Uncover customer demand trends to make pivotal business decisions around sales forecasts, inventory planning, pricing and other supply functions
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?
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.
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.
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.
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.
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:
phq_aggregate_data = PredictHQ_API(type='aggregate_event_impact')
phq_aggregate_data_df = TransformToDataFrame(phq_aggregate_data)
model = ForecastingModel()
model.fit(your_demand_data, phq_aggregate_data_df)
future_demand = DataFrame(period=60)
forecast = model.predict(future_demand)
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
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
Contact us now to find out the best solutions for your business. We'll get back to you within 1 working day.
Head of Data Science at Legion Technologies
Head of Product at HQ plus
Head of Offer Optimization of Airlines at Amadeus
CEO and Founder of Wheelhouse
Travel Appeal CEO and Founder
CIO at Online Republic