How e-commerce platform Thirstie reduced their sales cycle by 30% with an analytics feature powered by Faraday
Thirstie, an e-commerce platform for beverage brands, built a new analytics feature, powered by Faraday, that drove adoption.
Learn how →Use predictions to optimize how you prioritize, route, and engage leads.
Use predictions to optimize how you prioritize, route, and engage leads.
Turbocharge your lead handling workflows with AI.
# ADAPTIVE DISCOUNTING
# First register your data to produce event streams
curl https://api.faraday.ai/datasets --header 'Authorization: Bearer $YOUR_API_TOKEN' --json '{
"name": "Signup",
"identity_sets": {
"shipping": {...}
},
"options": {
"type": "hosted_csv",
"upload_directory": "signup_data_files"
},
"output_to_streams": {
"signup": {
"data_map": {
"datetime": "created_at"
},
"value": "total"
}
}
}'
# Now organize your customer data into cohorts
curl https://api.faraday.ai/cohorts --header 'Authorization: Bearer $YOUR_API_TOKEN' --json '{
"name": "Leads",
"stream_name": "signup"
}'
# Next, declare your prediction objectives
# ⚠️ Uses prerelease features which may not work for your account: Forecast
curl https://api.faraday.ai/forecasts --header 'Authorization: Bearer $YOUR_API_TOKEN' --json '{
"name": "Forecasted spend",
"stream_name": "transaction",
"stream_property_name": "value"
}'
# And finally complete your pipeline to deploy
curl https://api.faraday.ai/scopes --header 'Authorization: Bearer $YOUR_API_TOKEN' --json '{
"name": "Adaptive discounting",
"population": {
"include": [
"$LEADS_COHORT_ID"
]
},
"payload": {
"forecast_ids": [
"$FORECASTED_SPEND_FORECAST_ID"
]
}
}';
Patterns that predict behavior in the early stages of a customer journey eventually stop working and new ones take over. Only Faraday uses time-linked model ensembles to tell the true story.
Faraday will detect and select the best tool for the job, from time-tested classics to cutting-edge GenAI.
All the power, flexibility, and data you need to ship fast.
Get more accurate predictions—and avoid the dreaded cold-start problem—with 1,500+ consumer attributes on nearly 240 million adults.
Explainability, privacy, bias management, and transparency: find your balance between power and fairness.
In addition to its built-in consumer data, Faraday can find patterns in whatever first-party data you have for even more accurate predictions.
Thirstie, an e-commerce platform for beverage brands, built a new analytics feature, powered by Faraday, that drove adoption.
Learn how →Here's a few ways to get started, based on how you like to work.
Let’s work together async with Slack and Figma to prototype fast. Everyone at Faraday from our CEO/CTO to our engineers can pitch in to get you shipping.