[SAMPLE] PredictHQ's Intelligent Event Data | Spending Data at Events | Seoul, South Korea | ...

Predicted event spend draws on: 1) verified and intelligent event data, 2) predicted attendance models which factor in venue, performers, historical attendance, and more to identify an accurate estimate of the event size, 3) anonymised customer impact data from our data lake that span across a wide range of businesses, 4) location specific information such as standard room rates and restaurant prices for thousands of locations, and 5) event categorization models which identify the category, type, and relevant labels to define events. PredictHQ created this tool because our customers requested it, but its application will vary significantly based on which industry, and which team, is using it. For example: 1) QSRs can use it to compare apples to apples i.e. the restaurant spend depending on the different kinds of events near the same store, 2) Accommodation businesses can use it to communicate to their hotel managers as to why the pricing recommendations have been increased or decreased, as well as to set appealing minimum stay discounts or similar packaging updates, 3) Event-based marketing or out-of-home marketing groups can use it to quickly describe the impact of an event for particular segments considering where to place their ads, and 4) Smaller businesses that don’t have the team capacity to use event data extensively in planning can use it as a shorthand for how impactful an event will be, and roster more staff or stock up on inventory accordingly. Predicted Event Spend demonstrates how individual or groupings of events drive massive spending on behalf of visitors and is a reflection of the total amount of additional spending within a defined area as a result of a specific event.

Location: Seoul, South Korea
Visibility Window: 1-Year Historical
Categories: Sports, Festivals, Expos, Conferences, Concerts, Performing Arts, and Community.

Fields Included:
- Title
- Category
- Labels
- Description
- Start date and time
- End date and time
- Predicted end time
- Country
- Lat / Lon
- Venue Name
- Venue Address
- Rank (PHQ Rank, Local Rank)
- PHQ Attendance
- Event status
- Place Hierarchy
- Created/updated timestamps
- Predicted event spend total
- Predicted event spend accommodation
- Predicted event spend hospitality
- Predicted event spend transportation

Polygon information: PredictHQ's polygons enable you to see the full area impacted by an event represented as a shape, because many types of events don't occur neatly at a point on a map. That means you will get a much more accurate picture of impact. Data samples including polygons are available upon request.

Data quality:
PredictHQ's data quality is one of its key strengths:
1) We have developed a set of Quality Standards for Processing Demand Causal Factors (QSPD), which are used to define the criteria for high-quality event data. By following these standards, PredictHQ ensures that their data meets the highest levels of quality.
2) We use more than 450 data sources to collect event data, including public records, social media, and ticketing websites.
3) We have built thousands of machine learning models that standardize, verify, enrich, and rank every single event.
4) On average we process 28 million events and 422,000 entities every day
5) We track the quality of our data over time and make improvements as needed.

About PredictHQ:
PredictHQ is the world’s first and only company that provides the missing context for the biggest external factor that impacts businesses demand – events. PredictHQ’s intelligent data of verified global events enables businesses to forecast shifts in demand from events to be able to adjust their inventory, make changes to labor, dynamically price and operate more efficiently. Think conferences, sports games, college graduations, floods, and more. PredictHQ brings all events into one place, combines it with world-first tools and intelligence to allow organizations to better predict and respond to changing customer demand created by events in an easy, reliable, and scalable way. We meet customers exactly where they are, ensuring they can access our data the way that suits them best.

Learn more about PredictHQ's real-world event data by visiting our Developer and Data Science Documentation: https://docs.predicthq.com/

Keywords: attended events, attendance, sports, festivals, expos, conferences, concerts, performing arts, community, polygon, consumer spending, predicted spend, location information, demand intelligence, financial data, venue location, accommodation, transportation, restaurant, demand intelligence, event intelligence, event categorisation, business insights, event tracking, historical event data, even impact analysis, event-driven decisions, predictive analytics, hospitality, travel, tourism, aviation, flight, ride-sharing, transportation, mobility, spending data, money, financial

预测活动支出模型基于以下数据源：1）经过核验的智能活动数据；2）综合场地、演出嘉宾、历史上座率等多维度因素的上座率预测模型，用以精准估算活动规模；3）覆盖多行业领域的匿名化客户影响数据（源自PredictHQ数据湖）；4）数千个点位的本地化信息，包括标准客房定价、餐饮消费价格等；5）用于定义活动类别、类型及关联标签的活动分类模型。

PredictHQ开发此工具源于客户需求，但其具体应用场景会因使用方所属行业与业务团队的不同而存在显著差异。例如：1）快餐连锁品牌（Quick Service Restaurants, QSRs）可借助该工具开展可比口径分析——即对比同一家门店周边不同类型活动对应的门店营收表现；2）住宿业态商户可通过该工具向酒店管理人员解释调价建议的调整依据，同时可据此设置更具吸引力的连住折扣或同类套餐优化方案；3）活动营销或户外营销团队可利用该工具快速评估特定客群场景下的活动影响，辅助广告投放点位决策；4）暂无充足团队资源开展深度活动数据规划的中小商户，可将该工具作为活动影响程度的快速评估依据，据此调整人员排班与库存备货量。

预测活动支出（Predicted Event Spend）可展示单个或成组活动如何为到访者带来大额消费，同时反映特定活动在划定区域内引发的额外消费总额。

覆盖区域：韩国首尔
可见时间窗口：1年历史数据
活动类别：体育赛事、节庆活动、展会、会议、音乐会、演艺演出及社区活动。

包含字段：
- 活动标题
- 活动类别
- 关联标签
- 活动描述
- 开始日期与时间
- 结束日期与时间
- 预测结束时间
- 国家
- 纬度/经度（Lat / Lon）
- 场地名称
- 场地地址
- 排名（PHQ排名、本地排名）
- PHQ预测上座率
- 活动状态
- 场所层级
- 创建/更新时间戳
- 预测活动总消费
- 预测活动住宿消费
- 预测活动餐饮接待消费
- 预测活动交通消费

多边形数据说明：PredictHQ的多边形数据可展示活动影响的完整区域（以图形形式呈现），因多数活动并非仅发生在地图上的单一坐标点。借此可更精准地呈现活动影响范围。含多边形数据的样本可按需申请获取。

数据质量：
数据质量是PredictHQ的核心优势之一：
1）我们制定了《需求因果因素处理质量标准》（Quality Standards for Processing Demand Causal Factors, QSPD），用以明确高质量活动数据的判定准则。遵循该标准可确保PredictHQ的数据达到顶级质量水准。
2）我们依托超450个数据源采集活动数据，涵盖公共档案、社交媒体及票务网站等渠道。
3）我们搭建了数千个机器学习模型，用于对每一项活动进行标准化处理、核验、丰富信息及排名评级。
4）我们日均处理约2800万项活动及42.2万个实体数据。
5）我们会持续追踪数据质量，并按需进行优化迭代。

关于PredictHQ：
PredictHQ是全球首家且唯一一家针对影响企业营收的核心外部变量——活动，提供缺失关联背景信息的企业。PredictHQ经过核验的全球活动智能数据，可帮助企业预测活动引发的需求波动，从而灵活调整库存、调配人力、实施动态定价并提升运营效率，例如会议、体育赛事、高校毕业典礼、洪涝灾害等各类事件。PredictHQ将各类活动数据整合至统一平台，结合全球首创的工具与智能分析能力，帮助各类组织以简便、可靠且可扩展的方式，更好地预测并应对活动引发的客户需求变化。我们充分贴合客户的实际场景，确保其可通过最适配自身的方式获取我方数据。

如需了解更多PredictHQ的真实场景活动数据信息，请访问我们的开发者与数据科学文档中心：https://docs.predicthq.com/

关键词：参与式活动、上座率、体育赛事、节庆活动、展会、会议、音乐会、演艺演出、社区活动、多边形区域、消费支出、预测支出、本地化信息、需求智能分析、金融数据、场地位置、住宿、交通、餐饮、需求智能分析、活动智能分析、活动分类、商业洞察、活动追踪、历史活动数据、活动影响分析、活动驱动决策、预测分析、接待餐饮、旅行旅游、航空航班、共享出行、出行流动性（mobility）、消费数据、资金金融

搜集汇总

背景与挑战

背景概述

该数据集为韩国首尔地区的活动支出预测样本，整合了智能事件数据、预测出席模型及多源信息，以估算活动引发的额外消费总额。它适用于餐饮、住宿、营销等行业进行需求预测和决策优化，并依托严格的质量标准和机器学习模型确保数据可靠性。