Train your predictive analytics models with historical event data to better anticipate demand anomalies and prepare before they occur.
Dynamic pricing: Setting prices and packaging based on smarter forecasts.
Labor optimization: Ensuring the right number of drivers and store staff to meet demand.
Yield optimization: Tracking incoming demand ahead of the booking curve.
Merchant Services: Adding relevant demand causal factor insights to customer communications.
Amazon Alexa's "Events Near Me" feature uses PredictHQ data to inform users about local events.
Demand forecasting: Getting drivers in the right place ahead of time to improve pick-up times.
Real-world event data is always changing, difficult to standardize, and often messy with duplicate events and spam. So predictive analytics models usually don’t account for this data set. Instead, data science teams have to manually pull this information separately.
Demand intelligence identifies demand catalysts based on intelligent event data so various teams within a business can spot out future demand fluctuations before they happen. Technical teams can train their models with the event API to get a more accurate depiction of forecasts. Business managers can set up event notifications to get instantly notified of when impactful events are taking place.
Centralize and aggregate historical event data.
Identify correlation between historical transaction data and events.
Train predictive analytics models to improve forecasting precision.
PredictHQ identifies the aggregate impact of historical events. You can gain access to millions of events from hundreds of data sources into a single event API.
Identify the relationship between historical event data and historical transaction data to establish correlation. This process can take teams months to understand and pinpoint but PredictHQ has transformed that process down to minutes.
Demand intelligence can be seamlessly integrated into your models once correlation is established. You’ll be able to identify the event categories that impact your business the most and train your models to better predict future demand.
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.
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Dynamic pricing: Setting prices and packaging based on smarter forecasts.
Demand forecasting: Getting drivers in the right place ahead of time to improve pick-up times.
Labor optimization: Ensuring the right number of drivers and store staff to meet demand.
Amazon Alexa's "Events Near Me" feature uses PredictHQ data to inform users about local events.
Data Centralization
Inventory Management
Workforce Optimization
Dynamic Pricing
Demand Forecasting