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Harness the power of demand intelligence
Knowing the impact of incoming events will transform your business. RideLondon brings 280k people and $32 million to the city each year - and that's only one event.
Demand Forecasting: Incorporating events into models for smarter forecasting.
Labor optimization: Ensuring the right number of drivers and store staff to meet demand.
Demand Forecasting: Incorporating events into models for smarter forecasting.
Demand forecasting: Incorporating events into their Antuit-built models to better understand demand across 9600 stores.
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.
Event visibility: Available in Zartico’s platform enabling deeper insight and smarter decisions.
Pricing: A key source of intelligence for the OTA Insights platform, enabling smarter pricing.
Labor optimization: Enriching the Legion Technologies platform, used by retailers to reduce labor inefficiencies.
Having insights into individual events is useful, but when it comes to demand forecasting, it’s useful to be able to identify peaks and throughs in demand on a daily basis.
The Features API reduces friction in forecasting by aggregating the total predicted attendees of events occurring in key locations on any given day. That means you spend less time trying to add up event attendees and then look for the spikes in demand. Instead, the Features API instantly shows you which days will be most impactful, and you’re able to drill down from there to see the individual event data.
Event locations matter. If there’s a big expo occuring next to a downtown hotel and a coffee shop, those businesses will see an uptick in patrons on those days due to the influx of people attending the event. Your business’s proximity to events that drive demand will be a good indicator of the scale of the impact you’ll experience from them.
The PredictHQ API relevance endpoint provides a number of parameters that you can use to understand an event’s importance to you. Features such as Radial Search allow you refine your results based on an event’s physical proximity to locations that matter to you and your business.
Not only do events themselves matter, but specific types of events will impact your business differently. Having labels adds rich context to the categories of events that you’re analyzing.
For example, you might have a Community Event occuring near one of your key locations. Being able to know whether that event is a fundraising event vesus a religious gathering can influence your forecasting models. If you know for certain that religious gatherings are influential, but fundraising events are not, you can exlude events with the “fundraising” label and ensure that it doesn’t interfere with your models.
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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import requests
response = requests.get(
url="https://api.predicthq.com/v1/events/",
headers={
"Authorization": "Bearer $ACCESS_TOKEN",
"Accept": "application/json"
},
params={
"active.gte": "2019-12-29",
"active.lte": "2020-02-20",
}
)
Typical users: Anyone who wants to test out and explore our data set
Access to our Web UI
Typical users: Revenue Management, Business Analysts, Product Managers, Operations
Access to both our Web UI and APIs
import requests
response = requests.get(
url="https://api.predicthq.com/v1/events/",
headers={
"Authorization": "Bearer $ACCESS_TOKEN",
"Accept": "application/json"
},
params={
"active.gte": "2019-12-29",
"active.lte": "2020-02-20",
}
)
Typical users: Data Engineers, Data Scientists, Developers
Knowing the impact of incoming events will transform your business. RideLondon brings 280k people and $32 million to the city each year - and that's only one event.
Contact our data science experts to find out the best solutions for your business. We'll get back to you within 1 business day.