We sent 26 real buying questions to ChatGPT and Google AI Overviews – the questions households actually ask. Then we counted which providers get named, and how often. The result is a ranking built from real AI answers, not opinion.
Persil (15.7%) and Ariel (7.1%) dominate the answers – the remaining 58 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 buying prompts, AI systems named 58 distinct laundry detergent brands — a number that underscores just how crowded this category is in both retail and in the minds of AI models. Yet despite that apparent breadth, the distribution of attention is anything but even. Persil alone accounts for 55 of the 350 total brand mentions, giving it a 15.7% share of voice. The top six brands — Persil, Ariel, Denkmit, Everdrop, Frosch, and Sonett — together collect 166 mentions, or 47.4% of everything the AI said. The remaining 52 brands share the other half.
This is a textbook power-law distribution: a small cluster of names dominates AI recall, while the long tail of 46 "other" brands collectively contribute 98 mentions spread so thinly that no single one registers a meaningful share of voice. For a category with shelf presence from hundreds of products — discount supermarket own-labels, eco startups, specialty detergents — this concentration is striking. AI compresses consumer choice signals into a very short mental shortlist.
The split between mass-market and eco brands inside the top six is particularly revealing. Persil and Ariel represent established Henkel and P&G powerhouses with decades of advertising spend and media coverage. Everdrop and Sonett, by contrast, are sustainability-focused challengers with a fraction of the marketing budget. That they appear alongside the giants in AI recommendations suggests that strong editorial coverage — eco media, test reports, sustainability rankings — can substitute for raw advertising volume in the sources AI models are trained on.
For brands outside the top six, the picture is sobering. Tandil, Domol, Ecover, and Formil each earn between 14 and 17 mentions — enough to appear occasionally, but not enough to anchor any specific prompt type. Lenor, despite being a Procter & Gamble fabric conditioner brand with heavy retail presence, manages only 10 mentions and a 2.9% share of voice, suggesting that category adjacency (fabric care vs. detergent) creates confusion that limits AI recall. Position in the top six is not merely a ranking distinction; it is the difference between being consistently recommended and being an occasional footnote.
AI visibility in laundry detergent is highly concentrated: if your brand is not in the top six, you are statistically invisible in most AI-generated buying recommendations. Earning a consistent presence requires a deliberate content and PR strategy, not just product quality or retail distribution.
Persil's visibility score of 80.8% means it appears in more than four out of every five AI responses when a consumer asks a laundry-related buying question. At the other end of the tracked set, brands like HAKA and Gut & Günstig register a 19.2% visibility score — present in roughly one in five responses. That is a fourfold performance gap between the most visible and the least visible brands in this study, a range that dwarfs what you would typically see in a Google organic ranking comparison.
Three brands — Ariel, Denkmit, and Everdrop — cluster at exactly 53.8% visibility, a meaningful second tier. They appear in more than half of AI responses but consistently fall short of the recommendation frequency that would put them on par with Persil. This cluster is particularly interesting because it includes both a P&G legacy brand (Ariel) and two price-positioning brands from discount retail (Denkmit for dm, Everdrop as a DTC eco brand). The fact that they occupy the same visibility tier despite radically different business models suggests that AI doesn't care about channel strategy — it cares about content volume and editorial recognition.
What drives visibility in the laundry detergent category specifically? Test reports from Stiftung Warentest and eco-test are major factors — AI models draw heavily on structured, authoritative consumer test outcomes. Brands that have been repeatedly featured in these publications over multiple years accumulate a strong citation base. Online pharmacy and drugstore content (dm, Rossmann, Aldi product descriptions) also contributes, as does coverage in parenting forums for allergy-friendly detergents and eco-blogs for sustainability-positioned brands.
The brands at the bottom of the visibility range — Lenor (23.1%), Perwoll (23.1%), Dalli (23.1%), and Spee (23.1%) — share a common trait: they tend to be positioned as either specialty products (Perwoll for wool and delicates), value alternatives (Spee, Dalli), or fabric conditioners misclassified as detergents (Lenor). AI handles niche positioning poorly when prompt language is generic. A brand that exclusively dominates one sub-segment — say, cold-wash sport detergent — may have high recall in that specific prompt type but very low recall overall, dragging its aggregate visibility score down sharply.
Visibility in this category is built on authoritative third-party test coverage and editorial breadth, not just market share. Brands in the 20–40% visibility range have a clear path upward — but it runs through consumer test platforms and topical content, not paid media.
The 26 prompts in this study were not all equivalent. They span four distinct intent types: best-of questions ("bestes Waschmittel 2026"), comparison-seeking questions, alternative-seeking questions, and use-case-specific questions (allergy sufferers, baby laundry, sportswear, cold-wash cycles). Each intent type surfaces a meaningfully different ranking. Persil dominates best-of and general buying prompts almost by default — it is the category shorthand that AI has learned. When someone asks for the "best laundry detergent," Persil is named first in the overwhelming majority of responses.
Use-case prompts tell a different story. The prompt for "bestes Waschmittel für Allergiker und empfindliche Haut" (best detergent for allergy sufferers and sensitive skin) reliably surfaces Frosch and Everdrop alongside or even ahead of the mass-market leaders. The prompt for cold-washing at 20 degrees brings Tandil and Domol into the picture as value-oriented alternatives. Sportswear odor prompts tend to recall Ariel's sport-specific marketing campaigns. This prompt-type fragmentation means that category leadership is not monolithic — it is niche-specific, and the winners in one intent type are often completely absent from another.
Comparison and alternative-seeking prompts are where mid-tier brands like Denkmit and Formil punch above their weight. These prompts explicitly invite AI to broaden its recommendations beyond the obvious first choice, and AI responds by pulling from a deeper knowledge pool. Denkmit's strong association with dm drugstores — a trusted retail brand in the German market — gives it a consistent mention whenever AI is asked to name affordable or drugstore alternatives. Formil appears in Lidl-adjacent searches for the same reason. Retail channel authority is a legitimate AI visibility signal in this category.
The content strategy implication is direct: brands that want broader AI visibility across all prompt types need to create content that explicitly addresses multiple use cases. A brand like Sonett, which earns strong visibility in eco-focused prompts (5.1% SOV overall), could expand its footprint by also creating content that positions it for baby laundry and allergy-sensitive use cases — since its natural ingredient profile is genuinely relevant to those queries. The data shows that prompt-type coverage is the multiplier that moves a brand from single-segment recall to category-wide recall.
Eco and sensitivity-specific prompts are the most accessible entry points for challenger brands: Everdrop, Frosch, and Sonett all outperform their overall SOV in these prompt types. Building content depth in one use-case vertical before expanding is a more efficient AI visibility strategy than trying to compete head-on with Persil on generic best-of queries.
Raw mention counts tell only half the story. The sentiment breakdown reveals which brands AI talks about with genuine enthusiasm versus which brands it mentions with caveats or hedges. Tandil stands out as the sentiment leader: 15 of its 17 mentions are positively framed, a rate of 88%. Denkmit follows closely at 80% positive (20 out of 25 mentions), as does Frosch at 80% (16 out of 20). These three brands — two discount private-label brands and one eco brand — earn more consistently positive AI framing than Persil itself, which despite its dominant SOV sits at 51% positive (28 out of 55 mentions).
What drives Persil's comparatively lower positive ratio? Volume is part of it — with 55 mentions, Persil appears in a wider variety of prompt contexts, including comparisons where AI might note trade-offs (price, environmental profile, or fragrance intensity for sensitive skin). Persil also receives 3 negative mentions, alongside 24 neutral ones. The neutral mentions often reflect responses where Persil is named as the default but the AI then redirects to more specialized alternatives for the specific use case. Being the default recommendation is powerful, but it does not automatically mean enthusiastic endorsement.
Lenor is the sole tracked brand to record more negative mentions (3) than positive ones (2), with 5 neutral mentions rounding out its 10-mention total. This is almost certainly driven by category confusion: Lenor is primarily a fabric softener, not a laundry detergent, and when AI mentions it in response to washing detergent prompts it often does so to clarify that distinction — which tends to register as a negative or limiting framing. Ecover, with 6 positive and 10 neutral mentions out of 16 total, shows a different pattern: it is mentioned matter-of-factly as an eco alternative but rarely with the enthusiasm that Frosch or Sonett generate, possibly due to lower brand recognition in the German market versus its Belgian home market.
What drives positive sentiment in the laundry detergent category more broadly? Consumer test results with top ratings are the clearest driver — Stiftung Warentest "Gut" or "Sehr gut" ratings create a durable positive signal that AI models learn and reproduce. Customer review aggregations on platforms like dm.de, Amazon, and Rossmann also contribute. Third-party eco certifications (ECOCERT, EU Ecolabel, Blauer Engel) tend to generate positive framing in sustainability-adjacent prompts. Brands with a clear, singular positioning — price value for Tandil, eco credentials for Frosch — receive more consistently positive framing than brands trying to cover all bases, because AI can anchor its recommendation on a clear factual reason.
Positive sentiment correlates strongly with clear positioning and third-party validation. Brands that have secured top ratings in Stiftung Warentest or carry recognized eco certifications earn disproportionately positive AI framing — even when their overall mention count is modest. Winning sentiment requires giving AI a concrete, citable reason to endorse rather than merely mention.
The laundry detergent category in AI search sits at an interesting juncture. On one hand, it displays the hallmarks of a mature AI category: a single dominant brand (Persil at 15.7% SOV and 80.8% visibility) with a large gap to the second tier, and a well-established shortlist of names that AI reliably produces across most prompt types. On the other hand, 58 distinct providers were named across just 26 prompts — a fragmentation index that is high by any measure. Mature, settled AI categories typically converge to fewer than 15–20 named providers. The German laundry detergent market's unique density of private-label brands and eco challengers inflates this number considerably.
The 46 brands that land in the "others" bucket collectively account for 98 mentions — more than any single tracked brand except Persil. This long tail is not noise; it reflects the genuine diversity of a category where sustainability positioning, price sensitivity, allergy compatibility, and fabric specialization all create legitimate sub-niches that AI surfaces when prompted correctly. The implication is that the category has not yet fully consolidated in AI memory the way that, say, project management software or CRM platforms have. There is still room for new entrants to earn significant AI visibility if they occupy a clearly differentiated position.
The presence of strong discount private-label brands — Denkmit (dm), Tandil (Aldi), Domol (Rossmann), Formil (Lidl), and Gut & Günstig (REWE) — in the AI-recommended set is a distinctive feature of the German market that has no close equivalent in most other European laundry markets. These brands earn AI mentions not because they invest in content marketing but because the retailers behind them have strong editorial authority in consumer comparison contexts. This means that in Germany, AI visibility in laundry detergent is partly a function of retailer brand equity, not just manufacturer investment — a structural factor that manufacturer brands cannot easily overcome without creating compelling product-specific content.
For brands wanting to build AI visibility in laundry detergent now, the window for efficient investment is still open but narrowing. Persil's dominance is durable — it is backed by decades of media investment that created the training data AI relies on. But the second tier (Ariel, Denkmit, Everdrop at 53.8% visibility) is not locked in. A brand that aggressively builds topical content across use cases, secures consistent test-report coverage, and earns eco certifications could realistically move from the 20–40% visibility band into the 50%+ tier within 12 to 18 months. The category is mature enough that the playbook is clear, but fragmented enough that execution still creates competitive differentiation.
The laundry detergent category is AI-mature at the top (Persil's position is structurally reinforced) but still contested in the middle tier. Brands in the 20–55% visibility range face a real opportunity to consolidate their position through targeted content investment — and the structural presence of eco and private-label competitors means differentiation through clear positioning is more effective than trying to compete on raw mention volume alone.
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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.
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Every prompt runs against all major AI models. We count mentions, position, sentiment and the cited sources.
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