If you have at least 2000 payments, Stripe data probably contains your buyer signal - the unique patterns that predict who will buy from you.
Last month, a friendly brand exported about 10,000 payment records from Stripe as a CSV. Then they uploaded them to Faraday and, within 24 hours, got their results:
Let's focus on one feature - "Science/new technology." If a person is interested in this, they are much more likely to buy this brand's products:
Faraday is batteries-included - we know things like whether people like science thanks to our built-in consumer data.
Now, this brand can predict the likelihood of more than 250 million Americans to buy their product. Faraday can take any list of people and sort it by likelihood to buy... or Faraday can generate the list in the first place, using a "Deployment".
Faraday only needs the following fields:
(Later, you can set up an automated integration. But for now, since this is a relatively stable model based on inherent consumer characteristics, it should be stable for at least a few weeks.)
In Faraday, you map the fields:
And finally, you make a likely buyers model.
In total, if this takes longer than 15 minutes, email the CTO because that's a critical bug!
Happy Signal Hunting!
PS. When this brand loaded our model into Facebook, we immediately started beating Facebook's internal model, even though we were still in learning phase...
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