Demand intelligence is a critical dataset for data lakes. Improve accuracy in models by understanding your catalysts of demand.
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.
Without it, companies struggle to know what drives their demand anomalies. Real-world event data is always changing, difficult to standardize, and often messy with duplicate events and spam. So many teams don’t factor this dataset into their models - creating a huge gap in teams’ analytical efforts.
Understanding event impact unlocks forecasting opportunities and improves accuracy in data models. By sharing this dataset in a data lake, teams across a company can train their models to identify future demand fluctuations and make better business decisions.
Centralize and aggregate real world event data.
Identify correlation between historical transaction data and events.
Provide data access and train data models with this new dataset.
Real-world event data is always changing, difficult to standardize, and often messy with duplicate events and spam. With PredictHQ, you can 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 the process down to minutes.
Demand intelligence can be seamlessly integrated into teams’ models once correlation is established, enabling your team to prepare for surges when incremental demand catalysts happen, or mitigate losses when decremental demand catalysts are coming.
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.
Predictive Analytics
Inventory Management
Workforce Optimization
Dynamic Pricing
Demand Forecasting