Enhance your Data Lake with Demand Intelligence

Demand intelligence is a critical dataset for data lakes. Improve accuracy in models by understanding your catalysts of demand.
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The Challenge

Most data repositories used for forecasting don’t factor in all demand causal factors like historical event data

Without it, companies struggle to know what drives their demand. Event data is always changing, difficult to standardize, and often messy with duplicate events and spam. Many teams don’t factor this dataset into their models — creating a huge gap in teams’ analytical efforts.

Our Solution

Add demand intelligence into your data lake and improve forecasting accuracy

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.

01

Centralize

Centralize and aggregate event data.
02

Correlate

Identify correlation between your historical transactional data and events.
03

Access & Train

Provide data access and train data models with new demand causal factors.
01

Centralize and aggregate

Uncover the catalysts causing demand fluctuations

Demand causal factors — like events — are always changing, difficult to standardize, and often messy with duplicate events and spam. With PredictHQ, you can access millions of events from hundreds of data sources in a single API.

More on Aggregate event impact
02

Correlate to discover what drives demand

Identify the relationship between your historical transactional data and the impact of events to establish correlation. This process can take teams months to understand and pinpoint but PredictHQ has transformed the process down to minutes.

How we correlateCorrelation Docs
PythonCurl
import requests

response = requests.get(
  url="https://api.predicthq.com/v1/events/",
  headers={
    "Authorization": "Bearer $ACCESS_TOKEN",
    "Accept": "application/json"
  },
  params={
    "country": "US",
    "q": "Coachella",
    "rank.gte": "80",
    "category": "festivals"
  }
)

print(response.json())
03

Make this dataset accessible to improve accuracy

Demand intelligence can be seamlessly integrated into your forecasting 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.

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Customer Stories

Designing data lakes to optimize analytics

Learn how customers are integrating demand intelligence into their forecasting models and seeing value.
Customer Stories
Thomas Joseph
Head of Data Science, 
Legion
Thomas Joseph
Head of Data Science, 
Legion
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 Legion’s Story

Optimizing data accuracy based on your industry

Your analytics strategy can define your success and save your team time. Here’s how businesses can use PredictHQ as breakthrough context to update their models
  • Retail

    Predict an influx in customers and better equip your stores long before fluctuations actually happen.

  • Aviation

    Identitfy the key events that drive demand and optimize yield ahead of time.

  • Accommodation

    Don’t be thrown off with a sudden influx of demand. Better plan for higher yield and occupancy.

  • Transportation

    Price and place better so you don’t have to be reactive to demand surges.

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