# How AI brand recommendations shift across the funnel — and why your decision-stage audit looks different

*By Eastbound Research · 9 May 2026*

Most foreign brands in Mainland China are running an audit on the wrong question. They test "best [category]" against three Chinese AIs, score green, file the report. Then a real buyer adds one word — "budget," "luxury," "for brand asset management" — and the engine recommends a different brand class entirely. Sometimes a different category entirely.

For most of the search era, a recommendation was static. Type "best design software" into Google in the morning and again at night, and the answer didn't move much. Marketers built brand audits on that assumption — measure the rank, optimize, measure again. Generative AI broke the assumption. The same engine, asked the same question by the same user, returns different brands depending on signals the marketer doesn't always see. **Buyer intent is the strongest of those signals.**

**Buyer intent in AI search** is the set of signals embedded in a user's prompt — funnel stage, budget pressure, price tier, and use-case specificity — that change which brands an AI engine recommends, even when the underlying product question is identical. The same prompt at "I'm exploring" and "I have to choose now" returns different brand classes. Brand audits that test only un-tagged queries miss the pivot.

This piece isolates four intent signals and measures how each one moves the recommendation set on DeepSeek, Qwen, and Doubao. The data isn't subtle. Brands drop out. Specialist tools enter. The product class itself reframes.

## Funnel stage is the strongest intent signal in AI brand recommendations

The strongest single intent signal in the study is buying-stage commitment. We asked the same prompt at three stages — awareness ("I'm exploring"), consideration ("I'm comparing top three"), and decision ("I have to choose now") — holding everything else constant. The recommendation set shifts substantially at every category × engine cell we measured.

The cleanest example is Qwen on design software. At awareness, Qwen surfaces a mass-market shelf: Canva, 即时设计, MasterGo, and Brandfolder. At decision, all four are gone. **Figma Enterprise, Frontify, Notion, and Zeroheight enter.** The engine reads "I have to choose" as a commitment signal and reshelves toward enterprise-grade specialist tools. Mass-market design tools that anchor the awareness shelf simply aren't on the decision shelf.

### Design software

| Engine | Held across stages | Drops at decision | Enters at decision |
|---|---|---|---|
| DeepSeek | Canva, Figma | Affinity Designer 2, Excalidraw, Frontify | — (Figma deepens its hold) |
| Qwen | Figma | Brandfolder, Canva, MasterGo, 即时设计 | Figma Enterprise, Frontify, Notion, Zeroheight |
| Doubao | Figma, MasterGo | Canva, Notion, 蓝湖 | Figma Enterprise, 即时设计 |

### Travel and hospitality loyalty

Travel shows the same shape, with a twist: alliance frameworks emerge only at decision. DeepSeek's awareness shelf names individual programs (Hilton, Singapore Airlines, Emirates, IHG). Its decision shelf names **alliance-level constructs** — Star Alliance, 国航凤凰知音, 凯悦天地 — together with Marriott Bonvoy.

| Engine | Held | Drops at decision | Enters at decision |
|---|---|---|---|
| DeepSeek | 万豪旅享家 | Emirates, Hilton, IHG优悦会, Singapore Airlines | Marriott Bonvoy, 国航凤凰知音, 凯悦天地, 星空联盟 |
| Qwen | 万豪旅享家, 东方万里行, 国航凤凰知音 | 东航东方万里行, 华住会 | IHG优悦会, Marriott Bonvoy |
| Doubao | 万豪旅享家 | 华住会, 华住会金会员, 南方航空, 南航明珠银卡 | 国泰航空马可孛罗会, 寰宇一家绿宝石, 华住会铂金, IHG Rewards Club |

### Overseas tertiary education

The shift is from *generalist marquee names* at awareness to *program-specific tracks* at decision. Qwen drops HBS, LBS, NUS, and Wharton at decision and brings in HEC Paris, Wharton MBA (the explicit MBA program rather than the general school), Williams College, and NUS MSc Quantitative Finance.

| Engine | Held | Drops at decision | Enters at decision |
|---|---|---|---|
| DeepSeek | HBS, INSEAD, Stanford GSB | London Business School, Wharton | Harvard University, NUS MFE |
| Qwen | INSEAD | HBS, LBS, NUS, Wharton | HEC Paris, NUS MSc Quant Finance, Wharton MBA, Williams College |
| Doubao | INSEAD MBA | HBS Full-Time MBA, LBS MBA, NUS MQuantFin, Stanford GSB Full-Time MBA | INSEAD, NUS MSc Quant Finance, Wharton MBA, 牛津大学 PPE |

> Qwen recommends Canva when you're exploring. It recommends Frontify, Figma Enterprise, and Zeroheight when you have to choose.

## The budget keyword displaces foreign brands in Chinese AI answers

Adding a budget constraint mid-conversation produces a more uniform shift than funnel stage: foreign brands drop, domestic substitutes enter. Across all three categories. Across all three engines.

The mechanism is intuitive once you see it. Foreign brands carry a higher implicit price tag in the engine's mental model. The moment "budget" or "predominantly within ¥X/month" enters the prompt, the engine swaps the brand class. Domestic alternatives that the engine knows are cheaper, locally accessible, and locally supported take the slots.

*Does adding "budget" to a prompt change AI brand recommendations?* In every category × engine cell we measured: yes, materially.

| Category × Engine | Drops when budget added | Enters when budget added |
|---|---|---|
| Design × DS | Figma | Canva (China), Canva可画, MasterGo, 即时设计 |
| Design × Qwen | Frontify, Notion, Zeroheight | Canva中国版, Pixso, 稿定设计, 飞书 |
| Travel × DS | AmEx Centurion, Marriott Bonvoy, 万豪旅享家, 星空联盟 | Air China Phoenix Miles, Huazhu, 华住会, 锦江荟 |
| Travel × Qwen | IHG优悦会, Marriott Bonvoy, 东方万里行, 国航凤凰知音 | 东航东方万里行, 华住会, 深航凤凰俱乐部, 锦江WeHotel |
| Education × DS | Cambridge Judge, HBS, INSEAD Exec Ed, Stanford GSB | HKU Exec Ed, HKUST Business School, NUS MBA |
| Education × Qwen | CEIBS EMBA, HBS SELP, HEC Paris, INSEAD, Wharton MBA | CEIBS+HEC Dual Degree, CEIBS FMBA, Fudan-BI Norwegian MBA, HKUST MBA, NUS MBA |

The implication for foreign brands is uncomfortable but precise. **Their AI visibility is conditional on the user not mentioning price.** Default queries surface them; budget-tagged queries do not.

## How price tier reframes the entire product class AI recommends

The third signal is more subtle than the first two: when the prompt's price tier moves to the high end, the engine doesn't just rerank the same brand class — it changes which class is even relevant. This is the most novel finding in the study.

DeepSeek travel makes the cleanest case. Ask about a budget trip (¥500–1,000/night, three-star hotel, economy class) and DS surfaces hotel and airline loyalty programs. Ask about a luxury trip (¥6,000+/night, five-star, first class) and **DS stops talking about loyalty programs entirely**. AmEx Centurion takes over the top picks. The relevant vehicle for a luxury traveler, in the engine's framing, isn't a points program — it's a top-tier credit-card status.

Doubao does something parallel. At its budget tier, Doubao surfaces individual mid-tier status cards. At its luxury tier, those drop and **alliance-top status** takes their place — 寰宇一家绿宝石 (oneworld Emerald), 凯悦天地环球客 (Hyatt Globalist), 万豪钛金 (Marriott Titanium). Same engine, same category, two intent tiers, two completely different product taxonomies.

| Engine | Budget tier — top picks | Luxury tier — top picks | What changed |
|---|---|---|---|
| DS travel | 万豪旅享家, Marriott Bonvoy, Air China Phoenix Miles, 国航凤凰知音 | AmEx Centurion, AmEx Centurion/Platinum | Loyalty programs → credit cards |
| Doubao travel | 东方航空银卡, 华住会铂金, 东方航空万里行银卡, IHG Rewards Club | 寰宇一家绿宝石, 凯悦天地环球客, 万豪钛金, 美联航环球客, GHA黑卡 | Mid-tier status → alliance-top status |

The strategic implication is unfamiliar to most brand teams. **A brand can be invisible in its own category at a different intent tier.** Hilton at ¥6,000/night isn't ranked low — it's not being recommended at all, because at that intent the engine isn't recommending hotel programs anymore.

> Ask DeepSeek about luxury travel and it stops talking about hotels.

## How use-case wording surfaces specialist tools instead of category leaders

The last signal is the most actionable for second-place brands. When the use-case word in the prompt sharpens — "for product design" → "for brand asset management" → "for design-system documentation" — engines substitute specialist tools for category leaders, even at the same price tier and same buying stage.

Hold the price tier and buying stage constant; vary only the use case. **For "initial product design," Qwen surfaces Figma. For "brand asset management" at the same price tier, Qwen surfaces Frontify — and Figma drops out entirely.**

| Engine | "Initial product design" | "Brand asset management" | What changed |
|---|---|---|---|
| DS | Figma | Figma + Canva | Adds Canva for asset organization |
| Qwen | Figma | Frontify (Figma drops) | Specialist replaces category leader |
| Doubao | MasterGo | MasterGo + 即时设计 | Adds 即时设计 for asset workflows |

The optimistic reading: **brands that are not category leaders have a real path to AI visibility — by owning a use-case keyword.** Frontify on "brand asset management." Zeroheight on "design system documentation." The engine respects the use-case framing and surfaces the right specialist when the framing is sharp.

## What this means for your China AI visibility strategy

Three takeaways follow.

**Awareness brand-tracking metrics overestimate brand value.** A brand that wins at "exploring" loses ground at "choosing." Awareness investment that ranks at the top-of-funnel doesn't necessarily convert to share at decision-stage. If a brand is measuring AI visibility on un-staged prompts, it's measuring an attribute that doesn't reach the buying moment.

**Foreign brands are visible only when budget is unspoken.** Once price intent enters the prompt, domestic substitutes systematically replace foreign brands. The audit dimension that matters most is the constraint-conditioned one — default, budget, localization, price-tier — not the un-tagged one.

**Specialist tools beat category leaders on use-case framing.** The path for non-flagship brands is to own a specific use-case keyword. The engine respects sharper framings.

## Where Eastbound comes in

Eastbound runs intent-conditioned brand visibility studies on DeepSeek, Qwen, and Doubao. The engagement starts with a stage-and-constraint diagnostic — we test your brand under default, budget, localization, and price-tier prompts, and show you exactly where your visibility breaks. If your team is sitting on the green-audit / red-engine gap, [run the free China AI visibility audit](https://eastbound.ai/ai-visibility-audit/) on your domain or [book an intro call](https://eastbound.ai/book-consultation/).

## Methodology

- **Sample.** 3,024 calls (66 segmentation prompts × 3 categories × 3 reps × 4 turns × 3 engines, plus a 20% retest 24–48 hours later). Three categories: design / collaboration software, overseas tertiary education for Mainland-Chinese applicants, premium travel and hospitality loyalty.
- **Engines.** DeepSeek (`deepseek-chat`); Qwen-Plus on DashScope international (`dashscope-intl.aliyuncs.com`); Doubao Seed-2-0-Pro on BytePlus ModelArk international (`ark.ap-southeast.bytepluses.com`). Temperature 0.7.
- **What we measured.** Brand mention rate per (prompt × stage × rep) cell; which brands drop and which enter as a single prompt variable changes. This is descriptive measurement of LLM recommendation behavior; it is not a causal claim about training data, retrieval, or human conversion.
- **What we did not measure.** Sales, conversion, attributable revenue. ChatGPT / Claude / Gemini / Perplexity / ERNIE / Yuanbao — not in this panel. Chat-with-Search-ON browsing surfaces — separate study.
- **Reliability.** Pooled cross-run top-5 brand-set Jaccard — DeepSeek 0.27, Qwen 0.34, Doubao 0.24. Per-rep matched cross-run is lower (0.12–0.17), which is why every claim above is a panel-level aggregate, not a per-prompt fact.
- **Limitations.** The coding model is DeepSeek-Chat — DS-vs-Qwen and DS-vs-Doubao contrasts may be slightly inflated by coder alignment with the DS surface. Neither DashScope nor BytePlus exposes pinned model-snapshot handles.

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