprint, though it converts to only 13 valid recommendations and no rank-one placements.

These are narrow wins. The evidence does not support claiming broader strength in the category.

Where Vasque Has the Clearest AI Visibility Gaps

Questions This Section Answers

• In which AI platforms is Vasque most severely under-represented?
• How does Vasque's mention-stage presence compare with the category leaders?
• What do Vasque's zero rank-one placements mean for its competitive position?

Vasque's core problem is absence, not weak placement. The brand is being displaced at the mention stage across most AI surfaces, meaning AI systems are not naming Vasque at sufficient scale to be considered for recommendation.

The most severe gaps are in ChatGPT and Copilot. Vasque appeared in only 3 of 81 ChatGPT observations and 3 of 92 Copilot observations, with zero valid recommendations on either platform. Google AI Mode surfaced Vasque in just 2 of 189 observations, and Google AI Overviews in 4 of 190. Gemini showed 6 mentions but zero valid recommendations.

The contrast with category leaders is stark. Merrell appeared in 726 of 742 observations with 82.35% valid recommendation coverage, and Salomon appeared in 705 observations with 81.13% coverage. Even mid-tier brands like Danner, at 28.44% coverage, and Oboz, at 30.59%, maintain presence rates above 40%. Vasque's 4.31% presence rate places it far below every tracked competitor.

Vasque also recorded zero rank-one placements in September, down from 0.3% in July. Its two top-three placements represent 0.27% of observations, the lowest top-three rate in the category.

Biggest Opportunity

The clearest opportunity for Vasque is rebuilding presence at the mention stage, particularly in ChatGPT and Copilot, where the brand is effectively invisible. Vasque's problem is not that it is mentioned and passed over; it is that AI systems are not retrieving the brand at all.

The path forward requires strengthening the public evidence layer that AI systems draw on when forming recommendations. This means ensuring that product information, reviews, comparisons, and category references are present in sources that ChatGPT, Copilot, Gemini, and Google AI Mode can retrieve and synthesize. Perplexity's relative willingness to surface Vasque suggests the brand has some source footprint, but it is not reaching the platforms where most AI-driven discovery occurs.

Competitive Landscape

Questions This Section Answers

• Which competitors hold the strongest recommendation-stage positions in this category?
• Where does Vasque rank against the tracked competitor set on placement metrics?

Salomon and Merrell hold dominant recommendation-stage strength in this category, with Salomon leading on rank-one placement at 41.11% and Merrell leading on overall coverage at 82.35%. Vasque sits at the bottom of the tracked set, with coverage below 3% and no rank-one placements.

Average recommended rank covers rank-eligible recommendations only.

The table shows Vasque trailing every tracked competitor on top-three rate and rank-one rate. Its average recommended rank of 5.50 is based on only two rank-eligible placements, and its sentiment score of 0.5625 is the lowest in the category, reflecting the small sample of mentions rather than negative framing.

Prompt Evidence

Perplexity / Best Hiking Boots and Trail Shoes Prompt: "What are the top 10 hiking shoes?" Result: Vasque appeared in 14.43% of Perplexity observations, its strongest platform showing, but converted to no rank-one placements.

ChatGPT / Best Hiking Boots and Trail Shoes Prompt: "What are the best hiking boots brands?" Result: Vasque appeared in only 3 of 81 observations with zero valid recommendations, indicating near-total absence from this surface.

Google AI Mode / Best Hiking Boots and Trail Shoes Prompt: "What is the best brand of hiking boots?" Result: Vasque was surfaced in just 2 of 189 observations, effectively removing the brand from consideration on this high-volume surface.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map which high-intent prompts and surfaces are failing to surface Vasque, and identify where competitors are being recommended instead.

Phase 2: Recommendation Readiness Plan Prioritize the platforms and prompt families where presence gains would have the largest impact, starting with ChatGPT and Copilot.

Phase 3: Owned Answer Layer Buildout Develop product, category, and comparison content that gives AI systems clear, retrievable information about Vasque's positioning.

Phase 4: Citation / Authority Layer Development Strengthen the external source footprint that AI systems draw on, focusing on the review, retail, and editorial sources that drive category recommendations.

Phase 5: Monthly AI Visibility and Recommendation Tracking Monitor presence rate, valid recommendation coverage, and placement trends monthly to measure whether the brand is moving from mention to recommendation.

Why This Matters

AI-generated recommendations are becoming the first filter for hiking footwear buyers. When a shopper asks an AI assistant for the best hiking boots, the brands named in that response form the consideration set, and brands that are absent are never evaluated.

Vasque's challenge is not that it is being recommended poorly. It is that the brand is not being mentioned at scale, and AI systems cannot recommend a brand they do not surface. The next move is targeted correction of the presence layer: building the owned content and external citation sources that give AI systems a reason to include Vasque in category answers.

Core Metrics

Sentiment Score

Questions This Section Answers

• How is the sentiment score calculated for Vasque?
• Why is classified sentiment required before interpreting AI visibility?

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

For Vasque, the calculation is (18 × 1 + 14 × 0 + 0 × -1) / 32, producing a score of 0.5625.

This score matters because unclassified mention counts are misleading. Vasque's 32 mentions look neutral on the surface, but the sentiment score reveals that the brand's mentions are predominantly positive when they occur. Share of voice is a diagnostic metric, not a business KPI. A positive recommendation, neutral reference, cautionary mention, and competitor-displaced mention are not equal, and counting all mentions as wins is bad measurement. Classified sentiment is required before interpreting AI visibility, because it distinguishes between a brand that is mentioned favorably and one that is merely present.

Sentiment by Platform

Methodology

1. This report is a company-level AI market strategy readout based on the LLM Authority Index AI Market Discovery Index public benchmark for Hiking Boots, Trail Shoes, and Outdoor Footwear, not a client implementation case study.
2. The reporting window is September 2026, with July 2026 as the baseline comparison month.
3. Six AI/search surface families were tracked: ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode.
4. The September run began with 800 prompt-surface observations (529 unique questions). Of those, 800 mentioned a tracked brand or competitor, 782 were relevant, and 742 qualified for the benchmark denominator.
5. The competitor universe includes 10 tracked brands: Altra, Columbia Sportswear, Danner, HOKA, KEEN, La Sportiva, Merrell, Oboz, Salomon, and Vasque.
6. All qualified observations in September fell into the Brand Recommendation buyer-intent class. No qualified observations were recorded for Pricing and Value or Multi-Brand Comparison clusters.
7. Stage 0 extraction captured prompt-level observations including the query, AI surface, answer, brand outcome, recommendation placement, sentiment, and citations where exposed.
8. A mention is defined as any appearance of a tracked brand in an AI response, regardless of framing or recommendation status.
9. A valid recommendation is defined as a clear, actionable recommendation naming the brand, distinct from a neutral reference or cautionary mention.
10. Vasque operates on a small qualified observation count, so percentage movements can appear larger than absolute changes warrant. Vasque's 2.02% coverage represents 15 valid recommendations.
11. The public benchmark measures brand presence, recommendation coverage, placement prominence, and sentiment. It does not measure market share, attributable sales, or organic-search ranking.
12. A movement in a metric identifies a signal worth investigating; it does not by itself establish the cause of that movement.

Get Your AI Visibility Audit

The public benchmark shows where Vasque is losing ground, but a company-level audit is needed to explain why. A detailed AI visibility audit maps the specific prompts, surfaces, and competitor displacement patterns behind the coverage decline, and identifies the owned content and citation sources that can restore Vasque's presence in AI-generated recommendations.