[SAMPLE] PredictHQ's Intelligent Event Data | Retail | Harrods, London, UK | September 2023 - ...

Retail companies need to know which events impact their demand, when similar events are coming up, and how significant these events will be - whether that’s how many people will be attending events around their locations or how severe an unscheduled event such as a severe storm is. This directly informs their inventory levels, workforce optimization, and pricing strategies. Events impact the strategies of brick and mortar stores or chains that will be capitalizing on increased footfall and need to make decisions based on an expected decrease in footfall. They also impact delivery-based retail that will need to update their fleet and warehousing strategies ahead of time.

Data also includes Predicted Event Spend, a dollar figure that reflects the predicted amount of retail, accommodation, and transportation spending in a specific area as a result of a major event. At the core of this figure sits our market leading global event coverage, predicted event attendance, local accommodation demand, aviation demand, third party data and more to give you greater geographical context of the scale of the economic impact an event will have.

Location: McDonald's, Times Square, New York City
Visibility Window: 6-Months Historical
Categories: community, concerts, conferences, expos, festivals, performing arts, sports, academic, daylight savings, observances, politics, public holidays, school holidays, airport delays, disasters, health warnings, severe weather, terror

Fields Included:
- Title
- Category
- Labels
- Description
- Start date and time
- End date and time
- Predicted end time
- Timezone
- 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, retail, shopping, weather, severe weather, historical weather

搜集汇总

背景与挑战

背景概述

该数据集是PredictHQ提供的零售业智能事件数据样本，聚焦于伦敦哈罗德百货2023年9月的事件信息，旨在帮助企业预测事件对需求的影响以优化库存、劳动力和定价策略。它包含事件类别、时间、位置、预测支出和出席人数等核心字段，数据基于多源收集和机器学习模型确保高质量。