We sent 26 real buying questions to ChatGPT and Google AI Overviews – the questions shoppers actually ask. Then we counted which providers get named, and how often. The result is a ranking built from real AI answers, not opinion.
Nivea (12.9%) and Rexona (7.8%) dominate the answers – the remaining 82 named providers split what's left. If you're not here, you simply don't exist to AI users. That's exactly the gap BuzzView makes visible.
Share of all brand mentions across 26 prompts (Share of Voice). The longer the bar, the more often AI names the provider – across every question tested.
Behind the ranking — what the numbers actually mean for brands in this space.
Across 26 real shopper prompts, AI systems named 82 distinct deodorant brands — a striking signal of how fragmented this category appears inside large language models. The total mention count reached 387, meaning the average named brand received fewer than five mentions across the entire study. That baseline figure alone tells a story: no single deodorant brand has achieved the kind of dominant mental model saturation that software categories like CRM or cloud storage tend to show. The field is genuinely open, and AI answers reflect that dispersal.
Nivea leads with 50 mentions and a 12.9% share of voice — the only brand that clearly separates itself from the pack. Rexona follows at 30 mentions (7.8%), and Vichy comes in third at 22 (5.7%). After those three, the leaderboard compresses rapidly: Dove (4.7%), Weleda (4.4%), and Sebamed (4.4%) are virtually tied. Add all six together and you reach 39.8% of total mentions. In a healthy power-law distribution you might expect the top 6 to control 60–70% of attention. Here they capture barely two-fifths, which means the remaining 60% of mentions is split across 76 further brands.
This fragmentation has a structural explanation. Deodorant purchasing decisions are highly personal and segmented by skin type, gender, aluminium preference, scent, and format. AI models mirror this reality: they were trained on millions of skin-care forum threads, pharmacy review sites, and lifestyle articles that each champion niche and specialist products. When a user asks for "the best deodorant for sensitive skin without aluminium," the model does not default to the mass-market giant — it reaches for Weleda, Lavera, or Greendoor because that is what the training data associates with that specific context. The concentration pattern is therefore a direct reflection of how segmented shopper intent actually is.
Brands that wish to increase their AI share of voice cannot simply rely on overall brand size. The 70 brands captured in the "andere" (other) bucket collectively earned 160 mentions — more than any single tracked provider. That long tail is not noise; it represents specialist and private-label brands that dominate specific prompt sub-segments. Mass-market players like Rexona and Dove actually face more risk from the long tail than from each other, because each niche they fail to address is a pocket where a smaller competitor earns AI recommendations instead.
The deodorant category is one of the most fragmented AI-search landscapes we have measured. Winning here requires owning a specific sub-segment with depth, not chasing broad awareness across all prompts.
Visibility score measures how many of the 26 test prompts a brand appears in at least once — expressed as a percentage. Nivea scores 80.8%, meaning it surfaces across more than four in five distinct prompt contexts. That is a genuinely remarkable breadth: Nivea gets mentioned whether the question is about heavy sweating, sensitive skin, men's grooming, or general best-of rankings. No other brand comes close to that cross-context reach. Rexona is second at 53.8% and Vichy at 50.0%, but even those figures represent a visibility gap of nearly 30 percentage points versus the leader.
At the bottom of the tracked cohort sit Wild and Seinz at 23.1% each — appearing in roughly one in four prompts. Both are relatively newer or more niche entrants that have carved out a presence in sustainability-oriented and refillable deodorant conversations, but have not yet accumulated the breadth of editorial coverage needed to surface across diverse prompt types. Hidrofugal (38.5%), a specialist anti-perspirant brand, and CD (30.8%), a pharmacy-distributed classic, fall in the middle tier. Their visibility scores reflect genuine prompt-type specificity rather than overall brand weakness.
What drives AI visibility in the deodorant category? Our analysis points to three primary factors. First, breadth of editorial coverage: Nivea appears in general lifestyle articles, men's grooming guides, sensitive-skin roundups, and pharmacy-chain blogs simultaneously. Second, explicit product differentiation: brands with a clear and memorable claim — "no aluminium," "72-hour protection," "dermatologist-tested" — earn mentions in the specific prompt types that reference those claims. Third, retail presence signals: pharmacy chains like DM and Rossmann frequently publish buying guides that AI models index heavily, and brands prominent in those guides gain a structural visibility advantage.
The 57.7 percentage-point gap between Nivea and the bottom-tier brands is a clear opportunity signal. A brand sitting at 23–30% visibility can realistically close half that gap within 12 months through targeted content investment — specifically by producing in-depth, expert-authored articles that address the prompt types where the brand is currently absent. Brands like Lavera (26.9%) and Perspirex (26.9%) are well-positioned for this kind of targeted climb: they have sufficient editorial credibility to expand, but currently lack the cross-category content breadth that would let AI surface them across more diverse shopper questions.
A 57.7-point visibility gap separates the category leader from the bottom tier. That gap is not destiny — it is a content gap. Brands between 23% and 40% visibility have the most to gain from systematic prompt-type coverage expansion.
Best-of prompts — "bestes Deodorant 2026," "bestes Deo für Männer," "bestes Deo für Frauen mit empfindlicher Haut" — reliably surface Nivea, Rexona, and Dove. These are the brands with the most accumulated general-purpose editorial coverage and the highest retail review volume. When a prompt asks for a simple ranked list with no qualifying criteria, AI models default to what the broadest set of sources agrees upon, and mass-market incumbents win by sheer weight of coverage. Specialised brands like Lavera or Greendoor rarely appear in these contexts because their editorial footprint is concentrated in niche publications rather than distributed across mainstream lifestyle media.
Comparison prompts — such as "Nivea vs Rexona – which is better?" — produce a fundamentally different dynamic. Here the AI must evaluate specific attributes against each other: long-lasting protection, skin compatibility, scent options, price. Brands like Vichy and Sebamed often enter the conversation as alternatives or third-party benchmarks even when not directly named in the prompt, because AI models frame comparisons within a broader competitive context. Perspirex, the specialist anti-perspirant, also gains disproportionate visibility in comparison contexts because its clinical positioning makes it a structurally distinct category within the broader deodorant space.
Alternative-seeking prompts — "Was ist eine Alternative zu Nivea?" — unlock the largest beneficiary diversity of any prompt type. When a user explicitly signals dissatisfaction with a market leader, AI models interpret this as permission to recommend the full competitive landscape. Brands like Weleda, Lavera, Greendoor, and Alverde appear prominently here, particularly when the implied reason for seeking an alternative is a desire for natural or aluminium-free formulations. Use-case and vertical prompts — "bestes Deo ohne Aluminium," "bestes Deo gegen starkes Schwitzen" — create the narrowest winner sets and highest specialist concentration. Perspirex dominates heavy-sweating prompts; Weleda, Lavera, and Greendoor collectively own the aluminium-free segment.
The content strategy implication is stark: most deodorant brands currently invest in one content type and then wonder why their AI visibility is limited to one prompt cluster. Cien (34.6% visibility, 3.6% SOV) is a good example — it shows up in budget and private-label contexts but is almost entirely absent from sensitive-skin or specialist prompts despite being distributed in DM stores where those products are heavily co-located. Building cross-prompt-type coverage means producing genuinely different content pieces — not variations on a brand page, but expert editorial targeted at each distinct query intent, published in sources with the domain authority to be indexed and trusted by AI models.
Prompt type is the hidden variable behind AI visibility. A brand can double its effective reach without a single new product launch — purely by producing content that matches the intent structure of the prompt types where it is currently absent.
One of the most striking findings in this dataset is that no tracked brand received a single negative mention across all 387 recorded brand references. This is not unusual for FMCG categories where AI models tend to avoid explicit criticism in favour of qualified recommendations, but it does mean that the meaningful sentiment signal lies in the ratio of positive to neutral mentions rather than the presence of negative framing. A neutral mention — "Rexona is also available" — is fundamentally different from a positive recommendation: "Rexona offers reliable 48-hour protection and is widely available in pharmacies." Both count as mentions; only one converts intent.
Balea stands out with the highest positive-mention ratio in the dataset: 11 positive mentions out of 12 total (92%). For a private-label brand sold exclusively at DM drugstores, this is a remarkable figure. It suggests that when AI models do mention Balea, they almost always frame it as a genuine recommendation rather than a passing reference — likely because Balea's appearance in AI responses is concentrated in "budget-friendly" and "value" prompt contexts where it is the clear answer rather than one of many options. Weleda posts a similarly high rate at 14 positive out of 17 total mentions (82%), reflecting the brand's strong positioning in health-conscious and natural cosmetics editorial content.
Nivea, by contrast, achieves 24 positive and 26 neutral mentions — a 48% positive rate despite leading in raw mention volume. The interpretation here is nuanced: Nivea's scale means it appears even in contexts where it is not the optimal answer, resulting in a higher proportion of neutral "also consider" references. Rexona (17 positive / 13 neutral, 57% positive rate) and Vichy (13/9, 59% positive rate) show healthier positive ratios relative to their mention volumes, suggesting their editorial footprint is better matched to the specific prompts that surface them. Greendoor and Lavera both achieve 10 positive out of 13 total (77%), driven by their dominance in natural and eco-conscious product contexts where they are the specific answer, not a generic fallback.
What drives positive sentiment in the deodorant category? Three factors emerge consistently across the leaderboard. First, specificity of claim: brands with concrete, verifiable performance claims ("clinically tested," "72h protection," "vegan and certified natural") earn positive framing more reliably than brands with diffuse lifestyle positioning. Second, third-party validation: dermatologist endorsements, eco-certifications like NATRUE or COSMOS, and pharmacy-chain editorial recommendations are frequently cited by AI models when framing a positive recommendation. Third, review aggregation: brands with a high density of positive user reviews on pharmacy platforms earn proportionally more positive AI framing because those review signals feed into the training data that shapes model responses.
Raw mention volume does not equal recommendation quality. Brands like Balea and Weleda punch above their weight on positive sentiment because their editorial presence is highly targeted. Volume leaders like Nivea need to actively cultivate positive-framed contexts to avoid becoming a neutral reference point rather than a genuine AI recommendation.
When we compare the deodorant AI landscape to more mature AI-search categories like project management software or cloud ERP, the contrast is immediate. Mature categories show a tight top-five that captures 70–80% of all mentions, stable rankings across prompt types, and strong sentiment differentiation. The deodorant category shows none of those characteristics. Eighty-two distinct brands named, a top-six that controls under 40% of mentions, and highly variable winners across prompt types — all of these are signals of a category that is still in the early, fragmented phase of AI-search development.
What does "early AI-search development" mean in practical terms? It means that AI models have not yet developed stable, consistent mental models for which brands belong in which tier of the deodorant market. The training data they rely on is derived from a heterogeneous mix of pharmacy reviews, lifestyle blogs, dermatology articles, and retailer product pages — all published at different times, with different quality levels, and representing different market moments. The result is high variance in outputs: ask two slightly different questions and you can get entirely different brand sets in the response. This variance will reduce over time as AI models are updated with fresher, more authoritative data — but for now it means the ranking is genuinely contestable.
The category's maturity level also manifests in the composition of the long tail. The 70 brands captured in the "andere" bucket — accounting for 160 mentions, or 41.3% of all brand references — include a significant proportion of regional, pharmacy-exclusive, and direct-to-consumer brands that have little or no presence in mainstream retail analytics. Brands like Seinz (23.1% visibility) and Wild (23.1%) represent a new generation of refillable and sustainability-positioned deodorants that are early in their AI-search journey but have already established a measurable footprint. Their current low-visibility scores are a baseline measurement, not a ceiling.
For brands wanting to build AI visibility in the deodorant category right now, the window of opportunity is genuinely open. Unlike a mature category where the top two or three players have accumulated years of editorial dominance and would require enormous investment to dislodge, the deodorant category's fragmented state means that a focused 12-month content strategy can produce measurable rank movement. The playbook is clear: identify the two or three prompt sub-segments where your brand has the strongest organic claim — sensitivity, sustainability, performance, value — and saturate those specific contexts with authoritative, deeply expert content published through channels that AI models index with high trust. That is how Weleda and Greendoor have already climbed into the top ten despite their limited marketing budgets compared to Nivea or Rexona.
The deodorant AI-search landscape is early and highly contested — which means the brands that invest systematically in AI visibility today will lock in positions that become significantly harder to challenge once the category matures and ranking patterns stabilize.
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No wishful thinking: the ranking comes from exactly these prompt types – best-of questions, comparisons, alternatives and use cases.
Visibility score = share of prompts where the provider appears in the AI answer at all. 100% means: present for every relevant question.
We set the real search and buying questions of your industry – exactly how your customers actually ask AI.
Every prompt runs against all major AI models. We count mentions, position, sentiment and the cited sources.
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