Demand Forecasting with Events

Uncover customer demand trends to make pivotal business decisions around sales forecasts, inventory planning, pricing and other supply functions

Customers using PredictHQ in their demand forecasting

  • Domino's
  • Wyndham
  • goPuff
  • Accenture
  • Amadeus
  • Sonder
  • Qantas
  • First Data

Demand forecasting challenges unveiled

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


Incorporate event data into demand forecasting

Event data — both historical and future data — is a crucial data set made up of a variety of event categories. Historical event data is used to correlate past event data to your demand while forward looking data aids in training forecast models.


How to forecast demand with event data

There are three essential steps to follow when forecasting demand with event data: centralize event data, correlate event data, and incorporate demand intelligence into your forecasting models.

Centralize and aggregate event data

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

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


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.


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

phq_aggregate_data = PredictHQ_API(type='aggregate_event_impact')
phq_aggregate_data_df = TransformToDataFrame(phq_aggregate_data)

model = ForecastingModel(), phq_aggregate_data_df)

future_demand = DataFrame(period=60)
forecast = model.predict(future_demand)

Demand forecasting based on industry

Forecasting is make-or-break for business. Here’s how our customers use PredictHQ as breakthrough context for more accurate models.


Optimize labor, improve supply chain, prevent stock outs, and focus on customer experiences

PHQ rank


Improve load factor rates, capitalize on booking trends, deliver new airport infrastructure and total revenue optimization (TRO)



Forecast for compression nights, trend booking pace, and increase RevPar



Align passenger and driver demand, streamline inventory and plan surge pricing


Get Started

Contact us now to find out the best solutions for your business. We'll get back to you within 1 working day.

Trusted By

  • Accenture
  • Wyndham
  • Amadeus
  • Qantas
  • Domino's

For other questions

  • * fields are required

Legion relies on PredictHQ to help retailers
eliminate labor inefficiencies

“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 case study
Thomas Joseph Head of Data Science at Legion Technologies

Hoteliers achieve an increase of 10% RevPar

“Hotels have a product with an expiration date. When you make the wrong decisions around a big event, that can cause thousands of dollars in missed revenue."

Read case study
Sven Blaurock Head of Product at HQ plus

Aviation Rank™ empowers airline analysts

“Airline analysts have traditionally been swamped with data around events with little guidance on what to do with it. PredictHQ's Aviation Rank changes this, vastly empowering analysts to make simpler, quicker and smarter sense of the impact events have on demand – in turn enabling airlines to optimize inventory and boost revenue."

More details
Benjamin Cany Head of Offer Optimization of Airlines at Amadeus

5x increase in revenue with PredictHQ

“With PredictHQ, we can now use their data and predicted impact to analyze and recommend prices for major events much more easily and accurately."

Read case study
Andrew Kitchell CEO and Founder of Wheelhouse

25% increase in customer sentiment

“PredictHQ has been a fantastic resource. They were able to help us uncover the impact of events, from sporting games, concerts and conferences to the effects of weather and political activity."

Read case study
Mirko Lalli Travel Appeal CEO and Founder

35% increase in conversions

“PredictHQ demonstrated that they could reveal insights better than we could guess with our manual alerts. They were instrumental in driving up conversion rates further than we could imagine.”

Read case study
Mark von Nagy CIO at Online Republic
PredictHQ Preparedness Kit - A Guide to Navigate COVID-19 Impact and Plan for Recovery