#1 moment for ‘日傘’: "ゴルフラウンド中に日差しが気になる時に" 45.3%

01At a glance

4,007,164

Monthly situation search volume

7

CEPs

7

Unclaimed situations

45.3%

Largest CEP share · ゴルフラウンド中に日差しが気になる時

Start here

1ゴルフラウンド中に日差しが気になる時にUnclaimed situation volume 1,816,645No brand shows up in this big situation yet → plant content & ads on its search terms first
2東京観光で日差しが強い時にUnclaimed situation volume 1,450,466No brand shows up in this big situation yet → plant content & ads on its search terms first
3旅行中に日差しが気になる時にUnclaimed situation volume 460,265Low search volume → deprioritize for now (start with the big situations above)

Unclaimed situation = a CEP where no brand was detected in its journeys or SERPs · Unclaimed situation volume = situation volume × (1 − brand fit). Situation volume is a proxy for how often the moment happens, not the volume of category-name searches

02Decision summary

what to do first

Priority	Entry situation (CEP)	Power	Unclaimed situation volume	Brand competition	Next action
1	ゴルフラウンド中に日差しが気になる時に	45.3%	1,816,645	unclaimed	No brand shows up in this big situation yet → plant content & ads on its search terms first evidence weak
2	東京観光で日差しが強い時に	36.2%	1,450,466	unclaimed	No brand shows up in this big situation yet → plant content & ads on its search terms first evidence weak
3	旅行中に日差しが気になる時に possibly seasonal	11.5%	460,265	unclaimed	Low search volume → deprioritize for now (start with the big situations above) evidence weak
4	夏フェスで日差しが強い時に	2.3%	92,600	unclaimed	Low search volume → deprioritize for now (start with the big situations above) evidence weak
5	散歩やウォーキング中に日差しが気になる時に	3.9%	157,733	unclaimed	Low search volume → deprioritize for now (start with the big situations above) evidence weak
6	通学時に日差しが気になる時に	0.4%	16,623	unclaimed	Low search volume → deprioritize for now (start with the big situations above) evidence weak
7	通勤時に日差しが気になる時に	0.3%	12,832	unclaimed	Low search volume → deprioritize for now (start with the big situations above) evidence weak

Click a column header to sort by it · click a CEP name to jump to its detail card

Power = this situation's share of all CEP situation searches (bigger = wider door in) · Brand competition = whether brands show up in this situation's journeys/SERPs · Evidence = strength of the supporting search evidence (strong/medium/weak)

03CEP occurrence over timederived

when it grew and when it fell

Total entry demand (all CEPs)YoY (12M)-7%Last 3M YoY-12%Peak–48 months · CEP 7stablenon-seasonal

Click a row to switch the chart above to that CEP. Click the same row again, or the top row, to return to the total.

CEP	YoY (12M)	Last 3M YoY	Peak		queries
Total entry demand (all CEPs)	-7%	-12%	–	stablenon-seasonal
ゴルフラウンド↗	-11%	-6%	–	stablenon-seasonal	5/5
東京観光↗	-2%	-22%	8	stablenon-seasonal	3/3
旅行中に日差しが気になる時に↗	-7%	-6%	7·8	stableseasonal	4/4
夏フェス↗	-15%	-24%	5·7·8	stableseasonal	3/3
散歩ウォーキング日差し↗	-5%	+6%	–	stablenon-seasonal	3/3
通学時に日差しが気になる時に↗	+9%	+22%	4·7·8	stableseasonal	4/4
通勤日差し↗	+77%	+31%	–	growingnon-seasonal	2/2

Peak calendar: CEP × calendar-month index

	1	2	3	4	5	6	7	8	9	10	11	12
ゴルフラウンド	0.8	0.8	1.0	1.0	1.2	1.1	1.0	1.1	1.0	1.1	1.1	0.8
東京観光	0.9	0.9	1.1	0.9	1.1	0.9	1.0	1.4	1.0	1.0	0.9	0.9
旅行中に日差しが気になる時に	0.8	0.8	0.8	0.8	0.9	1.2	2.1	1.4	0.9	0.9	0.7	0.7
夏フェス	0.6	0.6	0.7	1.0	1.5	1.2	2.1	1.6	1.1	0.7	0.6	0.4
散歩ウォーキング日差し	1.0	0.9	1.0	1.1	1.2	0.9	0.8	1.1	1.0	1.2	1.0	0.8
通学時に日差しが気になる時に	0.7	0.7	1.0	1.4	1.2	1.2	1.7	1.5	0.8	0.7	0.7	0.6
通勤日差し	1.1	1.2	1.3	1.3	1.0	0.9	0.8	0.8	0.9	0.9	0.9	0.9

Cell = that month's average volume ÷ overall monthly average (1.0 = average). Bold = peak month.

Monthly search volume per situation over 48 measured months: when and how often each situation occurs. Combined with survey data, it shows both the size of a situation and how it changes.

Rule: growing/declining = trailing 12-month sum vs the prior 12 months ±20% or more / peak month = calendar-month index ≥1.30 / seasonal = max ÷ min month index ≥1.8. Monthly values are measured monthly search volumes [measured]; CEP series are sums over measured measured queries [derived]. Unlike survey CEP power (share of respondents), this frequency is search occurrence.

04Competitive structurederived

fixed leaders, contested and open situations

fixed leader (0)

–

contested (0)

–

dispersed (0)

–

open (7)

ゴルフラウンド 45.3%
東京観光 36.2%
旅行中に日差しが気になる時に 11.5%
散歩ウォーキング日差し 3.9%
夏フェス 2.3%
通学時に日差しが気になる時に 0.4%
通勤日差し 0.3%

Open has two kinds: measured with no brand mentions is a claim candidate; unmeasured needs a deep run or survey before claiming. · Contested = gap between #1 and #2 ≤10pt · dispersed = leader share <35% · HHI = sum of squared shares. Core CEPs (power ≥ median) are filled, others faded. Unmeasured are dashed (unknown, not empty).

05CEP power

each situation's share of all situation searches

total 7 CEP · [derived] power = the CEP's search volume ÷ total volume of all CEPs

06Brand × entry situation

claimed vs. open (all CEPs)

Brand links measured for 0 of 7 entry situations · the other 7 are graded as opportunities. Small k/m in a cell = brand-mention sample (small m = low confidence).

	Brand link
ゴルフラウンド45.3%	Big land-grab: large unclaimed situation 45.3% · mo. 1,816,645
東京観光36.2%	Big land-grab: large unclaimed situation 36.2% · mo. 1,450,466
旅行中に日差しが気になる時に11.5%	Big land-grab: large unclaimed situation 11.5% · mo. 460,265
散歩ウォーキング日差し3.9%	Big land-grab: large unclaimed situation 3.9% · mo. 157,733
夏フェス2.3%	Big land-grab: large unclaimed situation 2.3% · mo. 92,600
通学時に日差しが気になる時に0.4%	Land-grab: unclaimed 0.4% · mo. 16,623
通勤日差し0.3%	Land-grab: unclaimed 0.3% · mo. 12,832

[derived] · colored cell = measured share (k/m sample) · green/amber row = land-grab opportunity (no brand linked, by situation size) · gray row = small · "·" = not queried (no data ≠ 0)

Discovered category brands (unlinked): ユニクロ, ワークマン, モンベル, 無印, フランフラン, リファ, サンバリア100, コールマン, 東レ, wpc

Brand counting basis: direct brand-name mentions (no product-name mapping).

07Category link rate on search pathsderived

does the situation lead to the category next

ゴルフラウンド 45.3% category-growth candidatelink rate 0% · low · 0/200 paths

東京観光 36.2% category-growth candidatelink rate 0% · low · 0/200 paths

Link rate = share of search paths through this situation whose later queries contain a category term (日傘) [derived]. low <20% · high ≥50%. Power ≥ median and low = category-growth candidate (the situation is big but the category is not chosen yet). Only journeys starting from the situation term are used; the category-anchor journey is excluded (linked by construction). CEPs whose own queries already contain the category term are skipped.

08W-axis mixassumption

where this map's situations cluster

Why · purpose/benefit2 CEPs · power sum 39%

Who · for whom0 CEPs · power sum 0%

When · timing/trigger0 CEPs · power sum 0%

Where · place1 CEPs · power sum 45%

While · doing what0 CEPs · power sum 0%

With whom0 CEPs · power sum 0%

Feeling · mood0 CEPs · power sum 0%

BEP (benefit entry points): 0 / 7

Empty axes: Who · for whom, When · timing/trigger, While · doing what, With whom, Feeling · mood. This is where a candidate list may be trapped in the designer's hypotheses; add entry-situation candidates on these axes in the next run's review step.

The W-axis shows the angle each CEP arises from (why, for whom, when, where, while doing what, with whom, feeling) [assumption]; when scene-7W evidence exists, the most populated axis is used [derived]. BEP = entries where the desired outcome comes first, like why or feeling.

09Audience in the query

Who it is for

Only searches that name an audience (the recipient, companion or user) are counted, and their monthly volume summed. It reads who people search for, and with whom, straight from the queries.

Audience	Top queries	CEP	Monthly volume
Women 女子	女子ゴルフ結果速報リーダーボード · 東京観光女子おすすめ	ゴルフラウンド中に日差しが気になる時に, 東京観光で日差しが強い時に	287,399
Men メンズ · 男性	日傘メンズ · 日傘男性用 · 日傘男性		86,191
Babies ベビー	日傘ベビーカー		1,693

Measured queries × fixed lexicon classification [derived]. A query naming two audiences counts in both.

10Evidence grade

What the qualitative attributes rest on

Nano intents, KBFs, constraints, avoidances, thresholds and reasons to believe carry one of four labels. Measured means the condition exists as real search queries with measured monthly volume. Consensus means Google's SERP and AI Overview state the same thing across several queries. A SERP is ranked and summarized from the click feedback of the people who searched that query, so content that repeats across queries and independent sources is read as what that society agrees on about the topic.

measured 100%

measured1 · exists as search queries, monthly volume measured

consensus0 · repeated in SERP/AIO of 2+ queries (or 3 independent sources)

derived0 · found in a single SERP sentence

assumption0 · interpretation without direct evidence

Based on the 1 qualitative attributes in this report; each counts once, at its strongest evidence.

Conditions with measured search demand measured

Condition	CEP	Top queries	Monthly volume	Trend
KBF遮熱性能	夏フェスで日差しが強い時に	遮熱日傘	13,433	↓-16%

Sum of monthly volume of measured queries that state the attribute's condition: queries containing its key terms verbatim, or queries that say the condition in other words (condition word checked, passed a separate review). Attributes hitting the same query set are merged. Trend uses 48 months of monthly volume (last 12 vs prior 12 months).

11Search data used in this report

A CEP is a situation that many search signals point to together, not a single keyword. This shows how much related-search, search-path, search-cluster, SERP text, PAA and AI Overview data was collected, and which of it served as evidence for each CEP's scene (when, where, why, with whom) and its nano context, KBF, constraints and avoidances.

Data	Collected	Used as CEP evidence
Related searches	250	in the dossiers of 0/7 CEPs
Search paths	695 paths (incl. 0 deep-measurement paths)	in the dossiers of 0/7 CEPs
Search clusters	0 clusters · 0 queries	in the dossiers of 0/7 CEPs
SERPs (Google results)	0	in the dossiers of 0/7 CEPs
AI Overview	0	in the dossiers of 0/7 CEPs
PAA (People also ask)	0	–

Where each CEP showed up

CEPs surface in different places depending on the topic and situation: some in search paths, some in clusters or related searches, some in AIO and PAA. Left: share of the CEP's measured query volume found in each source (discovery). Right: how often each source was cited for the scene 7W and attributes. 'none' means that source never reached this CEP.

CEP	Discovery (volume share)						Cited as evidence (count)
CEP	related searches	search paths	search clusters	AIO	PAA	mined from paths/clusters/AIO	CEP queries	search paths	search clusters	related searches	AIO	PAA	top results
ゴルフラウンド中に日差しが気になる時に	–	99%	–	–	–	–	·	·	·	·	·	·	·
東京観光で日差しが強い時に	–	100%	–	–	–	–	·	·	·	·	·	·	·
旅行中に日差しが気になる時に	–	–	–	–	–	–	·	·	·	·	·	·	·
散歩やウォーキング中に日差しが気になる時に	–	–	–	–	–	–	·	·	·	·	·	·	·
夏フェスで日差しが強い時に	–	–	–	–	–	–	·	·	·	·	·	·	·
通学時に日差しが気になる時に	–	–	–	–	–	–	·	·	·	·	·	·	·

12CEP detail cards

CEP 1purpose CEP

ゴルフラウンド中に日差しが気になる時に

Macroexplicit CEP

power 45.3% derived · avg monthly volume 1,816,645 · trend (3M) +3%

W-axis Wherestructure open YoY (12M) -11%stablenon-seasonalcategory link 0%

The situationassumption

ゴルフ場でプレイ中や女子ゴルフ観戦中に、日差しが気になることがあります。長時間のラウンドや観戦で、強い日差しから身を守り、快適に過ごしたいと感じます。そのため、「ゴルフ」や「ゴルフ場」での日差し対策、特に「晴雨兼用傘ゴルフ」について検索します。

Next actionNo brand shows up in this big situation yet → plant content & ads on its search terms first evidence weak

Investment priority derived

Investment candidate #1Unclaimed situation volume up to 1,816,645/mo

Market size 100 · Brand fit – · Provability 0(evidence thin), highest-priority investment on this 3-axis combination

Combines the 3 selection axes + power/volume via a fixed rule; a new calculation only, no new data

Scenes: micro-CEPs within this situation

女子ゴルフ観戦中に assumption ‹女子ゴルフ結果速報リーダーボード›282,666 · 16% derived

ゴルフ場でプレイ中に assumption ‹ゴルフ場›450,000 · 25% derived

Scene names are [assumption] bonded to quoted evidence · volume is the measured sum of scene-attributed queries [derived] · evidence-less scenes are discarded by code

Search journey: just before and after derived

Adjacent transitions aggregated from 200 measured paths; an aggregate graph flow, not one person's timeline

Show 3 evidence blocksHide evidence

Evidence queries measured

ゴルフ 1,073,333measuredゴルフ場 450,000measured女子ゴルフ結果速報リーダーボード 282,666measuredラウンド 10,633measured晴雨兼用傘ゴルフ 13measuredIKG

Context (7W)

where: ゴルフ場 assumptionwhile: ゴルフ assumption

Selection axes (market · fit · provability)

Market size100

Brand fit–

Provability0

Appendix: AI search exposure

AI tracking questions assumption

1“ゴルフラウンド中に日差しを避けるためのアイテムは？”existing prompt

2“ゴルフ場で日差しが強い時におすすめの対策は？”

3“女子ゴルフ観戦中に日差しを避ける方法は？”

Questions this situation's consumers may ask an AI; used to track AI-response exposure

Reference block needed only when managing AI (AI Overview / chatbot) exposure. The blocks above cover situation understanding and marketing decisions.

CEP 2purpose CEP

東京観光で日差しが強い時に

Macroexplicit CEP

power 36.2% derived · avg monthly volume 1,450,466 · trend (3M) +0%

W-axis Whystructure open YoY (12M) -2% · Peak 8mo.stablenon-seasonalcategory link 0%

The situationassumption

東京観光で日帰りの時や女子旅の時に、日差しが強いと感じることがあります。観光中に強い日差しが気になり、快適に過ごしたいと願います。そのため、「東京観光」での日差し対策について検索します。

Next actionNo brand shows up in this big situation yet → plant content & ads on its search terms first evidence weak

Investment priority derived

Investment candidate #2Unclaimed situation volume up to 1,450,466/mo

Market size 80 · Brand fit – · Provability 0(evidence thin), highest-priority investment on this 3-axis combination

Combines the 3 selection axes + power/volume via a fixed rule; a new calculation only, no new data

Scenes: micro-CEPs within this situation

東京観光で日帰りの時に assumption ‹東京観光日帰り›2,400 · 0% derived

東京観光で女子旅の時に assumption ‹東京観光女子おすすめ›4,733 · 0% derived

Scene names are [assumption] bonded to quoted evidence · volume is the measured sum of scene-attributed queries [derived] · evidence-less scenes are discarded by code

Search journey: just before and after derived

Adjacent transitions aggregated from 200 measured paths; an aggregate graph flow, not one person's timeline

Show 3 evidence blocksHide evidence

Evidence queries measured

観光 1,443,333measured東京観光女子おすすめ 4,733measured東京観光日帰り 2,400measured

Context (7W)

why: 日差しが強い assumptionwhere: 東京 assumptionwhile: 観光 assumption

Selection axes (market · fit · provability)

Market size80

Brand fit–

Provability0

Appendix: AI search exposure

AI tracking questions assumption

1“東京観光で日差しが強い時におすすめのアイテムは？”existing prompt

2“東京で観光する時に日差し対策が必要な場合、何を持っていけばいい？”

3“女子旅で東京観光する時に日差しを避ける方法は？”

Questions this situation's consumers may ask an AI; used to track AI-response exposure

Reference block needed only when managing AI (AI Overview / chatbot) exposure. The blocks above cover situation understanding and marketing decisions.

CEP 3purchase CEP

旅行中に日差しが気になる時に

Macroexplicit CEP

power 11.5% derived · avg monthly volume 460,265 · trend (3M) +193% possibly seasonal

structure open YoY (12M) -7% · Peak 7·8mo.stableseasonal

The situationassumption

旅行中、特に夏休み期間中に日差しが気になることがあります。熱中症予防や紫外線対策をしっかり行い、旅行を存分に楽しみたいと願います。そのため、「旅行」中の「熱中症予防」や「紫外線対策」について検索します。

Next actionLow search volume → deprioritize for now (start with the big situations above) evidence weak

Investment priority derived

Investment candidate #3Unclaimed situation volume up to 460,265/mo

Market size 25 · Brand fit – · Provability 0(evidence thin), highest-priority investment on this 3-axis combination

Combines the 3 selection axes + power/volume via a fixed rule; a new calculation only, no new data

Show 2 evidence blocksHide evidence

Evidence queries measured

旅行 216,000measured夏休み 214,333measured熱中症予防 23,566measured紫外線対策 6,366measured

Selection axes (market · fit · provability)

Market size25

Brand fit–

Provability0

CEP 4purpose CEP

夏フェスで日差しが強い時に

Macroexplicit CEP

power 2.3% derived · avg monthly volume 92,600 · trend (3M) +30%

W-axis Whystructure open YoY (12M) -15% · Peak 5·7·8mo.stableseasonal

The situationassumption

夏フェスや野外ライブに参加する際、日差しが強いと感じることがよくあります。暑さ対策が必要だと感じ、快適にフェスを楽しみたいと願います。そのため、「フェス暑さ対策」や「野外ライブ」での日差し対策について検索します。

Next actionLow search volume → deprioritize for now (start with the big situations above) evidence weak

Investment priority derived

Investment candidate #4Unclaimed situation volume up to 92,600/mo

Market size 5 · Brand fit – · Provability 17(evidence thin), highest-priority investment on this 3-axis combination

Combines the 3 selection axes + power/volume via a fixed rule; a new calculation only, no new data

Scenes: micro-CEPs within this situation

夏フェスでの暑さ対策 assumption ‹フェス…›90,700 · 98% derived

Scene names are [assumption] bonded to quoted evidence · volume is the measured sum of scene-attributed queries [derived] · evidence-less scenes are discarded by code

Message angle assumption

Core message“夏フェスで強い日差しも、遮熱性能で快適に楽しもう。”

Content ideas夏フェス会場での快適さをアピールするショート動画 · フェスファッションに溶け込む遮熱アイテムのスタイリング提案 · 日中のフェスを乗り切るための遮熱グッズ活用術

快適エネルギッシュ楽しいスタイリッシュ

Synthesized from KBF, reasons to believe, emotion & style, and the 6W1H scene

Show 4 evidence blocksHide evidence

Evidence queries measured

フェス 46,500measured暑さ対策 44,200measured野外ライブ 1,900measured

Context (7W)

why: 日差しが強い assumption

KBF (choice criteria)

遮熱性能 ‹search '遮熱日傘' · 13,433/mo›measuredIKG

Selection axes (market · fit · provability)

Market size5

Brand fit–

Provability17

Appendix: AI search exposure

AI tracking questions assumption

1“夏フェスで日差しを避けるためのアイテムは？”existing prompt

2“フェス会場で日差しが強い時におすすめの対策は？”

3“夏フェス参加中に日差しを避ける方法は？”

Questions this situation's consumers may ask an AI; used to track AI-response exposure

Reference block needed only when managing AI (AI Overview / chatbot) exposure. The blocks above cover situation understanding and marketing decisions.

CEP 5purchase CEP

散歩やウォーキング中に日差しが気になる時に

Macroexplicit CEP

power 3.9% derived · avg monthly volume 157,733 · trend (3M) +20%

structure open YoY (12M) -5%stablenon-seasonal

The situationassumption

散歩やウォーキング中、特に夕方でも日差しが気になることがあります。快適に運動を続けたい、日差しによる影響を避けたいと願います。そのため、「散歩」や「ウォーキング」中の日差し対策について検索します。

Next actionLow search volume → deprioritize for now (start with the big situations above) evidence weak

Investment priority derived

Investment candidate #5Unclaimed situation volume up to 157,733/mo

Market size 9 · Brand fit – · Provability 0(evidence thin), highest-priority investment on this 3-axis combination

Combines the 3 selection axes + power/volume via a fixed rule; a new calculation only, no new data

Show 2 evidence blocksHide evidence

Evidence queries measured

散歩 85,000measuredウォーキング 56,833measured夕方 15,900measuredIKG

Selection axes (market · fit · provability)

Market size9

Brand fit–

Provability0

Appendix: AI search exposure

AI tracking questions assumption

1“散歩中に日差しが気になる時に日傘が必要だと感じる”existing prompt

2“ウォーキング中に日焼けを防ぎたい時に”

3“散歩やウォーキング中に紫外線対策が必要な時に”

Questions this situation's consumers may ask an AI; used to track AI-response exposure

Reference block needed only when managing AI (AI Overview / chatbot) exposure. The blocks above cover situation understanding and marketing decisions.

CEP 6purchase CEP

通学時に日差しが気になる時に

Macroexplicit CEP

power 0.4% derived · avg monthly volume 16,623 · trend (3M) -37%

structure open YoY (12M) +9% · Peak 4·7·8mo.stableseasonal

The situationassumption

通学時や登下校中に、日差しが気になることがあります。日焼け防止やシミ対策をしっかり行い、快適に通学したいと願います。そのため、「通学」時の「日焼け防止」や「シミ対策」について検索します。

Next actionLow search volume → deprioritize for now (start with the big situations above) evidence weak

Investment priority derived

Investment candidate #6Unclaimed situation volume up to 16,623/mo

Market size 1 · Brand fit – · Provability 0(evidence thin), highest-priority investment on this 3-axis combination

Combines the 3 selection axes + power/volume via a fixed rule; a new calculation only, no new data

Show 2 evidence blocksHide evidence

Evidence queries measured

通学 10,000measured日焼け防止 2,900measuredシミ対策 2,900measured登下校 823measured

Selection axes (market · fit · provability)

Market size1

Brand fit–

Provability0

CEP 7purchase CEP

通勤時に日差しが気になる時に

Macroexplicit CEP

power 0.3% derived · avg monthly volume 12,832 · trend (3M) -15%

structure open YoY (12M) +77%growingnon-seasonal

The situationassumption

通勤時、特に駅までの道のりで日差しが気になることがあります。毎日の通勤を快適に過ごしたい、日差しによる不快感を避けたいと願います。そのため、「通勤」時の日差し対策について検索します。

Next actionLow search volume → deprioritize for now (start with the big situations above) evidence weak

Investment priority derived

Investment candidate #7Unclaimed situation volume up to 12,832/mo

Market size 1 · Brand fit – · Provability 0(evidence thin), highest-priority investment on this 3-axis combination

Combines the 3 selection axes + power/volume via a fixed rule; a new calculation only, no new data

Show 2 evidence blocksHide evidence

Evidence queries measured

通勤 10,766measured駅まで 2,066measured

Selection axes (market · fit · provability)

Market size1

Brand fit–

Provability0

Appendix: AI search exposure

AI tracking questions assumption

1“通勤時に日差しが気になる時に日傘が必要だと感じる”existing prompt

2“駅までの道で日焼けを防ぎたい時に”

3“通勤中に紫外線対策が必要な時に”

Questions this situation's consumers may ask an AI; used to track AI-response exposure

Reference block needed only when managing AI (AI Overview / chatbot) exposure. The blocks above cover situation understanding and marketing decisions.

13Brand summary

behavioral MMS proxy · width×depth

	MMS proxy [derived]	branded volume [measured]	depth (avg rank)
ユニクロ	–	5,373,333	–
ワークマン	–	3,393,333	–
モンベル	–	941,000	–
無印	–	632,000	–
フランフラン	–	483,333	–
リファ	–	422,666	–
サンバリア100	–	105,333	–
コールマン	–	97,000	–
東レ	–	53,166	–
wpc	–	31,100	–

14Limits

what this map does not say

Any brand share / response-priority judgment ("how a brand is doing") holds only where brand linkage was actually measured for that CEP; check the measured ratio in Evidence readiness just below. Where many CEPs are unmeasured, treat brand-related conclusions as provisional.
Search reflects familiarity and purchase-consideration more strongly than unaided recall; in person-level validation, recognition alone drove search only 1.22x (near-negligible), while familiarity/consideration were clearly larger, reaching 7.0x (smartphones) / 5.3x (autos) when all 5 positive brand attitudes were held (Dotson et al., Google). This correlation is confounded by brand size (bigger brands move on many metrics at once), and validation was done at brand-total level.
Existing/habitual buyers don't disappear from search; the same study found brand owners searched their brand 2.5-3.5x more than non-owners (Dotson et al., Google). But much of that is non-need-expressing (brand-name/navigational/support queries), so this map's need-type CEP distribution skews toward new-entry/switch-consideration demand.
Brand share is relative within the measured set; an unqueried brand x CEP pair is [no data], not zero.
Paths are aggregate graph flow, not one person's timeline; "transition" is a modeled statistic.
CEP power's denominator is classified queries, not people; it isn't directly comparable to survey-based CEP power (denominator = people); the same person's 10 queries count as 10 votes on this map.
Queries that pick by naming a brand/origin are closer to a search mode than a situation, and differ in kind from other CEPs - they're bucketed separately to avoid contaminating need-type CEPs, so when counting "number of entry situations," we recommend excluding this item and the unclassified long tail.
Before decisions like budget/resource allocation, we recommend pairing this with survey-based recall measurement (person-level CEP power / share of mind).
The code discarded 37 AI-suggested item(s) lacking evidence (evidence is enforced).
Of 1 qualitative attributes: 1 measured (100%), 0 consensus (0%), 0 derived, 0 assumption. Measured means the condition exists as search queries, not that it drives purchase. Consensus is the social common sense Google has organized, not a fact check.

Evidence readiness: partial · 7 of 7 CEPs have measured search queries, brand linkage measured for 0; the rest need the co-query matrix / trigger expansion (deep run)

15Reproducibility

Search volume lookup 8 · Search paths 3 · Related searches 1 · AI synthesis 1

Data retrieval detail

Connector calls	Parameters	Credits spent
Related searches	日傘 +3 / jp / limit 250	620
Search paths	日傘 / jp / limit 300	4330
Search volume lookup	12 / jp	12
Search volume lookup	16 / jp	16
Search volume lookup	9 / jp	9
Search paths	観光 / jp / limit 200	5370
Search paths	ゴルフ / jp / limit 200	6570
Search volume lookup	274 / jp	274
Search volume lookup	3 / jp	3
Search volume lookup	80 / jp	80
Search volume lookup	20 / jp	20
Search volume lookup	3 / jp	3
AI synthesis	gemini-2.5-flash (S0 grounded + S3 synthesis)	0

Credits spent: 17307 [measured]

Reused IKG graph: topic '日傘' · readiness EXPAND · 0-day-old snapshot · adopted 3 entry situations / 1 attributes · 0 credits IKG

CEP Finder 2.1 run; input "日傘", depth=quick
Volume is the trailing 3-month average monthly searches [measured]. Power's denominator is the set of queries classified into CEPs.
Brand share uses one method per CEP: brand appearance in search paths, measured brand co-searches, or brand mentions in the situation's search results and AI Overviews; never blended.
A CEP is a unit of "situation/scene," not "person"; from a penetration angle: more entry situations means a wider door for non-customers and light users.
Irrelevance screening: quarantined 1 homonym/other-market keyword(s) from the collection net (e.g. 雨の日)
Reused a stored IKG (Intent Knowledge Graph) graph: topic '日傘', readiness EXPAND, 0-day-old snapshot, run 59a49c37b623 (0 credits). 3 entry-situation and 1 attribute proposals were adopted only after passing this run's measurement and evidence checks (33 dropped); adopted items carry an IKG source tag in the report
7 CEP(s) with unmeasured brand linkage; can be expanded via a deep run

what to do first

when it grew and when it fell

Peak calendar: CEP × calendar-month index

fixed leaders, contested and open situations

each situation&#x27;s share of all situation searches

claimed vs. open (all CEPs)

does the situation lead to the category next

where this map&#x27;s situations cluster

Who it is for

What the qualitative attributes rest on

Where each CEP showed up

ゴルフラウンド中に日差しが気になる時に

東京観光で日差しが強い時に

旅行中に日差しが気になる時に

夏フェスで日差しが強い時に

散歩やウォーキング中に日差しが気になる時に

通学時に日差しが気になる時に

通勤時に日差しが気になる時に

behavioral MMS proxy · width×depth

what this map does not say

CEP Finder at a glance

each situation's share of all situation searches

where this map's situations cluster