信号采集与数据管线
多源采集海外社媒、搜索、竞品榜单与广告素材库；清洗、去重、多语言归一后按 ODS / DWD / DWS / ADS 分层入仓，保证口径一致、可回溯。
已就绪每一个判断，都能追溯到数据。
这一页说明 PlaySignal 用到的核心技术、它们如何串成一条闭环，以及每一项现在做到了哪一步。
数据来源
海外社媒
搜索需求
竞品榜单
广告素材库
你的 H5 / App
你的投放数据
采集与清洗
多源采集
去重与多语言归一
要素抽取
数仓分层
ODS 原始层
DWD 明细层
DWS 汇总层
ADS 应用层
原生智能体
信号
编剧
制作
闭环
产物
趋势账本
选题简报
测试素材
要素权重
共享数据账本
虚线框为你授权接入的数据
五项核心技术
内容要素图谱
用大语言模型把剧集拆解为题材、桥段、人设、关系、冲突、情绪、钩子与反转。选题、素材与剧集共用同一套要素 ID，这是后面能做归因的前提。
随测试档交付原生智能体编排
每个智能体只有一种写权限、只写一样产物；统计与归因等确定性计算，和选题、剧本等生成式任务分开运行。关键决策设人工闸门，所有输入输出留痕。
信号、编剧、制作已就绪；闭环为增长档开发生成工具
剧本方向、分镜与测试素材的批量生成，按钩子、人设、开场等变量出多个版本，并自动打上要素标签。
已开发完成；模型 API 另计归因与闭环
把 H5 / App 行为数据与你的投放数据对齐到剧集、版本与要素，计算要素权重并写回选题。
增长档开发人群分类：先知道在跟谁说话
我们按「市场 × 内容偏好 × 观看深度 × 付费方式」四个维度给观众分群。依据两类数据：公开的海外平台信号，以及你授权接入、经过去标识化处理的 H5 / App 行为数据。
- 市场与语言
- 国家与地区、语言、主要平台
- 内容偏好
- 偏好的题材与要素组合
- 观看深度
- 首集完播、连续观看集数、回看
- 付费方式
- 付费墙转化、激励广告接受度
- 深度追剧型北美 · 高完播 · 付费墙转化高
- 广告解锁型东南亚 · 完播中等 · 以激励广告为主
- 高客单型中东 · 集数少 · 单次付费高
- 浅尝型拉美 · 首集后流失快 · 付费低
用户模拟：花钱之前，先让模拟观众看一遍
我们用分群画像构建模拟观众面板，让它们「观看」选题梗概、开场钩子和前 30 秒脚本，输出偏好排序与预计流失点。它的作用是缩小候选范围，比如把 20 个选题筛到 5 个，再交给真实观众验证。它不替代真实观众。
- 1
构建画像
从分群数据生成模拟观众，每个都带有市场、偏好与行为参数。
- 2
呈现内容
分别呈现梗概、钩子和开场脚本，避免彼此干扰。
- 3
采集反应
输出偏好排序、预计流失点和理由摘要。
- 4
用实测校准
每轮真实测试后回校面板，并在报告中给出模拟与实测的一致情况。
行为分析：观众在哪里留下，在哪里离开
在 H5 与 App 中埋点，按统一口径记录每一次观看、跳出、付费与广告解锁，并还原到剧集、版本与要素。
集间留存 · 版本 A 与 B
- 首集完播率
- 看完第一集的观众占比
- 集间留存
- 每一集仍在观看的观众比例
- 付费墙转化
- 到达付费集后完成付费的比例
- 激励广告解锁
- 通过看广告解锁的次数与比例
- 回看与分享
- 重复观看与分享行为
- ARPU / LTV
- 单用户收入与生命周期价值
数据与隐私
数据归你
你的内容、用户与表现数据归你所有，服务结束可完整导出。
去标识化
分析使用去标识化数据，不以可直接识别个人的信息做建模。
按市场合规
按目标市场法规（例如 GDPR、CCPA）配置采集与同意机制，具体方案写入 SOW。
全程留痕
每个智能体的输入、输出与人工审批都有记录，可审计。
成熟度一览
| 技术 | 当前状态 | 所属服务 |
|---|---|---|
| 信号采集与数据管线 | 已就绪 | 测试 |
| 内容要素图谱 | 随热度数据系统交付 | 测试 |
| 信号、编剧、制作智能体 | 已就绪 | 测试 |
| 生成工具 | 已开发完成 | 测试 |
| 人群分类与行为分析 | 基于 H5 / App 数据交付 | 验证 |
| 用户模拟 | 按项目配置，结果仅作预筛 | 测试 / 验证 |
| 闭环智能体与要素归因 | 需数据层与工作流开发 | 增长 |
| 剧本直出 | 技术可行，需针对性调优 | 另议 |
想看这些技术在你的数据上怎么跑？
Every call traces back to data.
This page explains the core technology behind PlaySignal, how it forms one loop, and how far along each piece is today.
Sources
Overseas social
Search demand
Competitor charts
Ad libraries
Your H5 / app
Your media data
Collect and clean
Multi-source capture
Dedup and language normalization
Element extraction
Warehouse layers
ODS raw
DWD detail
DWS summary
ADS application
Native agents
Signal
Story
Studio
Loop
Artifacts
Trend ledger
Topic briefs
Test creative
Element weights
Shared ledger
Dashed boxes are data you choose to connect
Five core technologies
Signal collection and data pipeline
Captures overseas social, search, competitor charts and ad libraries; cleans, deduplicates and normalizes across languages, then loads into ODS, DWD, DWS and ADS layers so every figure is consistent and traceable.
ReadyContent element graph
Large language models break titles into genre, trope, character, relationship, conflict, emotion, hook and twist. Topics, creative and titles share one set of element IDs, which is what makes attribution possible later.
Delivered in TestNative agent orchestration
Each agent has one write permission and writes one artifact. Deterministic work such as statistics and attribution runs separately from generative work such as topics and scripts. Key decisions sit behind human gates, and every input and output is logged.
Signal, Story, Studio ready; Loop built in GrowGeneration tooling
Batch generation of script directions, storyboards and test creative, with versions varied by hook, character and opening, each tagged with its elements automatically.
Built; model API billed separatelyAttribution and loop
Aligns H5 and app behavior with your media data at the level of title, version and element, computes element weights and writes them back to topic selection.
Built in GrowAudience segmentation: know who you’re talking to
We group viewers along four dimensions: market, content preference, viewing depth and how they pay. Two data sources feed it: public overseas platform signals, and the de-identified H5 and app behavior you choose to connect.
- Market and language
- Country, language, main platforms
- Content preference
- Preferred genres and element combinations
- Viewing depth
- Episode 1 completion, consecutive episodes, rewatch
- How they pay
- Paywall conversion, rewarded-ad acceptance
- Binge watchersNorth America · high completion · strong paywall conversion
- Ad unlockersSoutheast Asia · medium completion · mostly rewarded ads
- High spendersMiddle East · fewer episodes · high single payments
- SamplersLatin America · fast drop after episode 1 · low pay
User simulation: let simulated viewers watch first
From segment profiles we build panels of simulated viewers and have them “watch” synopses, opening hooks and first-30-second scripts, producing a preference ranking and likely drop-off points. The job is to narrow the field, say from 20 topics to 5, before real viewers decide. It does not replace real viewers.
- 1
Build profiles
Generate simulated viewers from segment data, each with market, preference and behavior parameters.
- 2
Present content
Show synopsis, hook and opening script separately so they don’t bias each other.
- 3
Collect reactions
Output a preference ranking, likely drop-off points and short reasons.
- 4
Calibrate on real data
After each real test we recalibrate the panel and report how simulation and reality compared.
Behavior analysis: where viewers stay, and where they leave
The H5 player and apps record every view, exit, payment and ad unlock under one set of definitions, traced back to title, version and element.
Episode retention · versions A and B
- Episode 1 completion
- Share of viewers who finish episode 1
- Episode retention
- Share still watching at each episode
- Paywall conversion
- Share who pay on reaching a paid episode
- Rewarded-ad unlocks
- Unlocks through watching ads
- Rewatch and share
- Repeat viewing and sharing
- ARPU / LTV
- Revenue per user and lifetime value
Data and privacy
Your data stays yours
Your content, users and performance data belong to you and can be exported in full.
De-identified
Analysis runs on de-identified data; directly identifying information isn’t used for modeling.
Compliant by market
Collection and consent are set up for each market’s rules, such as GDPR and CCPA, and written into the SOW.
Logged end to end
Every agent’s inputs, outputs and human approvals are recorded and auditable.
Maturity at a glance
| Technology | Status | Service |
|---|---|---|
| Signal collection and data pipeline | Ready | Test |
| Content element graph | Delivered with the trend data system | Test |
| Signal, Story and Studio agents | Ready | Test |
| Generation tooling | Built | Test |
| Segmentation and behavior analysis | Delivered from H5 and app data | Validate |
| User simulation | Configured per project; pre-screening only | Test / Validate |
| Loop agent and element attribution | Needs data layer and workflow build | Grow |
| Direct script generation | Feasible; needs targeted tuning | Quoted separately |