Scope

ClickMasters builds predictive analytics systems for B2B companies across the USA, Europe, Canada, and Australia. Churn prediction that identifies at-risk customers 30-90 days before they cancel. Demand forecasting that reduces inventory waste and stockouts. Lead scoring that ranks your pipeline by close probability. Anomaly detection that surfaces fraud and operational issues in real time. Built on scikit-learn, XGBoost, and LightGBM deployed as production APIs your applications can call.

Predictive Analytics Services

The ML Development Process for Predictive Analytics

SHAP Model Interpretability

SHAP (SHapley Additive exPlanations) is a framework for explaining individual ML model predictions based on game theory's Shapley values. For each prediction, SHAP calculates how much each feature contributed to pushing the prediction above or below the baseline (the average prediction across all examples). This enables two types of explanation: global explanations (overall, which features drive the model's predictions the most important business intelligence from the model) and local explanations (for this specific prediction, why did the model score this customer as high-risk "decreased login frequency contributed -0.23, support ticket increase contributed +0.19"). SHAP is essential for B2B predictive analytics because business stakeholders need to understand why a model made a specific prediction before they act on it and because regulators in many industries require model decisions to be explainable.

What this engagement delivers

Churn Prediction

Per-customer churn probability score enabling proactive retention outreach. Feature engineering from event data (product usage frequency, feature adoption, support tickets, billing history, contract proximity). Gradient boosting (LightGBM/XGBoost) on labeled examples. SHAP explanations for each at-risk account. Daily scoring pipeline with CRM/Slack alerts.

Demand Forecasting

Time series forecasting for inventory management, workforce planning, capacity optimization. Lag features, rolling statistics, calendar effects. LightGBM with time-series CV or Prophet/N-BEATS/TFT. Confidence intervals via quantile regression or conformal prediction. Day-ahead to 12-week-ahead horizons.

Lead Scoring & CLV

Replace static rule-based scoring with ML trained on historical win/loss. Features: firmographic (company size, industry, tech stack), behavioural (pages visited, content downloads, email engagement), CRM history (stage progression, time-in-stage). Output: conversion probability + SHAP attribution per lead. CLV prediction for acquisition budget allocation.

Anomaly Detection

Real-time and batch anomaly detection for fraud, operational monitoring, QC. Statistical (Z-score/IQR), Isolation Forest (unsupervised fast, effective without labeled anomalies), autoencoder (neural network, high reconstruction error for anomalies), or LSTM for time series. Streaming on Kafka with PagerDuty/Slack alerts.

Propensity Models

Probability that a customer/prospect will take a specific action: upsell propensity (which customers likely to upgrade), cross-sell propensity (adjacent product purchase), campaign response propensity, reactivation propensity (churned customers likely to return). Ranked scores integrated with CRM for sales/marketing prioritization.

Process

Phase 1 · Week 1
Data Audit
Review available data sources, assess data quality (completeness, consistency, recency), identify labeling approach, calculate baseline rate, confirm sufficient sample size (minimum 500-1000 positive examples). Deliverable: Data Audit Report with feasibility assessment.
Phase 2 · Week 2-4
Feature Engineering
Extract and transform raw data into model-ready features: event aggregation (count, sum, mean over time windows), time-based features (recency, tenure, days-since), ratio features, encoding (one-hot, target), missing value strategy. Deliverable: Feature pipeline code.
Phase 3 · Week 3-5
Model Development
Time-based train/validation/test split (never random for time series), baseline model (logistic regression), candidate models (LightGBM, XGBoost, Random Forest), hyperparameter tuning (Optuna Bayesian, 100-300 trials), evaluation (AUC-ROC, F1, precision-recall, calibration curve). Deliverable: Model Comparison Report.
Phase 4 · Week 5-6
Interpretability
SHAP values: global feature importance, local explanations (per-prediction), SHAP summary plots. Partial Dependence Plots for non-linear relationships. Deliverable: Model Explainability Report.
Phase 5 · Week 6-8
Production Deployment
FastAPI prediction endpoint (input: features â†’ output: probability + SHAP), batch scoring pipeline (daily/weekly updates to CRM), monitoring setup (prediction distribution drift alerts), A/B test design (business impact measurement). Deliverable: Production API + monitoring dashboard.

Tech stack

Core Libraries: scikit-learn, XGBoost, LightGBM, Prophet, statsmodels
Time Series: Darts, PyTorch Forecasting (TFT, N-BEATS), Nixtla, statsmodels
Feature Engineering: pandas, numpy, Feature-engine, Feast
Model Tuning: Optuna (Bayesian optimization), Hyperopt, GridSearchCV
Interpretability: SHAP, LIME, Eli5, Partial Dependence Plots
Deployment: FastAPI, MLflow, BentoML, SageMaker Endpoints
Monitoring: Evidently AI (drift), custom Prometheus + Grafana

How we work

30-90 Day Advance Warning

Churn models that give retention teams time to intervene | Basic: Post-hoc analysis with no proactive window

SHAP Interpretability

Every prediction explained "why is this customer at risk?" | Basic: Black-box predictions with no explanation

Time-Based Evaluation

Never random splits temporal cutoffs simulate real prediction scenarios | Basic: Optimistic metrics from random splits

Confidence Intervals

Quantile regression or conformal prediction decision-makers need ranges, not point estimates | Basic: Point forecasts only (no uncertainty quantification)

Business Validation

Translate model performance to business outcomes before deployment | Basic: Technical metrics only (no business impact projection)

Where this is used

SaaS Churn Prediction

Predict which subscription customers will cancel. 30-90 day advance warning. Feature engineering from product usage, support tickets, billing history. SHAP explanations for each at-risk account.

Retail Demand Forecasting

Forecast product demand by SKU and location. Reduce inventory waste and stockouts. Multi-horizon forecasts with confidence intervals.

B2B Lead Scoring

Rank sales pipeline by conversion probability. Replace static rules with ML trained on historical win/loss. SHAP explanations for each lead.

Fraud Detection

Real-time anomaly detection on transaction streams. <100ms latency. Isolation Forest or autoencoder with threshold calibration.

Pricing guidance

These ranges are planning anchors. The quote names scope, timeline, and what is excluded. Talk through a quote.

Data Feasibility Audit

$3,000 - $7,000

1 - 2 weeks

Data review, label definition, baseline analysis, feasibility report

Churn Prediction Model

$12,000 - $35,000

4 - 8 weeks

Feature engineering, LightGBM/XGBoost, SHAP, CRM integration, monitoring

Demand Forecasting

$12,000 - $35,000

4 - 8 weeks

Time series features, model selection, quantile intervals, forecast API

Lead Scoring / CLV Model

$10,000 - $30,000

3 - 7 weeks

Firmographic + behavioural features, conversion model, SHAP, CRM sync

Anomaly Detection

$10,000 - $30,000

3 - 7 weeks

Isolation forest or autoencoder, streaming or batch, alert routing

Propensity Model Suite

$15,000 - $45,000

4 - 9 weeks

Upsell/cross-sell/response/reactivation 2-4 models with shared feature pipeline

Full Predictive Stack

$25,000 - $80,000

6 - 14 weeks

Multiple models + feature store + batch pipeline + production APIs + monitoring

ML Retainer

$4,000 - $10,000/mo

Ongoing

Retraining, drift monitoring, new features, model iteration

FAQs

What is predictive analytics and how does it differ from reporting?

Reporting describes what happened dashboards, charts, and aggregations of historical data. Predictive analytics uses statistical and ML models trained on historical data to estimate the probability of a future outcome. A dashboard tells you that 15% of customers churned last quarter. A churn prediction model tells you which specific customers are at risk of churning in the next 30 days enabling proactive intervention before the churn happens. Predictive analytics does not replace reporting it adds a forward-looking layer that converts historical patterns into actionable probability scores that operations, sales, and customer success teams can act on.

How much data do I need to build a predictive model?

The minimum viable dataset depends on the prediction task, but a practical rule of thumb for binary classification (churn/no-churn, convert/no-convert): at least 500-1,000 positive examples (churned customers, converted leads) in your training dataset. Below this threshold, models typically overfit and do not generalise reliably. For time series forecasting (demand, revenue), a minimum of 2 full seasonal cycles (24 months for monthly data, 730 days for daily data) is recommended to capture seasonal patterns. Data quality matters more than quantity a clean, complete dataset of 2,000 examples consistently outperforms a noisy, inconsistent dataset of 20,000. ClickMasters performs a data feasibility audit as the first step of every predictive analytics engagement.

What is SHAP and why does it matter for ML model interpretability?

How do you ensure a predictive model performs well in production?

Production ML model performance is maintained through three practices. Evaluation methodology: time-based train/test split (simulate real production conditions by training on past data and evaluating on future data never use random splits for time-sensitive predictions, which produce optimistic metrics that don't reflect real performance). Calibration: verify that the model's stated probability matches the actual frequency (a model that says "70% churn probability" should be right 70% of the time use isotonic regression or Platt scaling to calibrate if needed). Monitoring: track the score distribution of new predictions vs. the training distribution when they diverge significantly (data drift or concept drift), retrain on more recent data. ClickMasters implements monitoring dashboards with automated alerts on every production model.

Predictive Analytics

Built to move from idea to production.