Spavik
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Predict which leads will convert

Your CRM already knows which leads became customers and what they did during the trial. Spavik learns from those past leads and gives each new prospect a probability of buying, so sales can call the right people first.

Returns
A probability of converting, per lead
Language
Python
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In your app

Acme

34%

conversion probability

Display the conversion probability on the prospect’s record.

The data you need

One row per past case. Spavik learns how the inputs relate to the column you want to predict.

Past leads, their interactions with your product and whether they became customers.

ColumnRoleMeaning
visitsInputVisits during the trial
trial_daysInputDays of trial used
demo_bookedInput1 if a demo was booked
convertedPredicted1 for a paying customer, otherwise 0

The code

A complete, commented script. Set SPAVIK_API_KEY in your server environment, never in browser code.

lead-conversion-prediction.py Python 
from spavik import Spavik
 
# Set SPAVIK_API_KEY in your server environment.
spavik = Spavik()
 
# leads.csv: visits, trial_days, demo_booked, converted.
# converted = 1 for a paying customer, otherwise 0.
history = "leads.csv"
report = spavik.evaluate(history, target="converted")
if report["assessment"]["status"] != "better_than_baseline":
    raise ValueError(report["assessment"]["message"])
 
# Create once, then reuse for new prospects.
model = spavik.train(history, target="converted")
 
# Acme's current activity, before a purchase is known.
prospect = {
    "visits": 8,
    "trial_days": 5,
    "demo_booked": 1,
}
result = model.predict(prospect)
 
# Use the probability of conversion, not the 0/1 label.
probability = result.lignes[0]["proba_1"]
print(f"Acme conversion probability: {probability:.0%}")

How it works

  1. 1

    Check your data

    evaluate() tells you, for free, whether your history does better than a simple guess.

  2. 2

    Learn from your history

    train() creates the model once, for free. Keep it and reuse it for every new case.

  3. 3

    Ask for predictions

    predict() returns a value for each new row, from your server. One credit per row.

  4. 4

    Keep it accurate

    Report what actually happened with submit_outcomes(), check performance(), then refresh().

Questions

How much history do I need?

At least 10 rows, with at least 2 different values in the column you predict. Include leads that converted and leads that did not, measured at the same point of the trial. evaluate() tells you for free whether your history does better than a simple guess before you train anything.

What does it cost?

Checking your data with evaluate() and creating the model with train() are free. Each predicted row costs one credit on the default engine. You train once and keep the model, so you pay only for the predictions you request.

Try it on your own data

Get an API key. Checking your data is free.

Get API key Read the docs