Demand Forecasting with Events

Uncover customer demand anomalies, improve forecasting and make pivotal business decisions around sales forecasts, inventory planning, pricing, labor and other supply functions.

The Challenge

Demand forecasting has been complex and inaccurate

Businesses know they need to capitalize on demand forecasting, but the intricacies of the process can be daunting. Creating demand forecasting methods, building out forecasting models and deploying both quantitative and qualitative modeling techniques are common business practices, but how can you ensure and optimize forecasting accuracy?

Our Solution

Incorporate demand causal factors into demand forecasting

Event data — both historical and future — are crucial demand causal factors that come from a variety of event categories . Historical event data aids in training forecasting models and is used for correlation with historical demand while forward looking data aids in forecasting future demand. Access demand intelligence by using our API or get real-time event Alerts with PredictHQ Notifications.

01

Centralize

Gain visibility of real-world events by centralizing and aggregating event data.

02

Correlate

Discover correlation between historical event data and demand.

03

Model

Integrate demand intelligence into forecasting models.

01

Centralize and aggregate demand causal factors

Gain access to events from hundreds of sources with one intelligent data API

Factors such as event type, attendance, location and audience will determine the actual impact on a business. PredictHQ has created proprietary event ranking technology that takes all factors into consideration.

02

Correlate data to business demand

Once you connect historical event data with historical incremental demand to establish the data correlation, you can begin to refine forecasting models.

PredictHQ provides feature engineering within our API like aggregate event impact, which helps businesses correlate events with demand faster. Get in touch with our experts to get access to our getting started guide.

03

Enhance forecasting models

Demand intelligence can be seamlessly integrated into your models once correlation is found – no matter what methods you’re currently using. Below are a few types of quantitative forecasting methods you might employ with event data:

  • Time series models
  • Regression analysis models
  • Machine learning models
  • Deep learning models
  • Causal models
curl -X GET https://api.predicthq.com/v1/events/count/
  -H "Accept: application/json"
  -H "Authorization: Bearer $ACCESS_TOKEN"

Customer Stories

Integrate PredictHQ into your demand forecasting and get results

Learn how customers are integrating demand intelligence into their forecasting models and seeing value.

Don't underestimate how much effort it takes to work with event data... Being able to rely on a company whose sole purpose is to remove the ambiguity of event data has been game-changing for us.
Read Legion’s Story

Demand forecasting based on industry

Data teams rely on high quality data to understand market conditions across all industries. Demand intelligence is designed to seamlessly plug into current forecasting models and improve the output
  • Accommodation

    Use demand intelligence in your yield management strategy to make the most of your occupancy and ensure higher revenue.

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  • Aviation

    Identify demand drivers to package, price and update load factoring long before seats start selling.

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  • Retail

    Know about the shifts in foot traffic before they occur and make decisions around staffing, marketing promotions, and more.

    Learn more
  • Transportation

    Know where demand surges are happening in advance so you can price effectively and place strategically.

    Learn more

    How else can I use PredictHQ?

    Explore all use casess

    Get Started

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