Identify demand drivers by correlating your data with events

Combine and correlate our event data with your transactional demand data to reveal exactly which kinds of events drive your demand so you can better predict future demand trends.
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“With PredictHQ, we have been able to measure the effects of events around the globe in order to discover insights and provide strategic solutions for hotels, restaurants, destinations and retailers.”
Mirko Lalli
Mirko Lalli
CEO & Founder, 
Travel Appeal
Relationship between demand & events

Correlation with events is complex. Let us do the hard work.

Events vary in size, impact, location, frequency and duration, and raw event data is unstandardized and unverified. This makes correlation complicated and time-consuming without PredictHQ.

Securely upload your demand data into our relevancy engine tool, Beam, to understand what drives your demand in seconds.

  • 01 Collect the data
    Collect and integrate event data into existing dataset.
  • 02 Prepare the data
    Transform your data to match the event data you're correlating to.
  • 03 Transform the data
    Feature engineer the data. This is an iterative process accelerated by PredictHQ features.
  • 04 Create models
    Create and finetune models for verifying correlation and predicting demand.
  • 05 Take action
    Inform business decisions based on the patterns and demand spikes you detect.
Let Beam show you
Why correlation is important

The value of establishing correlation

Accurate prediction is driven by correlation. By understanding the relationship between your demand and key events, you can strategically plan for the future. PredictHQ's relevancy engine tool, Beam, harnesses the power of AI to identify these correlations, revealing which event categories have historically impacted your demand, enabling you to make more informed, data-driven decisions moving forward.
View our correlation docs
Python

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

model = ForecastingModel()
model.fit(your_demand_data, phq_aggregate_data_df)

future_demand = DataFrame(period=60)
forecast = model.predict(future_demand)
How to use correlation

Better understand your demand

Finding the statistical relationship between historical events and transactional demand helps businesses understand event impact. Transactional demand patterns can be complex and relevant event data can be difficult to source and process, so advanced time series modeling can take months to complete.

Beam converts unstructured, dynamic event data into a workable dataset. It then decomposes your data into a baseline and remainder and shows you the correlation with events data. We created our Demand Analysis tool, to turn months of work into minutes. Take the work out of establishing correlation so your teams can spend more time perfecting forecasting models.

Learn more about Beam
Documentation

Start the process today to establish correlation and improve your forecasts

Get started with step-by-step directions on where to get started with our technical documentation and start improving your demand forecasting models today.

Tech docs
Our data pipeline

Correlation is the foundation for accurate prediction

Transforming the data is typically the most time consuming and error-prone part of the process. Extracting features from raw data to better represent the underlying problem to predictive models is critical for model accuracy. Better features, means better results.

View the pipeline

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