Turn behavioral data into clear purchase probability estimates. Tune settings for segments, channels, and risk. Export CSV or PDF and review outcomes instantly here.
| # | Lead | Recency | Interactions | Sessions | Open % | AOV | Cart | Tickets | Tenure | Channel | Risk | Discount | Example probability |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 82 | 5 | 25 | 45 | 42 | 260 | 8 | 0 | 18 | Referral | Low | Low | 98.79% |
| 2 | 55 | 30 | 10 | 15 | 22 | 90 | 2 | 2 | 8 | Organic | Medium | Medium | 86.37% |
| 3 | 28 | 120 | 3 | 4 | 6 | 40 | 0 | 6 | 3 | Paid | High | High | 32.57% |
This predictor uses a logistic model to map weighted signals into a probability.
For production use, train coefficients on historical outcomes and validate calibration on a holdout set.
Input fields represent common customer signals such as lead score, recency, interaction frequency, sessions, email engagement, cart activity, and support pressure. The calculator converts each signal into a comparable 0–1 feature using min–max scaling or log(1+x) scaling. This reduces the influence of extreme values while preserving ordering, making the weighted model more stable for diverse segments.
The model produces a probability through a sigmoid transformation of the log-odds score. When probability is near 0.50, small data changes can move results noticeably, so the tool labels confidence as Low, Medium, or High based on distance from 0.50. Use the driver list to understand which inputs most increased or decreased the score, then validate drivers against your business intuition.
Decision thresholds convert probability into actions. A higher threshold can focus effort on the strongest prospects, while a lower threshold can broaden nurture campaigns. If you enter outcome value and outreach cost, the calculator also estimates expected value and ROI, supporting budget allocation across channels. Treat value outputs as directional and recheck assumptions when pricing or costs change.
Advanced options let you change scaling maxima to match your observed data ranges. For data science teams, coefficient overrides provide a place to paste trained weights from a calibrated logistic regression. Run scenario tests by editing one signal at a time, comparing results, and storing exported CSV or PDF snapshots. This approach helps teams align on what measurable behaviors define readiness or risk.
For production, train weights on historical outcomes, apply cross-validation, and check calibration curves by cohort. Monitor drift in feature distributions, especially during seasonality or product changes. Use consistent definitions for recency and interactions across systems, and document threshold decisions with governance reviews. When using the churn preset, interpret probability as risk of leaving within your chosen horizon; pair it with intervention playbooks, measure lift after outreach, and periodically retrain to keep decisions aligned with recent customer behavior. With disciplined updates, the calculator becomes a transparent bridge between analytics and frontline action.
It is the estimated likelihood of the selected outcome given your inputs and weights. It is not a guarantee, so validate it with historical outcomes and calibration checks before using it for automation.
Yes. Enable coefficient overrides and paste your intercept and feature weights from a trained logistic regression. Keep feature definitions consistent, test on holdout data, and adjust scaling maxima to match training ranges.
Counts like sessions and interactions are usually skewed. log(1+x) compresses extreme values, reduces outlier influence, and preserves ordering for small counts, which makes contributions more stable across segments.
Base thresholds on capacity and desired precision. Raise thresholds when outreach resources are limited, lower them for broad nurture. Track lift by band regularly, and update thresholds whenever costs, margins, or volumes change.
Expected value equals probability multiplied by outcome value minus outreach cost. ROI divides expected value by cost. Use consistent currency and include incentives and labor. Treat results as directional comparisons, not a full financial plan.
After running a prediction, use Download CSV or Download PDF. Exports include inputs, probability, band, decision label, and top drivers, so you can share evidence with stakeholders and archive scenario experiments.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.