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audience-report-jp

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UpdatedJune 17, 2026 at 09:53

Generate DXP audience analysis report via show_widget for Japanese-market clients (English UI). Triggers on Segment ID + month, uploaded audience data files, or multi-segment comparison requests. English output only.

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Audience Report Skill (Japan — English UI)

Generate a comprehensive, visually rich audience analysis report inline in the Chat interface using the Visualizer (show_widget tool).

Language rule: All report content — section titles, card labels, chart axes, tooltip text, persona summaries, insights, and recommendations — must be written in English. This includes any text rendered inside widgets. The audience selector UI for this edition (tagtoo-audiences-jp.html) emits an English trigger phrase, which activates this skill. This skill then calls the MCP with language="en", so all server-side data (user profiles, keyword labels, etc.) is returned in English. Chinese text should not appear in the MCP response or in the report output.

Trigger conditions

This skill activates when ANY of the following conditions are met:

The user provides a Segment ID (e.g., tm:d1028, tm:d_3542_k_140 — starting with tm:) together with a YYYYMM month (e.g., 202602) and mentions "audience", "report", or "segment"
The user uploads audience data files (keyword frequency JSON, consumption JSON, user profile TXT) and asks for analysis
The user asks to compare multiple segments — "compare", "differences", "which audience"

Multiple Segment IDs automatically switch to comparison mode. Output is ALWAYS inline widgets via show_widget — never file-based reports (no docx/pptx/pdf).

Deciding the report mode

Before producing anything, determine which mode to use:

Single-segment mode — the user provides data for one segment. Produce a 7-section deep-dive report (Sections 1–7 below).
Multi-segment comparison mode — the user provides data for 2+ segments (e.g., multiple Segment IDs, multiple file sets, or explicitly asks to "compare"). Produce a comparison report (Sections C1–C5 below).

Signals that indicate multi-segment mode:

Multiple segment names or IDs mentioned in the prompt
Multiple consumption_distribution files with different segment fields
Multiple user profile files for different segments
The user says "比較", "compare", "異同", "differences", "哪個受眾", "which audience"

When in doubt, ask the user to confirm.

Expected input files

Each segment can have up to four types of data. Not all are required.

File type	Format	Description
View item keyword frequency	JSON `{"keyword": weight, ...}`	Browse/view behavior keyword weights (0–100 scale)
Purchase keyword frequency	JSON `{"keyword": weight, ...}`	Actual purchase behavior keyword weights (0–100 scale)
Consumption distribution	JSON with `consumption_distribution` array of `{range, user_count}`	Spending power distribution
User profile description	TXT or JSON	AI-predicted demographics, interests, shopping preferences

For multi-segment mode, the user provides one set of files per segment. File names usually contain the segment name (e.g., Segment_轉址網站_-_超市零售_消費力原始資料.json). Group files by segment before processing.

Section 0: Data fetching via Tagtoo MCP Gateway

When the user provides Segment ID(s) and a month (YYYYMM format) instead of uploading files directly, fetch the report data via the Tagtoo MCP Gateway before proceeding to report generation.

Required user inputs

Input	Format	Example
Segment ID(s)	String, may contain colons and underscores	`tm:d1028`, `tm:d_3542_k_140`
Month	YYYYMM	`202602`

If the user says something like "Show me the audience report for tm:d1028 in February", extract:

Segment ID = tm:d1028
Month = 202602

If multiple Segment IDs are provided, fetch data for each and enter multi-segment comparison mode automatically.

MCP tool

Use the dxp-mcp_get_audience_report tool from the Tagtoo MCP Gateway server. One call per segment returns all four data types (user profile, browse keywords, purchase keywords, consumption distribution) in a single response.

Parameters:

Parameter	Type	Required	Description
`segment_id`	string	Yes	Segment ID (e.g., `tm:d1028`)
`year_month`	string	Yes	Report month in YYYYMM format (e.g., `202604`)
`party_type`	string	No	`first`, `second`, or `insite_second`. If omitted, auto-detects across all three types
`insite_or_outsite`	string	No	Only applies when `party_type` is `first`. Values: `insite` or `outsite`. If omitted, prefers insite; falls back to outsite
`language`	string	No	`zh` (default, Traditional Chinese) or `en` (English)

For this skill, always set language to "en".

Response format

Success (single match):

{
  "party_type": "second",
  "insite_or_outsite": null,
  "report": {
    "user_profile_pic_url": "https://storage.googleapis.com/.../使用者輪廓.png",
    "user_profile_txt": "1. 年齡：25-40歲\n2. 性別：...\n3. 興趣：...\n4. 購物偏好：...",
    "purchase_power_pic_url": "https://storage.googleapis.com/.../消費力分佈.png",
    "view_wordcloud_pic_url": "https://storage.googleapis.com/.../熱門瀏覽商品關鍵字.png",
    "purchase_wordcloud_pic_url": "https://storage.googleapis.com/.../熱門購買商品關鍵字.png",
    "purchase_power_data": {
      "segment": "轉址網站 - 購物狂熱者",
      "location": "",
      "consumption_distribution": [
        {"range": "1000以下", "user_count": 534},
        {"range": "1001~3000", "user_count": 708},
        {"range": "3001~5000", "user_count": 192}
      ]
    },
    "view_freq_data": {
      "露營帳篷": 100.0,
      "DHA魚油": 92.3,
      "日拋隱形眼鏡": 87.1
    },
    "purchase_freq_data": {
      "DHA魚油": 100.0,
      "益生菌": 88.7,
      "膠原蛋白": 82.4
    }
  }
}

report fields reference:

Field	Type	Used by	Description
`user_profile_txt`	string	Section 2	AI-predicted persona text (demographics, interests, shopping preferences)
`user_profile_pic_url`	string	—	Pre-rendered user profile image URL (reference only)
`purchase_power_data`	object	Sections 1, 3, C1, C3	Raw consumption distribution JSON with `segment`, `location`, and `consumption_distribution` array of `{range, user_count}`
`purchase_power_pic_url`	string	—	Pre-rendered spending distribution chart URL (reference only)
`view_freq_data`	object	Sections 1, 4, 6, C1, C4a	Browse keyword frequency: `{"keyword": weight}`, weight 0–100
`purchase_freq_data`	object	Sections 1, 5, 6, C1, C4b	Purchase keyword frequency: `{"keyword": weight}`, weight 0–100
`view_wordcloud_pic_url`	string	—	Pre-rendered browse word cloud image URL (fallback only)
`purchase_wordcloud_pic_url`	string	—	Pre-rendered purchase word cloud image URL (fallback only)

Success (multiple matches):

{
  "matches": [
    {"party_type": "first", "insite_or_outsite": "insite", "report": { ... }},
    {"party_type": "second", "insite_or_outsite": null, "report": { ... }}
  ]
}

When multiple matches are returned, use the one whose party_type matches the user's request. If the user did not specify, prefer second.

Failure:

{
  "error": "查不到 segment tm:d9999 的報告"
}

Report the error to the user and skip sections that depend on missing data.

Important notes

No manual GCS path construction needed. The MCP tool handles bucket routing, party_type detection, and file discovery internally.
party_type auto-detection: If you omit party_type, the tool probes all three types automatically. Only pass it explicitly if the user specifies one.
Large response handling: The report JSON payload can be large. If the MCP tool result is too large for context, it may be saved to a local file path. Use bash_tool with Python to read and process it.
Error handling: If a segment is not found for the given month, the response contains an error field. Report which data is missing and proceed with whatever was successfully fetched.

Using the response

After calling dxp-mcp_get_audience_report, extract the report object from the response. If the response contains a matches array (multiple matches), select the one whose party_type matches the user's request (prefer second if unspecified), then extract its report object.

All subsequent sections reference data fields via the report. prefix — for example, report.view_freq_data means the view_freq_data field inside this report object, report.purchase_power_data.consumption_distribution means the nested consumption_distribution array inside purchase_power_data.

Multi-segment fetching

When multiple Segment IDs are provided, call dxp-mcp_get_audience_report once per segment. Extract the report object from each response independently. Keep each segment's report organized by segment ID. After all data is fetched, determine the report mode (single vs multi-segment) and proceed to the appropriate sections below.

Single-segment mode (Sections 1–7)

Produce the report in this order. Skip sections if the corresponding data is not available. Each section uses one show_widget call, with 1–3 paragraphs of prose analysis between sections (never stack widgets back-to-back). Write all content in English.

Section 1: Report header & KPI dashboard

Purpose: A one-glance summary — which segment, how much data, what scale.

Output: A banner with segment name, plus 3–4 metric cards showing: total browse keywords, total purchase keywords, total consumers, dominant spending range.

Widget layout:

Segment name + icon at the top
CSS grid repeat(4, minmax(0, 1fr)) of metric cards
Each card: muted 13px label + 24px/500 number
Card style: background: var(--color-background-secondary), no border, border-radius: var(--border-radius-md), padding 1rem

Labels to use (English): "Browse Keywords", "Purchase Keywords", "Total Consumers", "Top Spending Range"

Data needed: Count keys in report.view_freq_data and report.purchase_freq_data; sum user_count from report.purchase_power_data.consumption_distribution; find the range with highest user_count.

Section 2: User persona

Purpose: Build empathy — who is this person before we look at any charts?

Output: Prose only (no widget). Summarize demographics, interests, and shopping behavior from the profile file in English. Highlight implications for the segment.

Section 3: Consumption distribution

Purpose: Answer "how much does this audience spend?"

Output: A vertical bar chart (Chart.js) showing user count per spending range.

Widget layout:

Canvas in <div> with height: 320px; position: relative
type: 'bar', tooltip: ctx.parsed.y + ' users'
Custom HTML legend below

Prose after widget: State the dominant range, its share of total, and what this implies for the segment's value. Write in English.

Section 4: Browse interest word cloud

Purpose: Answer "what is this audience interested in?" — a word cloud reveals the overall interest landscape at a glance, with font size encoding keyword weight.

Output: A word cloud of the top 30 browse keywords rendered via show_widget.

Mandatory: This section must be executed immediately after reading the data. The following are strictly forbidden:

❌ Bar charts (horizontal or vertical) ❌ Tables ❌ Bullet lists ❌ Any chart type other than a word cloud

How to produce:

From report.view_freq_data, sort all keywords by weight descending, take top 30
Run the Python template from dxp-audience-report-style.md "Section 4/5: Single Audience Word Cloud Python Template" in bash_tool, using browse parameters: CONTAINER_ID=wc4, TITLE=Browse Keywords Top 30, TITLE_COLOR=#185FA5, BG_COLOR=#f0f6ff, TOOLTIP_LABEL=Browse Score, PR=[55,138,221], LT=[168,205,239]
Render the output HTML string with show_widget

Prose after widget: Interpret what the top keywords collectively reveal about the persona — life stage, aspirations, purchase intent signals. Write in English.

Section 5: Purchase behavior word cloud

Purpose: Answer "what do they actually buy here?" — ground truth of spending.

Output: A word cloud of the top 30 purchase keywords rendered via show_widget.

Mandatory: Same rules as Section 4 — word cloud only, no other chart types.

How to produce: Same flow as Section 4, but using report.purchase_freq_data and purchase parameters: CONTAINER_ID=wc5, TITLE=Purchase Keywords Top 30, TITLE_COLOR=#993C1D, BG_COLOR=#fff5f2, TOOLTIP_LABEL=Purchase Score, PR=[216,90,48], LT=[240,180,154]

Prose after widget: Dominant purchase keywords, notable brands or product types, and what this reveals about actual spending behavior. Write in English.

Section 6: Browse vs Purchase gap analysis

Purpose: Reveal the mismatch between interest and action — the most actionable section, exposing unmet demand and the channel's real role.

Output: Two-column comparison layout + insight callout box.

Widget layout:

Left column header: "Browse Interests" — top 10 browse keywords as colored pills
Center: arrow icon
Right column header: "Actual Purchases" — top 10 purchase keywords as colored pills
Bottom: insight box (background: var(--color-background-warning)) with English insight text

How to produce: From report.view_freq_data take the top 20 browse keywords; from report.purchase_freq_data take the top 20 purchase keywords. Compare the two sets. Identify keywords that appear only in browse (unmet demand / window shopping) and only in purchase (habitual buying). Note any surprising gaps or overlaps.

Section 7: Marketing recommendations

Purpose: Turn insights into executable actions.

Output: 3–4 strategy cards stacked vertically.

Widget layout:

Card style: white bg, 0.5px border, border-radius-lg, padding 1.25rem
Priority badge with semantic bg colors (e.g., "High Priority", "Medium Priority")
Highest-priority card: border: 2px solid var(--color-border-info)

How to produce: Derive from Sections 3–6. Typical types: cross-sell, browse-to- purchase conversion, promotion timing, persona-specific targeting. Write all card content in English.

Multi-segment comparison mode (Sections C1–C5)

When 2+ segments are provided, the report shifts from deep-diving one audience to comparing across audiences. The goal is to answer: how are these segments similar, how are they different, and what does that mean for marketing strategy?

Before starting, process each segment's data independently using the same techniques from single-segment mode (keyword grouping, consumption stats). Store the results in a structured format so they can be compared side by side.

Section C1: Comparison dashboard

Purpose: Show all segments at a glance so the reader immediately sees scale differences and can orient themselves.

Output: A table-like grid where each column is one segment.

Widget layout:

Header row: segment names (one per column)
Metric rows: Browse Keywords, Purchase Keywords, Total Consumers, Top Spending Range — one value per segment per row. All labels in English.
Highlight the highest/lowest value in each row with a subtle accent (background: var(--color-background-info) for highest, background: var(--color-background-danger) for lowest)
If 2 segments: use repeat(2, minmax(0, 1fr))
If 3–4 segments: use repeat(N, minmax(0, 1fr))
If 5+ segments: stack vertically (one card per segment) to avoid cramping

Prose after widget: Summarize the scale differences in English in one paragraph.

Section C2: Persona comparison

Purpose: Put the human profiles side by side so the reader can feel the contrast before seeing data.

Output: A card per segment, each containing the persona summary in English (condensed to 3–4 lines). Use a distinct color accent per segment.

Widget layout:

Cards in a row (same grid as C1)
Each card: color-coded top border (2px solid, using segment's assigned color), white bg, 0.5px border on other sides
Persona text: 13px, secondary color, 1.6 line-height

Prose after widget: Call out the key persona differences — age, gender, interests, life stage in English. Are these fundamentally different people or variations of the same profile?

Section C3: Consumption comparison

Purpose: Answer "which segment spends more, and how does the spending shape differ?"

Output: A grouped or stacked bar chart (Chart.js) with one color per segment, showing user count per spending range.

Widget layout:

type: 'bar' with multiple datasets, one per segment
Each dataset gets its own color from the segment color assignments
Group mode: options.scales.x.stacked = false (side-by-side bars)
Canvas wrapper height: 360px
Custom legend showing segment name + color square. All labels in English.

Prose after widget: Compare dominant ranges, overall spending power, and whether one segment skews higher or lower. Write in English.

Section C4: Interest & purchase overlap analysis

Purpose: The core comparison — where do these audiences overlap in what they browse and buy, and where do they diverge? This reveals whether the segments can be targeted with the same campaigns or need distinct strategies.

Output: Two widgets in sequence (with prose between them):

Widget C4a — Browse interest comparison: Side-by-side word clouds, top 30 browse keywords per segment. Mandatory — must use word clouds, no other chart type allowed.

How to build:

For each segment, from report.view_freq_data, sort by weight descending, take top 30
Use the HTML template from dxp-audience-report-style.md "Section C4: Side-by-Side Word Clouds" — one word cloud column per segment in a CSS grid
Each segment uses its assigned color ramp (Segment 1: Blue pr/lt, Segment 2: Coral pr/lt)
Tooltip label: Browse Score
For 3+ segments, expand the grid to N columns

Prose between C4a and C4b: Note which keywords appear in multiple segments (shared interest) vs which are unique to one segment (differentiators). Write in English.

Widget C4b — Purchase comparison: Same structure as C4a, applied to purchase data. Top 30 purchase keywords per segment, rendered as side-by-side word clouds. Mandatory.

How to build:

For each segment, from report.purchase_freq_data, sort by weight descending, take top 30
Same template as C4a, change tooltip to Purchase Score and title to Purchase Keywords Comparison Top 30

Prose after C4b: Identify: shared purchase keywords (target together), exclusive keywords (differentiate campaigns), and any surprising mismatches between browse and purchase across segments. Write in English.

Section C5: Comparative recommendations

Purpose: Actionable strategies that leverage the differences between segments.

Output: Strategy cards, but now each card specifies which segment(s) it applies to. All card content in English.

Widget layout: Same as Section 7, but each card includes an "Applies to:" line with segment name badges.

How to produce recommendations:

Shared strengths: categories strong across all segments → unified campaigns
Unique opportunities: categories strong in one segment only → targeted campaigns
Gap arbitrage: a category that Segment A browses and Segment B buys → cross- promote between segments, or tailor the conversion strategy per segment
Budget allocation: if one segment has higher consumption, recommend proportional ad spend; if one segment has better browse-to-purchase alignment, recommend efficiency plays

Data processing

The MCP response report.view_freq_data and report.purchase_freq_data are both {"keyword": weight} objects. Sections 4, 5, C4a, and C4b all use the same pattern — sort by weight, take top 30:

# data = report.view_freq_data or report.purchase_freq_data
# (already a dict, no file I/O needed)

top30 = sorted(data.items(), key=lambda x: -x[1])[:30]

for keyword, weight in top30:
    print(f'{keyword}: {weight:.1f}')

For multi-segment mode, run this once per segment and collect results:

# data_a = segment A's report.view_freq_data
# data_b = segment B's report.view_freq_data
segment_tops = {
    'seg_a': sorted(data_a.items(), key=lambda x: -x[1])[:30],
    'seg_b': sorted(data_b.items(), key=lambda x: -x[1])[:30],
}

lookup = {seg: dict(tops) for seg, tops in segment_tops.items()}
all_keywords = sorted(set(k for tops in segment_tops.values() for k, _ in tops))
for kw in all_keywords:
    print(kw, {seg: lookup[seg].get(kw, 0) for seg in segment_tops})

No keyword translation: Display keywords as-is from the data. Do not translate individual keywords or assign them to named categories.

Segment color assignments (multi-segment mode)

In comparison mode, each segment gets a dedicated color used across all charts and cards. Assign in order of appearance:

Segment index	Ramp	Primary (400)	Light fill (50)
Segment 1	Blue	#378ADD	#E6F1FB
Segment 2	Coral	#D85A30	#FAECE7
Segment 3	Teal	#1D9E75	#E1F5EE
Segment 4	Purple	#7F77DD	#EEEDFE
Segment 5	Amber	#854F0B	#FAEEDA

These are for identifying segments. Within a single segment's charts (e.g., the browse interest cards), use the full 9-ramp category palette below.

Category color palette (single-segment mode)

Used by the word cloud renderer in Sections 4, 5, C4a, and C4b. Each word cloud interpolates between its light (lt) and primary (pr) color based on keyword weight.

Index	Ramp	Dot/bar (400)	Card fill (50)	Card title (800)	Card subtitle (600)
0	Blue	#378ADD	#E6F1FB	#0C447C	#185FA5
1	Coral	#D85A30	#FAECE7	#712B13	#993C1D
2	Pink	#D4537E	#FBEAF0	#72243E	#993556
3	Teal	#1D9E75	#E1F5EE	#085041	#0F6E56
4	Amber	#854F0B	#FAEEDA	#633806	#854F0B
5	Green	#639922	#EAF3DE	#27500A	#3B6D11
6	Purple	#7F77DD	#EEEDFE	#3C3489	#534AB7
7	Red	#E24B4A	#FCEBEB	#791F1F	#A32D2D
8	Gray	#888780	#F1EFE8	#444441	#5F5E5A

Chart.js common setup

Dark mode: const isDark = matchMedia('(prefers-color-scheme: dark)').matches
Ticks: isDark ? '#c2c0b6' : '#5f5e5a'; Grid: isDark ? 'rgba(255,255,255,0.06)' : 'rgba(0,0,0,0.06)'
Disable default legend; build custom HTML with 10px color squares
Canvas in <div> with explicit height and position: relative
Load: <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/4.4.1/chart.umd.js">
Round all numbers; canvas cannot resolve CSS variables — use hardcoded hex

After the report

End with a structural summary in English listing all sections produced, then offer next steps:

Export to PPTX or PDF
Extract categorization rules into a reusable mapping table
(Multi-segment mode) Drill down into any single segment for a full deep-dive report

name	audience-report-jp
description	Generate DXP audience analysis report via show_widget for Japanese-market clients (English UI). Triggers on Segment ID + month, uploaded audience data files, or multi-segment comparison requests. English output only.

Audience Report Skill (Japan — English UI)

Generate a comprehensive, visually rich audience analysis report inline in the Chat interface using the Visualizer (show_widget tool).

Trigger conditions

This skill activates when ANY of the following conditions are met:

The user provides a Segment ID (e.g., tm:d1028, tm:d_3542_k_140 — starting with tm:) together with a YYYYMM month (e.g., 202602) and mentions "audience", "report", or "segment"
The user uploads audience data files (keyword frequency JSON, consumption JSON, user profile TXT) and asks for analysis
The user asks to compare multiple segments — "compare", "differences", "which audience"

Multiple Segment IDs automatically switch to comparison mode. Output is ALWAYS inline widgets via show_widget — never file-based reports (no docx/pptx/pdf).

Deciding the report mode

Before producing anything, determine which mode to use:

Single-segment mode — the user provides data for one segment. Produce a 7-section deep-dive report (Sections 1–7 below).
Multi-segment comparison mode — the user provides data for 2+ segments (e.g., multiple Segment IDs, multiple file sets, or explicitly asks to "compare"). Produce a comparison report (Sections C1–C5 below).

Signals that indicate multi-segment mode:

Multiple segment names or IDs mentioned in the prompt
Multiple consumption_distribution files with different segment fields
Multiple user profile files for different segments
The user says "比較", "compare", "異同", "differences", "哪個受眾", "which audience"

When in doubt, ask the user to confirm.

Expected input files

Each segment can have up to four types of data. Not all are required.

File type	Format	Description
View item keyword frequency	JSON `{"keyword": weight, ...}`	Browse/view behavior keyword weights (0–100 scale)
Purchase keyword frequency	JSON `{"keyword": weight, ...}`	Actual purchase behavior keyword weights (0–100 scale)
Consumption distribution	JSON with `consumption_distribution` array of `{range, user_count}`	Spending power distribution
User profile description	TXT or JSON	AI-predicted demographics, interests, shopping preferences

Section 0: Data fetching via Tagtoo MCP Gateway

Required user inputs

Input	Format	Example
Segment ID(s)	String, may contain colons and underscores	`tm:d1028`, `tm:d_3542_k_140`
Month	YYYYMM	`202602`

If the user says something like "Show me the audience report for tm:d1028 in February", extract:

Segment ID = tm:d1028
Month = 202602

If multiple Segment IDs are provided, fetch data for each and enter multi-segment comparison mode automatically.

MCP tool

Parameters:

Parameter	Type	Required	Description
`segment_id`	string	Yes	Segment ID (e.g., `tm:d1028`)
`year_month`	string	Yes	Report month in YYYYMM format (e.g., `202604`)
`party_type`	string	No	`first`, `second`, or `insite_second`. If omitted, auto-detects across all three types
`insite_or_outsite`	string	No	Only applies when `party_type` is `first`. Values: `insite` or `outsite`. If omitted, prefers insite; falls back to outsite
`language`	string	No	`zh` (default, Traditional Chinese) or `en` (English)

For this skill, always set language to "en".

Response format

Success (single match):

{
  "party_type": "second",
  "insite_or_outsite": null,
  "report": {
    "user_profile_pic_url": "https://storage.googleapis.com/.../使用者輪廓.png",
    "user_profile_txt": "1. 年齡：25-40歲\n2. 性別：...\n3. 興趣：...\n4. 購物偏好：...",
    "purchase_power_pic_url": "https://storage.googleapis.com/.../消費力分佈.png",
    "view_wordcloud_pic_url": "https://storage.googleapis.com/.../熱門瀏覽商品關鍵字.png",
    "purchase_wordcloud_pic_url": "https://storage.googleapis.com/.../熱門購買商品關鍵字.png",
    "purchase_power_data": {
      "segment": "轉址網站 - 購物狂熱者",
      "location": "",
      "consumption_distribution": [
        {"range": "1000以下", "user_count": 534},
        {"range": "1001~3000", "user_count": 708},
        {"range": "3001~5000", "user_count": 192}
      ]
    },
    "view_freq_data": {
      "露營帳篷": 100.0,
      "DHA魚油": 92.3,
      "日拋隱形眼鏡": 87.1
    },
    "purchase_freq_data": {
      "DHA魚油": 100.0,
      "益生菌": 88.7,
      "膠原蛋白": 82.4
    }
  }
}

report fields reference:

Field	Type	Used by	Description
`user_profile_txt`	string	Section 2	AI-predicted persona text (demographics, interests, shopping preferences)
`user_profile_pic_url`	string	—	Pre-rendered user profile image URL (reference only)
`purchase_power_data`	object	Sections 1, 3, C1, C3	Raw consumption distribution JSON with `segment`, `location`, and `consumption_distribution` array of `{range, user_count}`
`purchase_power_pic_url`	string	—	Pre-rendered spending distribution chart URL (reference only)
`view_freq_data`	object	Sections 1, 4, 6, C1, C4a	Browse keyword frequency: `{"keyword": weight}`, weight 0–100
`purchase_freq_data`	object	Sections 1, 5, 6, C1, C4b	Purchase keyword frequency: `{"keyword": weight}`, weight 0–100
`view_wordcloud_pic_url`	string	—	Pre-rendered browse word cloud image URL (fallback only)
`purchase_wordcloud_pic_url`	string	—	Pre-rendered purchase word cloud image URL (fallback only)

Success (multiple matches):

{
  "matches": [
    {"party_type": "first", "insite_or_outsite": "insite", "report": { ... }},
    {"party_type": "second", "insite_or_outsite": null, "report": { ... }}
  ]
}

When multiple matches are returned, use the one whose party_type matches the user's request. If the user did not specify, prefer second.

Failure:

{
  "error": "查不到 segment tm:d9999 的報告"
}

Report the error to the user and skip sections that depend on missing data.

Important notes

No manual GCS path construction needed. The MCP tool handles bucket routing, party_type detection, and file discovery internally.
party_type auto-detection: If you omit party_type, the tool probes all three types automatically. Only pass it explicitly if the user specifies one.
Large response handling: The report JSON payload can be large. If the MCP tool result is too large for context, it may be saved to a local file path. Use bash_tool with Python to read and process it.
Error handling: If a segment is not found for the given month, the response contains an error field. Report which data is missing and proceed with whatever was successfully fetched.

Using the response

Multi-segment fetching

Single-segment mode (Sections 1–7)

Section 1: Report header & KPI dashboard

Purpose: A one-glance summary — which segment, how much data, what scale.

Output: A banner with segment name, plus 3–4 metric cards showing: total browse keywords, total purchase keywords, total consumers, dominant spending range.

Widget layout:

Segment name + icon at the top
CSS grid repeat(4, minmax(0, 1fr)) of metric cards
Each card: muted 13px label + 24px/500 number
Card style: background: var(--color-background-secondary), no border, border-radius: var(--border-radius-md), padding 1rem

Labels to use (English): "Browse Keywords", "Purchase Keywords", "Total Consumers", "Top Spending Range"

Section 2: User persona

Purpose: Build empathy — who is this person before we look at any charts?

Output: Prose only (no widget). Summarize demographics, interests, and shopping behavior from the profile file in English. Highlight implications for the segment.

Section 3: Consumption distribution

Purpose: Answer "how much does this audience spend?"

Output: A vertical bar chart (Chart.js) showing user count per spending range.

Widget layout:

Canvas in <div> with height: 320px; position: relative
type: 'bar', tooltip: ctx.parsed.y + ' users'
Custom HTML legend below

Prose after widget: State the dominant range, its share of total, and what this implies for the segment's value. Write in English.

Section 4: Browse interest word cloud

Purpose: Answer "what is this audience interested in?" — a word cloud reveals the overall interest landscape at a glance, with font size encoding keyword weight.

Output: A word cloud of the top 30 browse keywords rendered via show_widget.

Mandatory: This section must be executed immediately after reading the data. The following are strictly forbidden:

❌ Bar charts (horizontal or vertical) ❌ Tables ❌ Bullet lists ❌ Any chart type other than a word cloud

How to produce:

From report.view_freq_data, sort all keywords by weight descending, take top 30
Run the Python template from dxp-audience-report-style.md "Section 4/5: Single Audience Word Cloud Python Template" in bash_tool, using browse parameters: CONTAINER_ID=wc4, TITLE=Browse Keywords Top 30, TITLE_COLOR=#185FA5, BG_COLOR=#f0f6ff, TOOLTIP_LABEL=Browse Score, PR=[55,138,221], LT=[168,205,239]
Render the output HTML string with show_widget

Prose after widget: Interpret what the top keywords collectively reveal about the persona — life stage, aspirations, purchase intent signals. Write in English.

Section 5: Purchase behavior word cloud

Purpose: Answer "what do they actually buy here?" — ground truth of spending.

Output: A word cloud of the top 30 purchase keywords rendered via show_widget.

Mandatory: Same rules as Section 4 — word cloud only, no other chart types.

Prose after widget: Dominant purchase keywords, notable brands or product types, and what this reveals about actual spending behavior. Write in English.

Section 6: Browse vs Purchase gap analysis

Purpose: Reveal the mismatch between interest and action — the most actionable section, exposing unmet demand and the channel's real role.

Output: Two-column comparison layout + insight callout box.

Widget layout:

Left column header: "Browse Interests" — top 10 browse keywords as colored pills
Center: arrow icon
Right column header: "Actual Purchases" — top 10 purchase keywords as colored pills
Bottom: insight box (background: var(--color-background-warning)) with English insight text

Section 7: Marketing recommendations

Purpose: Turn insights into executable actions.

Output: 3–4 strategy cards stacked vertically.

Widget layout:

Card style: white bg, 0.5px border, border-radius-lg, padding 1.25rem
Priority badge with semantic bg colors (e.g., "High Priority", "Medium Priority")
Highest-priority card: border: 2px solid var(--color-border-info)

How to produce: Derive from Sections 3–6. Typical types: cross-sell, browse-to- purchase conversion, promotion timing, persona-specific targeting. Write all card content in English.

Multi-segment comparison mode (Sections C1–C5)

Section C1: Comparison dashboard

Purpose: Show all segments at a glance so the reader immediately sees scale differences and can orient themselves.

Output: A table-like grid where each column is one segment.

Widget layout:

Header row: segment names (one per column)
Metric rows: Browse Keywords, Purchase Keywords, Total Consumers, Top Spending Range — one value per segment per row. All labels in English.
Highlight the highest/lowest value in each row with a subtle accent (background: var(--color-background-info) for highest, background: var(--color-background-danger) for lowest)
If 2 segments: use repeat(2, minmax(0, 1fr))
If 3–4 segments: use repeat(N, minmax(0, 1fr))
If 5+ segments: stack vertically (one card per segment) to avoid cramping

Prose after widget: Summarize the scale differences in English in one paragraph.

Section C2: Persona comparison

Purpose: Put the human profiles side by side so the reader can feel the contrast before seeing data.

Output: A card per segment, each containing the persona summary in English (condensed to 3–4 lines). Use a distinct color accent per segment.

Widget layout:

Cards in a row (same grid as C1)
Each card: color-coded top border (2px solid, using segment's assigned color), white bg, 0.5px border on other sides
Persona text: 13px, secondary color, 1.6 line-height

Prose after widget: Call out the key persona differences — age, gender, interests, life stage in English. Are these fundamentally different people or variations of the same profile?

Section C3: Consumption comparison

Purpose: Answer "which segment spends more, and how does the spending shape differ?"

Output: A grouped or stacked bar chart (Chart.js) with one color per segment, showing user count per spending range.

Widget layout:

type: 'bar' with multiple datasets, one per segment
Each dataset gets its own color from the segment color assignments
Group mode: options.scales.x.stacked = false (side-by-side bars)
Canvas wrapper height: 360px
Custom legend showing segment name + color square. All labels in English.

Prose after widget: Compare dominant ranges, overall spending power, and whether one segment skews higher or lower. Write in English.

Section C4: Interest & purchase overlap analysis

Output: Two widgets in sequence (with prose between them):

Widget C4a — Browse interest comparison: Side-by-side word clouds, top 30 browse keywords per segment. Mandatory — must use word clouds, no other chart type allowed.

How to build:

For each segment, from report.view_freq_data, sort by weight descending, take top 30
Use the HTML template from dxp-audience-report-style.md "Section C4: Side-by-Side Word Clouds" — one word cloud column per segment in a CSS grid
Each segment uses its assigned color ramp (Segment 1: Blue pr/lt, Segment 2: Coral pr/lt)
Tooltip label: Browse Score
For 3+ segments, expand the grid to N columns

Prose between C4a and C4b: Note which keywords appear in multiple segments (shared interest) vs which are unique to one segment (differentiators). Write in English.

Widget C4b — Purchase comparison: Same structure as C4a, applied to purchase data. Top 30 purchase keywords per segment, rendered as side-by-side word clouds. Mandatory.

How to build:

For each segment, from report.purchase_freq_data, sort by weight descending, take top 30
Same template as C4a, change tooltip to Purchase Score and title to Purchase Keywords Comparison Top 30

Section C5: Comparative recommendations

Purpose: Actionable strategies that leverage the differences between segments.

Output: Strategy cards, but now each card specifies which segment(s) it applies to. All card content in English.

Widget layout: Same as Section 7, but each card includes an "Applies to:" line with segment name badges.

How to produce recommendations:

Shared strengths: categories strong across all segments → unified campaigns
Unique opportunities: categories strong in one segment only → targeted campaigns
Gap arbitrage: a category that Segment A browses and Segment B buys → cross- promote between segments, or tailor the conversion strategy per segment
Budget allocation: if one segment has higher consumption, recommend proportional ad spend; if one segment has better browse-to-purchase alignment, recommend efficiency plays

Data processing

# data = report.view_freq_data or report.purchase_freq_data
# (already a dict, no file I/O needed)

top30 = sorted(data.items(), key=lambda x: -x[1])[:30]

for keyword, weight in top30:
    print(f'{keyword}: {weight:.1f}')

For multi-segment mode, run this once per segment and collect results:

# data_a = segment A's report.view_freq_data
# data_b = segment B's report.view_freq_data
segment_tops = {
    'seg_a': sorted(data_a.items(), key=lambda x: -x[1])[:30],
    'seg_b': sorted(data_b.items(), key=lambda x: -x[1])[:30],
}

lookup = {seg: dict(tops) for seg, tops in segment_tops.items()}
all_keywords = sorted(set(k for tops in segment_tops.values() for k, _ in tops))
for kw in all_keywords:
    print(kw, {seg: lookup[seg].get(kw, 0) for seg in segment_tops})

No keyword translation: Display keywords as-is from the data. Do not translate individual keywords or assign them to named categories.

Segment color assignments (multi-segment mode)

In comparison mode, each segment gets a dedicated color used across all charts and cards. Assign in order of appearance:

Segment index	Ramp	Primary (400)	Light fill (50)
Segment 1	Blue	#378ADD	#E6F1FB
Segment 2	Coral	#D85A30	#FAECE7
Segment 3	Teal	#1D9E75	#E1F5EE
Segment 4	Purple	#7F77DD	#EEEDFE
Segment 5	Amber	#854F0B	#FAEEDA

These are for identifying segments. Within a single segment's charts (e.g., the browse interest cards), use the full 9-ramp category palette below.

Category color palette (single-segment mode)

Used by the word cloud renderer in Sections 4, 5, C4a, and C4b. Each word cloud interpolates between its light (lt) and primary (pr) color based on keyword weight.

Index	Ramp	Dot/bar (400)	Card fill (50)	Card title (800)	Card subtitle (600)
0	Blue	#378ADD	#E6F1FB	#0C447C	#185FA5
1	Coral	#D85A30	#FAECE7	#712B13	#993C1D
2	Pink	#D4537E	#FBEAF0	#72243E	#993556
3	Teal	#1D9E75	#E1F5EE	#085041	#0F6E56
4	Amber	#854F0B	#FAEEDA	#633806	#854F0B
5	Green	#639922	#EAF3DE	#27500A	#3B6D11
6	Purple	#7F77DD	#EEEDFE	#3C3489	#534AB7
7	Red	#E24B4A	#FCEBEB	#791F1F	#A32D2D
8	Gray	#888780	#F1EFE8	#444441	#5F5E5A

Chart.js common setup

Dark mode: const isDark = matchMedia('(prefers-color-scheme: dark)').matches
Ticks: isDark ? '#c2c0b6' : '#5f5e5a'; Grid: isDark ? 'rgba(255,255,255,0.06)' : 'rgba(0,0,0,0.06)'
Disable default legend; build custom HTML with 10px color squares
Canvas in <div> with explicit height and position: relative
Load: <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/4.4.1/chart.umd.js">
Round all numbers; canvas cannot resolve CSS variables — use hardcoded hex

After the report

End with a structural summary in English listing all sections produced, then offer next steps:

Export to PPTX or PDF
Extract categorization rules into a reusable mapping table
(Multi-segment mode) Drill down into any single segment for a full deep-dive report

audience-report-jp

More from this repository

More from this repository

Audience Report Skill (Japan — English UI)

Trigger conditions

Deciding the report mode

Expected input files

Section 0: Data fetching via Tagtoo MCP Gateway

Required user inputs

MCP tool

Response format

Important notes

Using the response

Multi-segment fetching

Single-segment mode (Sections 1–7)

Section 1: Report header & KPI dashboard

Section 2: User persona

Section 3: Consumption distribution

Section 4: Browse interest word cloud

Section 5: Purchase behavior word cloud

Section 6: Browse vs Purchase gap analysis

Section 7: Marketing recommendations

Multi-segment comparison mode (Sections C1–C5)

Section C1: Comparison dashboard

Section C2: Persona comparison

Section C3: Consumption comparison

Section C4: Interest & purchase overlap analysis

Section C5: Comparative recommendations

Data processing

Segment color assignments (multi-segment mode)

Category color palette (single-segment mode)

Chart.js common setup

After the report

Audience Report Skill (Japan — English UI)

Trigger conditions

Deciding the report mode

Expected input files

Section 0: Data fetching via Tagtoo MCP Gateway

Required user inputs

MCP tool

Response format

Important notes

Using the response

Multi-segment fetching

Single-segment mode (Sections 1–7)

Section 1: Report header & KPI dashboard

Section 2: User persona

Section 3: Consumption distribution

Section 4: Browse interest word cloud

Section 5: Purchase behavior word cloud

Section 6: Browse vs Purchase gap analysis

Section 7: Marketing recommendations

Multi-segment comparison mode (Sections C1–C5)

Section C1: Comparison dashboard

Section C2: Persona comparison

Section C3: Consumption comparison

Section C4: Interest & purchase overlap analysis

Section C5: Comparative recommendations

Data processing

Segment color assignments (multi-segment mode)

Category color palette (single-segment mode)

Chart.js common setup

After the report