基于提供的数据,我们可以从以下维度进行核心分析:
品牌集中度:
多渠道投放:
类目偏好:
运营效率:
计算TOP3品牌小商店(完美日记、彩棠和小奥汀)销售额占比。
# 示例代码
top_brands_sales = {
"perfectdiary": 25000,
"cettia": 18000,
"aoatingofficial": 15000
}
total_sales = sum(top_brands_sales.values())
# 计算占比
top3_brand_ratio = {brand: (sales / total_sales) * 100 for brand, sales in top_brands_sales.items()}
print(top3_brand_ratio)
比较关联达人/直播/视频数量差异。
# 示例数据
multi_channels = {
"perfectdiary": {"influencers": 10, "live_streams": 5, "videos": 8},
"cettia": {"influencers": 7, "live_streams": 4, "videos": 6},
"aoatingofficial": {"influencers": 9, "live_streams": 3, "videos": 7}
}
# 比较数量差异
for brand, channels in multi_channels.items():
print(f"{brand} - Influencers: {channels['influencers']}, Live Streams: {channels['live_streams']}, Videos: {channels['videos']}")
分析官方小店中热门带货类目的分布情况。
# 示例数据
category_distribution = {
"perfectdiary": {"Makeup": 40, "Skincare": 25, "Nail Care": 15},
"cettia": {"Makeup": 35, "Skincare": 30, "Body Care": 18},
"aoatingofficial": {"Makeup": 45, "Skincare": 20, "Hair Care": 10}
}
# 分析热门类目
for brand, categories in category_distribution.items():
print(f"{brand} - Popular Categories: {categories}")
评估动销商品数与直播/视频投放的联动表现。
# 示例数据
efficiency_data = {
"perfectdiary": {"sold_items": 500, "live_streams": 8, "video_views": 10000},
"cettia": {"sold_items": 400, "live_streams": 6, "video_views": 7500},
"aoatingofficial": {"sold_items": 350, "live_streams": 4, "video_views": 5000}
}
# 计算联动效果
for brand, data in efficiency_data.items():
print(f"{brand} - Sold Items: {data['sold_items']}, Live Streams: {data['live_streams']}, Video Views: {data['video_views']}")
通过以上分析步骤,可以得到关于品牌集中度、多渠道投放情况、类目偏好和运营效率的具体数据。这些信息有助于进一步优化品牌的营销策略和资源配置。
以上分析数据来源:互联岛