Introducing the Forecasts API — Event-driven forecasts for precise demand planning. Fast, accurate, and easy to run.
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  • Introduction
  • Swagger UI
  • Loop
  • System Status
  • Getting Started
    • API Quickstart
    • Data Science Notebooks
    • PredictHQ Data
      • Data Accuracy
      • Event Categories
        • Attendance-Based Events
        • Non-Attendance-Based Events
        • Unscheduled Events
        • Live TV Events
      • Labels
      • Entities
      • Ranks
        • PHQ Rank
        • Local Rank
        • Aviation Rank
      • Predicted Attendance
      • Predicted End Times
      • Predicted Event Spend
      • Predicted Events
      • Predicted Impact Patterns
    • Guides
      • Geolocation Guides
        • Overview
        • Searching by Location
          • Find Events by Latitude/Longitude and Radius
          • Find Events by Place ID
          • Find Events by IATA Code
          • Find Events by Country Code
          • Find Events by Placekey
          • Working with Location-Based Subscriptions
        • Understanding Place Hierarchies
        • Working with Polygons
        • Join Events using Placekey
      • Date and Time Guides
        • Working with Recurring Events
        • Working with Multi-day and Umbrella Events
        • Working with Dates, Times and Timezones
      • Events API Guides
        • Understanding Relevance Field in Event Results
        • Attendance-Based Events Notebooks
        • Non-Attendance-Based Events Notebooks
        • Severe Weather Events Notebooks
        • Academic Events Notebooks
        • Working with Venues Notebook
      • Features API Guides
        • Increase Accuracy with the Features API
        • Get ML Features
        • Demand Forecasting with Event Features
      • Forecasts API Guides
        • Getting Started with Forecasts API
        • Understanding Forecast Accuracy Metrics
        • Troubleshooting Guide for Forecasts API
      • Live TV Event Guides
        • Find Broadcasts by County Place ID
        • Find Broadcasts by Latitude and Longitude
        • Find all Broadcasts for an Event
        • Find Broadcasts for Specific Sport Types
        • Aggregating Live TV Events
        • Live TV Events Notebooks
      • Beam Guides
        • ML Features by Location
        • ML Features by Group
      • Demand Surge API Guides
        • Demand Surge Notebook
      • Guide to Protecting PredictHQ Data
      • Streamlit Demo Apps
      • Guide to Bulk Export Data via the WebApp
      • Industry-Specific Event Filters
      • Using the Snowflake Retail Sample Dataset
      • Tutorials
        • Filtering and Finding Relevant Events
        • Improving Demand Forecasting Models with Event Features
        • Using Event Data in Power BI
        • Using Event Data in Tableau
        • Connecting to PredictHQ APIs with Microsoft Excel
        • Loading Event Data into a Data Warehouse
        • Displaying Events in a Heatmap Calendar
        • Displaying Events on a Map
    • Tutorials by Use Case
      • Demand Forecasting with ML Models
      • Dynamic Pricing
      • Inventory Management
      • Workforce Optimization
      • Visualization and Insights
  • Integrations
    • Integration Guides
      • Keep Data Updated via API
      • Integrate with Beam
      • Integrate with Loop Links
    • Third-Party Integrations
      • Receive Data via Snowflake
        • Example SQL Queries for Snowflake
        • Snowflake Data Science Guide
          • Snowpark Method Guide
          • SQL Method Guide
      • Receive Data via AWS Data Exchange
        • CSV/Parquet Data Structure for ADX
        • NDJSON Data Structure for ADX
      • Integrate with Databricks
      • Integrate with Tableau
      • Integrate with a Demand Forecast in PowerBI
      • Google Cloud BigQuery
    • PredictHQ SDKs
      • Python SDK
      • Javascript SDK
  • API Reference
    • API Overview
      • Authenticating
      • API Specs
      • Rate Limits
      • Pagination
      • API Changes
      • Attribution
      • Troubleshooting
    • Events
      • Search Events
      • Get Event Counts
    • Broadcasts
      • Search Broadcasts
      • Get Broadcasts Count
    • Features
      • Get ML Features
    • Forecasts
      • Models
        • Create Model
        • Update Model
        • Replace Model
        • Delete Model
        • Search Models
        • Get Model
        • Train Model
      • Demand Data
        • Upload Demand Data
        • Get Demand Data
      • Forecasts
        • Get Forecast
      • Algorithms
        • Get Algorithms
    • Beam
      • Create an Analysis
      • Upload Demand Data
      • Search Analyses
      • Get an Analysis
      • Update an Analysis
      • Partially Update an Analysis
      • Get Correlation Results
      • Get Feature Importance
      • Refresh an Analysis
      • Delete an Analysis
      • Analysis Groups
        • Create an Analysis Group
        • Get an Analysis Group
        • Search Analysis Groups
        • Update an Analysis Group
        • Partially Update an Analysis Group
        • Refresh an Analysis Group
        • Delete an Analysis Group
        • Get Feature Importance for an Analysis Group
    • Demand Surge
      • Get Demand Surges
    • Suggested Radius
      • Get Suggested Radius
    • Saved Locations
      • Create a Saved Location
      • Search Saved Locations
      • Get a Saved Location
      • Search Events for a Saved Location
      • Update a Saved Location
      • Delete a Saved Location
    • Loop
      • Loop Links
        • Create a Loop Link
        • Search Loop Links
        • Get a Loop Link
        • Update a Loop Link
        • Delete a Loop Link
      • Loop Settings
        • Get Loop Settings
        • Update Loop Settings
      • Loop Submissions
        • Search Submitted Events
      • Loop Feedback
        • Search Feedback
    • Places
      • Search Places
      • Get Place Hierarchies
  • WebApp Support
    • WebApp Overview
      • Using the WebApp
      • API Tools
      • Events Search
      • How to Create an API Token
    • Getting Started
      • Can I Give PredictHQ a Go on a Free Trial Basis?
      • How Do I Get in Touch if I Need Help?
      • Using AWS Data Exchange to Access PredictHQ Events Data
      • Using Snowflake to Access PredictHQ Events Data
      • What Happens at the End of My Free Trial?
      • Export Events Data from the WebApp
    • Account Management
      • Managing your Account Settings
      • How Do I Change My Name in My Account?
      • How Do I Change My Password?
      • How Do I Delete My Account?
      • How Do I Invite People Into My Organization?
      • How Do I Log In With My Google or LinkedIn Account?
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      • I Signed Up Using My Google/LinkedIn Account, but I Want To Log In With My Own Email
    • API Plans, Pricing & Billing
      • Do I Need To Provide Credit Card Details for the 14-Day Trial?
      • How Do I Cancel My API Subscription?
      • Learn About Our 14-Day Trial
      • What Are the Definitions for "Storing" and "Caching"?
      • What Attribution Do I Have To Give PredictHQ?
      • What Does "Commercial Use" Mean?
      • What Happens If I Go Over My API Plan's Rate Limit?
    • FAQ
      • How Does PredictHQ Support Placekey?
      • Using Power BI and Tableau With PredictHQ Data
      • Can I Download a CSV of Your Data?
      • Can I Suggest a New Event Category?
      • Does PredictHQ Have Historical Event Data?
      • Is There a PredictHQ Mobile App?
      • What Are Labels?
      • What Countries Do You Have School Holidays For?
      • What Do The Different Event Ranks Mean?
      • What Does Event Visibility Window Mean?
      • What Is the Difference Between an Observed Holiday and an Observance?
    • Tools
      • Is PHQ Attendance Available for All Categories?
      • See Event Trends in the WebApp
      • What is Event Trends?
      • Live TV Events
        • What is Live TV Events?
        • Can You Access Live TV Events via the WebApp?
        • How Do I Integrate Live TV Events into Forecasting Models?
      • Labels
        • What Does the Closed-Doors Label Mean?
    • Beam (Relevancy Engine)
      • An Overview of Beam - Relevancy Engine
      • Creating an Analysis in Beam
      • Uploading Your Demand Data to Beam
      • Viewing the List of Analysis in Beam
      • Viewing the Table of Results in Beam
      • Viewing the Category Importance Information in Beam
      • Feature Importance With Beam - Find the ML Features to Use in Your Forecasts
      • Beam Value Quantification
      • Exporting Correlation Data With Beam
      • Getting More Details on a Date on the Beam Graph
      • Grouping Analyses in Beam
      • Using the Beam Graph
      • Viewing the Time Series Impact Analysis in Beam
    • Location Insights
      • An Overview of Location Insights
      • How to Set a Default Location
      • How Do I Add a Location?
      • How Do I Edit a Location?
      • How Do I Share Location Insights With My Team?
      • How Do I View Details for One Location?
      • How Do I View My Saved Locations as a List?
      • Search and View Event Impact in Location Insights
      • What Do Each of the Columns Mean?
      • What Is the Difference Between Center Point & Radius and City, State, Country?
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  2. Beam (Relevancy Engine)

Beam Value Quantification

An in-depth explanation of the terms used in Beam Value Quantification

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Last updated 1 month ago

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Beam's Value Quantification module scans for anomalies in your uploaded demand data and leverages PredictHQ's comprehensive event data and coverage to confidently attribute a proportion of your demand fluctuations to demand shifts caused by events.

In order to gain the best results from Beam's Value Quantification module, please make sure to have specified your demand unit price, unit description and currency. This will not only ensure your results are accurate, but it will also make the page and any generated PDF reports more readable.

Your unit price can be the average, or blended costs of whichever type of unit they represent. For example, if the demand data represents food sales, you can set it as the average price of a meal at your establishment. If the demand data represents actual revenue, the unit price can remain at 1.

Explanation of terms

Overview

When viewing your Value Quantification results, you can expand the results to view more detailed information. The expanded section looks like this:

Current Annual Revenue due to Events

Demand Variability

Represents the positive variability, or positive remainder mentioned above. This is the anomalous shifts in demand that cannot be explained by overall growth trends nor seasonality.

Attributed to Events

This figure represents the proportion of the overall positive variability that can be attributed to events.

Current Annual Revenue Loss due to Events

Similar to the annual revenue gain, this figure represents the annual revenue loss that the Beam Value Quantification model has identified as attributable to events.

Negative Variability

Represents the negative variability, or negative remainder. This is the anomalous negative shifts in demand that cannot be explained by overall growth or decline trends nor seasonality.

Attributed to Events

This figure represents the proportion of the overall negative variability that can be attributed to events.

Estimated Revenue Increase

This is the estimated revenue increase that could be gained through integrating PredictHQ data into your forecasts or operational decision-making. It is calculated based on the attributed historic impact of events (both positive and negative), an estimated error-reduction rate (see Projected Forecasting Impact) and the unit price.

Additional Units

This represents the same uplift but in raw demand units. Whereas the Estimated Revenue Increase is in revenue format, this represents the uplift in demand units (e.g. Room Bookings) you could expect.

Projected Forecasting Impact

This represents the estimated forecasting and error-reduction improvements you could expect from integrating PredictHQ data into your forecasting models and decision-making. This percentage is measured in relative MAPE (Mean Absolute Percentage Error) improvements. It is based off the historical impact of events that has been observed on your demand patterns. The model assumes a 25% existing customer MAPE figure.

This represents the annual revenue that the Beam Value Quantification model has identified as attributable to events. The model looks at the proportion of positive variability () then leverages an attribution model to associate a percentage of the remainder to the impact of events taking place on a given day. Once those elements are worked out, it applies the unit you specified in order to calculate the annual revenue gain that is being caused by events.

positive remainder
Beam Analysis missing unit type
Add unit price, description and currency to the analysis
Expanded insights for Value Quantification