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趋势分析技能,分析 Google Trends 数据、社交媒体热度、季节性模式,预测市场趋势
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Based on SOC occupation classification
| name | trend-analysis |
| description | 趋势分析技能,分析 Google Trends 数据、社交媒体热度、季节性模式,预测市场趋势 |
识别和分析市场趋势,帮助判断产品机会的时机和持续性。
获取方式:
关键指标:
Reddit:
X.com:
步骤:
1. 确定核心关键词
- 主关键词: "battery health"
- 相关关键词: "battery anxiety", "battery optimization"
2. 收集 Google Trends 数据
- 时间范围: 最近 12-24 个月
- 地区: 目标市场 (如美国、中国)
3. 分析趋势方向
- 上升趋势: 需求增长,机会增大
- 下降趋势: 需求减少,谨慎进入
- 平稳趋势: 稳定需求,竞争可能激烈
- 波动趋势: 季节性或事件驱动
4. 识别拐点
- 突然上升: 发生了什么事件?
- 突然下降: 问题被解决了吗?
示例分析:
### "iOS battery anxiety" 搜索趋势
**时间范围**: 2024-01 至 2025-01
**趋势方向**: 上升 (+35%)
**关键观察**:
- 2024-09: 搜索量激增 (iOS 18 发布)
- 2024-12: 持续高位 (问题未解决)
- 2025-01: 略有下降但仍高于年初
**结论**:
- 痛点真实且持续存在
- iOS 18 加剧了问题
- 市场需求强劲
**相关查询**:
1. "battery health 96% normal" - 上升 150%
2. "ios 18 battery drain" - 上升 200%
3. "battery replacement cost" - 上升 80%
Reddit 分析:
-- 月度帖子数趋势
SELECT
strftime('%Y-%m', created_at) as month,
COUNT(*) as post_count,
SUM(upvotes) as total_upvotes,
AVG(comment_count) as avg_comments
FROM reddit_posts
WHERE subreddit = 'iPhone'
AND title LIKE '%battery%'
GROUP BY month
ORDER BY month;
X.com 分析:
-- 周度推文量趋势
SELECT
strftime('%Y-W%W', created_at) as week,
COUNT(*) as tweet_count,
SUM(likes) as total_likes,
SUM(retweets) as total_retweets
FROM tweets
WHERE content LIKE '%battery anxiety%'
GROUP BY week
ORDER BY week DESC
LIMIT 12;
热度评分:
热度分数 = (帖子数 × 1) + (Upvotes × 0.1) + (评论数 × 0.5)
分级:
- 低热度: < 100
- 中热度: 100-500
- 高热度: 500-1000
- 极高热度: > 1000
方法:
1. 收集至少 12 个月的数据
2. 按月份聚合
3. 识别重复模式
示例:
- 1月: 新年新手机,电池关注度高
- 9月: iPhone 新品发布,关注度激增
- 12月: 假期购物季,关注度上升
SQL 查询:
-- 按月份统计 (跨年度)
SELECT
CAST(strftime('%m', created_at) AS INTEGER) as month,
AVG(monthly_count) as avg_count
FROM (
SELECT
strftime('%Y-%m', created_at) as year_month,
strftime('%m', created_at) as month,
COUNT(*) as monthly_count
FROM user_quotes
GROUP BY year_month
)
GROUP BY month
ORDER BY month;
可视化:
月份: 1 2 3 4 5 6 7 8 9 10 11 12
热度: ███ ██ ██ ██ ██ ██ ██ ██ ████ ███ ███ ████
简单预测方法:
线性趋势:
- 如果过去 6 个月平均增长 10%/月
- 预测下个月: 当前值 × 1.10
移动平均:
- 3 个月移动平均: (M1 + M2 + M3) / 3
- 用于平滑波动
增长率预测:
- 计算月度增长率
- 取平均值
- 应用到未来月份
SQL 实现:
-- 计算月度增长率
WITH monthly_data AS (
SELECT
strftime('%Y-%m', created_at) as month,
COUNT(*) as count
FROM user_quotes
GROUP BY month
ORDER BY month
)
SELECT
a.month,
a.count as current_count,
b.count as previous_count,
(a.count - b.count) * 100.0 / b.count as growth_rate
FROM monthly_data a
LEFT JOIN monthly_data b ON
DATE(a.month || '-01', '-1 month') = DATE(b.month || '-01')
WHERE b.count IS NOT NULL;
特征:
机会:
风险:
特征:
机会:
风险:
特征:
机会:
风险:
特征:
机会:
风险:
事件类型:
1. 产品发布 (iOS 18)
2. 媒体报道 (科技新闻)
3. 病毒内容 (热门视频/帖子)
4. 竞品动态 (新 App 发布)
5. 政策变化 (App Store 规则)
## iOS 18 发布对电池焦虑的影响
**事件时间**: 2024-09-16
**数据对比**:
| 指标 | 发布前 (8月) | 发布后 (10月) | 变化 |
|------|-------------|--------------|------|
| Google 搜索 | 65 | 92 | +42% |
| Reddit 帖子 | 12 | 35 | +192% |
| X.com 推文 | 45 | 120 | +167% |
**结论**:
- iOS 18 显著加剧了电池焦虑
- 问题在发布后 2 个月仍未解决
- 市场需求被验证
CREATE TABLE market_entries (
id INTEGER PRIMARY KEY,
app_name TEXT,
entry_date TEXT,
exit_date TEXT,
reason TEXT
);
-- 分析市场活跃度
SELECT
strftime('%Y-%m', entry_date) as month,
COUNT(*) as new_entries
FROM market_entries
WHERE exit_date IS NULL
GROUP BY month;
HHI (Herfindahl-Hirschman Index):
= Σ(市场份额²)
解读:
- HHI < 1500: 竞争激烈
- HHI 1500-2500: 中等集中
- HHI > 2500: 高度集中
# [主题] 趋势分析报告
## 执行摘要
- 分析时间: [时间范围]
- 核心发现: [3-5 个关键洞察]
- 趋势方向: 上升/下降/平稳/波动
## 搜索趋势
### Google Trends
- 主关键词趋势: [图表]
- 相关查询: [列表]
- 地区分布: [表格]
## 社交媒体热度
### Reddit
- 帖子数趋势: [图表]
- 热门讨论: [样本]
### X.com
- 推文量趋势: [图表]
- 话题标签: [列表]
## 季节性分析
- 高峰月份: [月份]
- 低谷月份: [月份]
- 模式解释: [原因]
## 事件影响
- 关键事件: [列表]
- 影响分析: [详细]
## 竞争趋势
- 新进入者: [数量]
- 退出者: [数量]
- 市场集中度: [HHI]
## 预测
- 未来 3 个月: [预测]
- 未来 6 个月: [预测]
- 不确定性: [风险]
## 建议
1. [基于趋势的具体建议]
2. [建议 2]
1. 访问 trends.google.com
2. 输入关键词
3. 设置:
- 地区: 目标市场
- 时间: 过去 12 个月
- 类别: 相关类别
4. 导出 CSV
5. 导入到 SQLite 分析
CREATE TABLE trend_data (
id INTEGER PRIMARY KEY,
keyword TEXT,
date TEXT,
search_volume INTEGER,
source TEXT
);
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