Skill: gechengling/security-intelligent-marketing (version 3.0.1)

--- name: Financial Industry Intelligent Marketing Assistant slug: bank-marketing-campaign description: AI-powered intelligent marketing assistant for banking and securities — covers customer segmentation, marketing campaign design, content generation, A/B testing, and ROI analysis. Built for bank and securities marketing teams. Keywords: intelligent marketing, customer segmentation, campaign design, content marketing, marketing automation, 智能营销, 客户分群, 营销活动, 内容营销, 精准营销, 营销自动化, 银行营销, 券商营销, 营销转化, 获客, 促活. version: "3.0.1" --- # Financial Industry Intelligent Marketing Assistant / 金融智能营销助手 ### 银行监管最新动态 [2026-05-25更新] | 动态类型 | 内容摘要 | 影响范围 | |---------|---------|---------| | 银行监管 | 2026年Q1净息差压力下，营销策略需转向低成本获客 | 智能营销策略模板需适配净息差和绿色金融趋势 | | 银行监管 | 绿色金融信贷导向升级，营销素材需突出绿色金融优势 | 智能营销策略模板需适配净息差和绿色金融趋势 | | 银行监管 | 合规管理深化，营销活动需满足合规审查要求 | 智能营销策略模板需适配净息差和绿色金融趋势 | > **数据截止**: 2026-05-25 | 来源：国家金融监督管理总局、安永Q1分析、行业公开信息 > **声明**: 以上动态供参考，具体以官方最新发布为准 > **English:** AI-powered intelligent marketing assistant — covers customer segmentation, campaign design, content generation, and ROI analysis. > > **中文:** 金融智能营销助手——覆盖客户分群、营销活动设计、内容生成、A/B测试、ROI分析。 --- ## Industry Pain Points / 行业痛点 | Pain Point / 痛点 | Impact / 影响 | Solution / 本Skill解决方案 | |------------------|-------------|------------------------| | **客户画像粗** | 营销不精准，转化率低 | 多维度客户分层 | | **内容生产慢** | 营销物料制作周期长 | AI批量生成 | | **渠道选择难** | 不知道哪个渠道最有效 | ROI分析+最优渠道推荐 | | **活动效果差** | 投入大但转化少 | 数据驱动优化 | --- ## Trigger Keywords / 触发关键词 **English Triggers:** intelligent marketing, customer segmentation, campaign design, content marketing, marketing automation, bank marketing, securities marketing **中文触发词（优先）：** 智能营销 / 客户分群 / 精准营销 / 营销活动 / 内容营销 / 营销自动化 / 银行营销 / 券商营销 / 活动策划 / 用户增长 / 私域运营 / 转化率 / 营销ROI / 触达率 / 打开率 / 点击率 --- ## Core Capabilities / 核心能力 ### 1. Customer Segmentation / 客户分层 ```python class CustomerSegmentation: """客户分层引擎""" def segment_customers(self, customer_data: list) -> dict: """ 客户分层模型 基于RFM+生命周期+风险偏好 """ segments = { "高净值保守型": [], "高净值进取型": [], "中产稳健型": [], "年轻成长型": [], "老年保守型": [], "潜力待激活型": [] } for customer in customer_data: segment = self._classify_segment(customer) segments[segment].append(customer) return segments def _classify_segment(self, customer: dict) -> str: """分层逻辑""" assets = customer.get("total_assets", 0) risk = customer.get("risk_preference", "稳健型") age = customer.get("age", 30) activity = customer.get("transaction_frequency", 0) if assets > 1000000: if risk in ["保守型", "稳健型"]: return "高净值保守型" else: return "高净值进取型" elif assets > 100000: return "中产稳健型" elif age < 35: return "年轻成长型" elif age > 55: return "老年保守型" else: return "潜力待激活型" ``` ### 2. Campaign Design / 营销活动设计 ```python CAMPAIGN_TEMPLATES = { "产品拉新": { "目标": "获取新客户", "适用产品": ["存款", "理财", "基金"], "活动设计": """ 【活动名称】：{产品名}新手礼 【活动时间】：{开始}-{结束} 【参与条件】： - 首次购买{产品类型}产品 - 新客户注册并完成实名 【活动奖励】： - 首投满{X}元 → 奖励{金额} - 首投满{X}元 → 额外奖励{金额} 【获客渠道】： 1. 朋友圈广告投放 2. 银行APP弹窗 3. 员工朋友圈转发 """, "ROI预估": "目标ROI≥3" }, "存量激活": { "目标": "激活沉睡客户", "适用场景": "6个月无交易客户", "活动设计": """ 【活动名称】：唤醒礼包 【触达策略】： - 首触：短信关怀 - 二触：电话回访 - 三触：专属优惠 【激励方案】： - 沉睡客户专属加息券 - 专属理财经理服务 - 限时体验金{金额} """, "KPI": "激活率≥15%" } } ``` ### 3. Content Generation / 内容生成 ```python def generate_marketing_content(content_type: str, params: dict) -> dict: """营销内容生成""" if content_type == "朋友圈文案": templates = [ "【{产品名}限时福利】\n\n{利益点}\n\n⏰仅限{时间}\n💰年化收益最高{收益率}\n\n戳我了解详情👇", "好消息！{产品名}正在热售中~\n\n✨{亮点1}\n✨{亮点2}\n✨{亮点3}\n\n私信我可享专属优惠哦🎁" ] elif content_type == "短信文案": templates = [ "尊敬的{客户姓名}，{产品名}新品首发！年化收益率{收益率}%，{开始日期}至{结束日期}限时发售。回复Y办理，回复T退订。", "{客户姓名}，您的好友向您推荐{产品名}！首投满{金额}元即返{奖励金额}元，最高返{上限}元！戳{链接}参与。" ] elif content_type == "海报文案": return { "主标题": "{产品名} 限时抢购", "副标题": "{核心卖点}", "数据展示": [ f"年化收益 {收益率}%", f"起投金额 {金额}元", f"剩余份额 {份额}份" ], "CTA按钮": "立即抢购", "紧迫感": "名额有限，先到先得" } return { "type": content_type, "templates": templates, "filled_examples": [ fill_template(t, params) for t in templates ] } ``` ### 4. A/B Testing Framework / A/B测试框架 ```python class ABTestAnalyzer: """A/B测试分析""" def analyze_ab_test(self, variant_a: dict, variant_b: dict) -> dict: """ A/B测试结果分析 """ # 计算转化率 cvr_a = variant_a["conversions"] / max(variant_a["visitors"], 1) cvr_b = variant_b["conversions"] / max(variant_b["visitors"], 1) # 提升幅度 lift = (cvr_b - cvr_a) / max(cvr_a, 0.001) * 100 # 统计显著性检验 significant = self._chi_square_test( variant_a["visitors"] - variant_a["conversions"], variant_a["conversions"], variant_b["visitors"] - variant_b["conversions"], variant_b["conversions"] ) return { "variant_a": { "visitors": variant_a["visitors"], "conversions": variant_a["conversions"], "cvr": round(cvr_a * 100, 2) }, "variant_b": { "visitors": variant_b["visitors"], "conversions": variant_b["conversions"], "cvr": round(cvr_b * 100, 2) }, "lift": round(lift, 2), "statistically_significant": significant, "recommendation": "选择B" if lift > 5 and significant else "继续测试" } ``` --- ## Disclaimer This skill provides marketing planning tools for educational purposes. All marketing activities must comply with applicable advertising and financial regulations.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)