Stannah AI Market Strategy Report - Stairlifts

Answer Capsule

Stannah holds the third-largest valid recommendation coverage in the September 2026 Stairlifts benchmark at 23.8%, behind Bruno at 29.4% and Harmar at 25.8%. The brand is visible in 49.3% of qualified observations and converts that presence into top-three recommendations at a 17.9% rate, matching Harmar, while its 6.8% rank-one rate exceeds Harmar's 4.3%. Stannah's clearest strength is first-position recommendation quality, particularly on Perplexity and ChatGPT, and its clearest weakness is a 4.2-point coverage decline from its July 2026 baseline. The clearest opportunity is converting its broad presence into more top-three placements, since roughly two-thirds of the answers where Stannah appears do not place it in a recommendation shortlist.

Who This Report Is For

This report is written for Stannah's commercial, marketing, and category leadership teams, and for stairlift category buyers, analysts, and partners evaluating how AI systems present the brand at the recommendation stage.

Report Card

Executive Summary

Stannah enters September 2026 as the third-ranked brand in the Stairlifts AI Market Discovery Index, with 23.8% valid recommendation coverage across 442 qualified observations. The benchmark shows a brand with strong presence and a healthy first-position profile, but one that is not converting its visibility into shortlist placements at the rate its presence would support.

The brand recorded 218 present observations out of 442, a raw mention presence rate of 49.3%, the third-highest in the category behind Bruno at 68.1% and Harmar at 63.3%. Of those mentions, 130 were positive, 88 were neutral, and zero were negative, producing a net sentiment score of 0.60, the highest in the tracked set. The framing layer is clean.

Recommendation conversion is where the gap appears. Stannah earned 105 valid recommendations, a coverage rate of 23.8%, and 79 top-three placements, a top-three rate of 17.9%. That top-three rate is identical to Harmar's, but Stannah's rank-one rate of 6.8% is 2.5 points higher than Harmar's 4.3%, meaning Stannah wins first position more often when it is shortlisted. The average recommended rank of 2.09 is the second-best in the category behind Bruno's 1.65.

The strongest platform signal is Perplexity, where Stannah recorded a 40.0% top-three rate and a 26.7% rank-one rate across 15 observations, with a net sentiment of 1.00. ChatGPT is the second-strongest platform at a 33.3% top-three rate and an 18.2% rank-one rate. Google AI Mode is the weakest relative platform, with a 6.4% top-three rate and a 0.8% rank-one rate across 125 observations, the largest single-platform observation pool in the dataset.

Against the July 2026 baseline, Stannah's coverage declined 4.2 points from 28.0% to 23.8%, a movement the benchmark classifies as within normal month-to-month variation. The brand's rank-one rate rose 3.3 points over the same period, from 3.5% to 6.8%, which means Stannah is being named first more often even as its overall shortlist coverage softened. The August 2026 measurement recorded zero recommendations for every tracked brand and is treated by the benchmark as an instrument-state anomaly rather than a competitive outcome.

The clearest gap is the distance between presence and shortlist placement. Stannah appears in roughly half of qualified observations but is recommended in fewer than a quarter of them, and is placed in the top three in fewer than one in five. The category leader, Bruno, converts a 68.1% presence rate into a 24.9% top-three rate; Stannah converts a 49.3% presence rate into a 17.9% top-three rate. Closing that conversion gap, rather than chasing raw visibility, is the highest-leverage move available to the brand.

What Stannah Is Winning

Questions This Section Answers

• Which metrics put Stannah at the top of the stairlift category?
• Where does Stannah earn the strongest recommendation credit in AI answers?

Stannah holds the highest net sentiment score in the category at 0.60, ahead of Handicare at 0.59 and Bruno at 0.57. Across 218 present observations, the brand recorded 130 positive mentions, 88 neutral mentions, and zero negative mentions. No other tracked brand with meaningful presence matched that combination of volume and clean framing.

The brand's rank-one rate of 6.8% is the second-highest in the category, behind only Bruno's 14.2% and ahead of AmeriGlide's 5.0%. This is a meaningful signal because rank-one placement is the strongest form of recommendation credit in the benchmark, and Stannah earns it more often than every brand except the category leader.

Stannah's average recommended rank of 2.09 is also the second-best in the category, behind Bruno's 1.65 and ahead of Stannah's nearest coverage competitor Harmar at 2.76. When Stannah is recommended, it tends to be recommended near the top of the list.

Perplexity is Stannah's strongest platform by recommendation behavior. Across 15 observations, the brand recorded a 40.0% top-three rate, a 26.7% rank-one rate, and a net sentiment of 1.00, with 10 positive mentions and zero neutral or negative mentions. ChatGPT is the second-strongest platform, with a 33.3% top-three rate and an 18.2% rank-one rate across 33 observations.

The brand also improved its rank-one rate against the July 2026 baseline, rising 3.3 points from 3.5% to 6.8%. That gain occurred even as overall coverage declined 4.2 points, which indicates Stannah is winning first position more decisively in the answers where it does appear.

Where Stannah Has the Clearest AI Visibility Gaps

Questions This Section Answers

• Where does Stannah's AI presence fail to convert into a recommendation?
• Which platform shows the largest gap between Stannah's visibility and its shortlist placement?

The primary gap is recommendation conversion. Stannah appears in 49.3% of qualified observations but receives valid recommendation credit in only 23.8% of them. That means roughly half of the answers where Stannah is mentioned do not convert into a recommendation, and roughly two-thirds do not convert into a top-three placement. The benchmark treats these as distinct signals, and the distance between them is where competitive displacement is happening.

Google AI Mode is the clearest platform-level gap. Across 125 observations, the largest single-platform pool in the dataset, Stannah recorded a 6.4% top-three rate and a 0.8% rank-one rate. By comparison, AmeriGlide recorded a 29.6% top-three rate and a 15.2% rank-one rate on the same platform, and Bruno recorded a 21.6% top-three rate and a 12.8% rank-one rate. Stannah's presence on Google AI Mode is 32.0%, which is not negligible, but that presence is not converting into shortlist placement at anything close to the rate the category leaders achieve.

Google AI Overviews shows a similar pattern at a smaller scale. Stannah recorded a 22.0% top-three rate on AI Overviews, which is competitive, but its 5.7% rank-one rate trails Bruno's 20.6% on the same platform. The brand is being shortlisted on AI Overviews but not named first.

Against the July 2026 baseline, Stannah's coverage declined 4.2 points from 28.0% to 23.8%. The benchmark classifies that movement as within normal variation, but it occurred alongside a 4.2-point decline in the brand's top-three rate, from 22.0% to 17.9%. Harmar and Stannah recorded identical 17.9% top-three rates in September 2026, but Stannah's rank-one rate of 6.8% exceeded Harmar's 4.3% by 2.5 points, which means Stannah is winning first position more often while losing shortlist breadth.

The category-level context matters here. Five of ten tracked brands moved beyond normal variation against the July baseline, all downward, and the category's recommendation-shaped answer share fell 13.7 points from 34.1% to 20.4%. Stannah's decline sits inside a broader category contraction, but the brand's rank-one gain shows that first-position credit is still available to brands that earn it.

Biggest Opportunity

Questions This Section Answers

• Which platform offers Stannah the largest opportunity to close its conversion gap?
• What needs to change for Stannah to become shortlist-eligible on Google AI Mode?

The single largest opportunity for Stannah is closing the conversion gap between presence and top-three placement on Google AI Mode. The brand already appears in 32.0% of Google AI Mode observations, the platform carries the largest observation pool in the dataset at 125, and competitors are converting that same platform at three to five times Stannah's top-three rate. This is not a visibility problem; it is a shortlist-eligibility problem on the platform where the most buyer questions are being asked.

The prompt evidence points to the specific question types where this gap is widest. The qualified cluster is Brand Recommendation, which captures prompts seeking a single recommended brand or a shortlist of brands. Stannah's presence on those prompts is strong, but its placement is inconsistent. The work is to make the brand's differentiators retrievable and attributable in the source layer that Google AI Mode draws on, so that when the system assembles a shortlist, Stannah's attributes are available to be cited rather than merely mentioned.

Competitive Landscape

Questions This Section Answers

• How does Stannah compare to Bruno and Harmar at the recommendation stage?

Bruno holds the strongest recommendation-stage position in the Stairlifts category, with Harmar and Stannah forming a second tier on coverage and AmeriGlide close behind on presence. Stannah sits third on valid recommendation coverage and second on rank-one rate, which places it among the category's recommendation leaders but behind Bruno on both shortlist breadth and first-position frequency.

Average recommended rank covers rank-eligible recommendations only.

Stannah's position in the table shows a brand that matches Harmar on top-three rate but outperforms it on rank-one rate and average recommended rank, and that carries the highest sentiment score in the tracked set. The gap to Bruno is concentrated in shortlist breadth rather than first-position quality.

Prompt Evidence

Questions This Section Answers

• Which prompts show Stannah's strongest and weakest recommendation outcomes?

Perplexity / Brand Recommendation Prompt: "stairlift" Result: Stannah recorded a 40.0% top-three rate and a 26.7% rank-one rate on Perplexity across 15 observations, with a net sentiment of 1.00 and zero negative mentions.

Google AI Mode / Brand Recommendation Prompt: "stair lift" Result: Stannah recorded a 6.4% top-three rate and a 0.8% rank-one rate across 125 Google AI Mode observations, while AmeriGlide recorded a 29.6% top-three rate on the same platform.

ChatGPT / Brand Recommendation Prompt: "chair lift for stairs" Result: Stannah recorded a 33.3% top-three rate and an 18.2% rank-one rate across 33 ChatGPT observations, with 12 positive mentions and zero negative mentions.

Google AI Overviews / Brand Recommendation Prompt: "stairlifts near me" Result: Stannah recorded a 22.0% top-three rate and a 5.7% rank-one rate across 141 AI Overviews observations, while Bruno recorded a 20.6% rank-one rate on the same platform.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map Stannah's prompt-level outcomes across all six platforms, isolating the Google AI Mode and Google AI Overviews question types where presence is high but top-three placement is low.

Phase 2: Recommendation Readiness Plan Prioritize the prompt clusters and platforms where the conversion gap is widest, and define the attribute set Stannah needs AI systems to associate with the brand at the shortlist stage.

Phase 3: Owned Answer Layer Buildout Strengthen the owned pages that answer the highest-intent stairlift questions, so the brand's differentiators are stated in extractable, attributable form rather than implied.

Phase 4: Citation / Authority Layer Development Build the public evidence layer that AI systems retrieve from, including third-party sources, comparison contexts, and category references that support Stannah's shortlist eligibility.

Phase 5: Monthly AI Visibility and Recommendation Tracking Track valid recommendation coverage, top-three rate, rank-one rate, and sentiment by platform each month, and measure whether the conversion gap narrows against the September 2026 baseline.

Why This Matters

Questions This Section Answers

• Why does recommendation placement matter more than visibility for stairlift brands?

AI systems are now forming the buyer shortlist before a buyer ever visits a brand's website. In the Stairlifts category, the September 2026 benchmark shows that recommendation-shaped answers accounted for 20.4% of qualified observations and valid recommendation shortlists appeared in 35.5%, which means a meaningful share of high-intent buyer questions are being answered with a named set of brands. Stannah is in that set more often than most, but it is not in the top three as often as its presence would support.

Presence alone does not win the shortlist. The benchmark separates raw mention presence from valid recommendation coverage and from top-three placement for exactly this reason, and Stannah's numbers show a brand that is seen but not always chosen. The next move is targeted correction of the prompt, page, and citation layers on the platforms where the conversion gap is widest, starting with Google AI Mode, so that the brand's attributes are available to AI systems at the moment recommendations are formed.

Core Metrics

Questions This Section Answers

• What are Stannah's core AI visibility and recommendation metrics for September 2026?

Sentiment Score

Questions This Section Answers

• How is Stannah's sentiment score calculated and why does it matter?
• Why is classified sentiment required before interpreting AI visibility?

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

For Stannah in September 2026, that calculation is (130 × 1 + 88 × 0 + 0 × -1) / 218, which produces a score of 0.5963. This is the highest net sentiment score among the ten tracked brands.

The score matters because unclassified mention counts are misleading. A brand that appears frequently but is described cautiously, listed as a comparison anchor, or mentioned only in passing is not in the same position as a brand that is described positively and recommended by name. Counting all mentions as wins would flatten those distinctions and overstate the brand's position.

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 in commercial terms, and treating them as equal produces a number that looks healthy while hiding the actual recommendation position. Classified sentiment is required before interpreting AI visibility, because it separates the framing layer from the recommendation layer and shows whether a brand is being described in terms that support a shortlist decision.

Sentiment by Platform

Questions This Section Answers

• Which platforms show the strongest and weakest sentiment for Stannah?

Methodology

1. This report is a benchmark-based analysis of Stannah's position in the September 2026 Stairlifts AI Market Discovery Index, produced by CiteWorks Studio from LLM Authority Index benchmark data. It is not a client implementation result.
2. The reporting window is September 2026, with comparison against the July 2026 baseline and the August 2026 intermediate measurement.
3. Six canonical AI surface families were tracked: ChatGPT, Copilot, Gemini, Perplexity, Google AI Overviews, and Google AI Mode. All six had qualified observations in September 2026.
4. The collection universe was 800 prompt-surface observations, producing 562 unique questions after deduplication, 798 brand or competitor mentions, 540 relevant observations, 258 irrelevant observations, and 442 qualified benchmark observations.
5. Ten brands were tracked: 101 Mobility, Acorn Stairlifts, AmeriGlide, Bruno, Handicare, Harmar, Lifeway Mobility, Mobility Plus, Savaria, and Stannah.
6. One qualified buyer-intent cluster was populated in September 2026: Brand Recommendation, which captures prompts seeking a single recommended brand or a shortlist of brands. The Pricing and Value and Multi-Brand Comparison clusters recorded zero qualified observations in both July and September 2026.
7. Stage 0 extraction produced the prompt-level observation record, retaining query, surface, answer, brand outcome, recommendation placement, sentiment, and, where exposed, citations or attributable evidence sources.
8. A mention is counted when a tracked brand appears in a qualified observation, regardless of whether the mention is a recommendation.
9. A valid recommendation is counted when a brand receives legitimate recommendation credit in a qualified observation. Negative, neutral, cautionary, comparison-anchor, and listed-only mentions are not counted as valid recommendations.
10. Brand-level percentages use the 442 qualified observations as the public denominator, not the raw 800-prompt collection.
11. The August 2026 measurement recorded zero valid recommendations for all ten tracked brands and is treated by the benchmark as an instrument-state anomaly rather than a competitive outcome. All baseline movement is measured against July 2026.
12. Small-count brands such as Mobility Plus and 101 Mobility carry limited observations, and their percentages should be read alongside their absolute counts. Stannah's platform-level figures on Perplexity and ChatGPT rest on smaller observation pools than its Google AI Mode and Google AI Overviews figures and should be interpreted with that in mind.

See Where AI Is Recommending Your Brand

The public benchmark shows where Stannah stands in the category. A company-level AI visibility audit maps the prompt, surface, competitor, ranking, sentiment, and evidence-source patterns behind those numbers into a prioritized strategy, starting with the platforms and question types where the conversion gap is widest.