Custom Predictive Analytics System with ML Models

We integrate predictive ML models into your operational processes using predictive analytics techniques. Our predictive analytics system combines ML models for churn prediction, demand forecasting, and risk analysis, all integrated with your CRM via a feature store and ML pipeline. We use SHAP values for interpretability and time series forecasting for demand. Within 8–12 weeks, you get a working prototype with 2–3 models; in 5–7 months, a full platform with predictive analytics, Feature Store, and automated monitoring. We reduce costs by 15–30% — in financial terms, that's 2–10 million rubles annually for a medium business. Typical project cost ranges from $50,000 to $200,000, with typical ROI between 300-500% in the first year. We'll assess your project in 1–2 days — contact us for a consultation.

With 5+ years of experience and 20+ successful projects, we guarantee 15-30% cost reduction. For example, for a retail client we reduced inventory costs by 25% within 3 months, and for a financial institution we cut fraud false positives by 60%.

How do we ensure centralized architecture and real-time accuracy?

Each model uses the same data: transactions, logs, IoT streams. A Feature Store (Feast/Hopsworks) becomes the single source of features. For example, 'number of visits in 7 days' is computed once and used in churn, LTV, and fraud models. This cuts development time by 40% — 1.67x faster than manual feature engineering — and eliminates feature inconsistencies. Without a Feature Store, each team recalculates identical features, leading to duplication and errors in production.

We automate model retraining on a schedule (weekly for volatile data, monthly for stable). Each run includes data quality checks via Great Expectations and an A/B test: the new model is compared against the champion version. If metrics drop, automatic rollback. We also monitor data drift (PSI > 0.25 triggers an alert) and concept drift (accuracy on a 30-day rolling window). As a result, classification accuracy stays above 85%, and regression MAE stays below 12%.

What predictive tasks and methods do we use?

We go deep into 2–3 tasks to ensure >85% accuracy rather than spreading thin across 10 superficial models.

Comparing Forecasting Methods:

Prophet wins in transparency but lags behind XGBoost on sparse data. LSTM excels on complex time series but is compute-intensive. In practice, we often combine: start with XGBoost, then fine-tune with LSTM if enough data.

Data Pipeline and System Architecture

Feature engineering includes aggregates over windows of 7, 30, 90, 365 days, RFM patterns, and embedding vectors for high-cardinality categorical features. Example: for churn prediction, we compute 'support tickets in 30 days', 'average ticket in 90 days', 'days since last purchase'. All features live in the Feature Store and update via sliding windows. The pipeline is orchestrated with Apache Airflow with versioned DAGs.

┌─────────────────────────────────────────────────────┐
│  Data Sources: ERP, CRM, IoT, Logs, External APIs   │
└─────────────────────┬───────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────┐
│  Data Platform: Data Warehouse + Feature Store       │
│  (Snowflake/BigQuery + Feast/Hopsworks)             │
└─────────────────────┬───────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────┐
│  ML Platform: Training + Serving                    │
│  (MLflow + Ray + Seldon/BentoML)                   │
└─────────────────────┬───────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────┐
│  Activation Layer: CRM hooks, Alerts, Dashboards    │
└─────────────────────────────────────────────────────┘

Model Interpretability and Integration

SHAP values are the standard for explainability. For each prediction, we output the top 5 drivers and compare against baseline. For example: 'Customer will churn with probability 0.85: support tickets dropped 60%, last purchase 45 days ago.' This allows the sales manager to take targeted action — not just 'notify', but offer a specific retention product. Without interpretability, business users don't trust the model — SHAP solves that.

Predictions are delivered to the decision point:

• Salesforce: predictive score in the contact card via API
• HubSpot: custom property with churn risk, automatic workflow triggers
• SAP S/4HANA: predictive equipment failure warnings
• Custom systems: REST API + webhooks for real-time forecasts

Alerts: when churn probability > 0.7 — automatic task for the account manager. When failure probability > 0.8 — notification in CMMS.

What's included in the work

• Data and business process audit, target metric definition
• Feature engineering and Feature Store development
• Model training and validation (PyTorch, XGBoost, Prophet)
• CRM/ERP integration via API
• Drift monitoring and automated retraining
• Model documentation and client team training
• 3 months of post-production support
• Access to model dashboards and APIs
• Weekly progress reports during development

Common Pitfalls and Timeline

Neglecting data quality: Garbage in, garbage out. Always validate data with Great Expectations before training.

Ignoring feature drift: Features that were predictive last month may not be today. Automate drift detection.

Lack of business interpretability: A black-box model is useless if stakeholders can't understand its decisions. Always provide SHAP explanations.

Overfitting on historical patterns: Use time-based cross-validation and monitor out-of-sample performance.

Timeline:

1. Assessment (1–2 days): We analyze your data sources and business goals, provide a feasibility estimate.
2. Prototyping (4–6 weeks): Build 2–3 models with basic features, deliver an MVP.
3. Full platform (5–7 months): Scale to Feature Store, integrate with CRM/ERP, implement monitoring.
4. Post-production (3 months): Handover, support, and iterative improvement.

Get a free preliminary assessment — contact us for a consultation. We'll calculate the economic impact in 2 days and prepare a commercial proposal.

When does a time series forecasting model fail in production?

The CFO requests a quarterly sales forecast. An analyst builds SARIMA on three years of data, achieves MAPE 8.3% on the test set, and deploys. Two months later, the metric in production jumps to 23%. The root cause: the model was trained on pre‑COVID data, tested on a stable period, but production hit a promotion and supply chain disruption. Data leakage plus distribution shift—perfect notebook numbers, a broken forecast in reality. We have seen this pattern dozens of times across retail, fintech, and IoT. Our team has delivered more than 50 forecasting projects over 5+ years.

Incorrect cross-validation. Standard train_test_split for time series creates data leakage: the model sees future values during training. The correct approach is TimeSeriesSplit or walk‑forward validation with an expanding window.

Multiple seasonality. Hourly electricity consumption has three seasonalities: daily (24h), weekly (168h), yearly (8760h). SARIMA handles only one. Prophet can handle multiple but scales poorly to thousands of series.

Missing values and anomalies. A missing sensor reading is information (the sensor turned off), not NaN. Linear interpolation destroys this signal. Proper handling depends on the missingness mechanism.

Cold start. A new SKU in a 50,000‑item assortment has no history, yet a forecast is needed. Standard approaches fail; cross‑learning or feature‑based methods are required.

Why is model selection critical for your data?

Prophet (Meta) – a solid start for business data with clear seasonality and holidays. Fast setup, interpretable, built‑in outlier detection. Fails on irregular patterns and does not scale beyond ~10k series without parallelization.

Gradient boosting on features (LightGBM, XGBoost) – often underestimated. Engineer lags (t‑1, t‑7, t‑28), rolling means, day‑of‑week, holidays. The model trains on all series simultaneously, solving cold start via transfer learning. MAPE in retail often beats neural nets with proper feature engineering.

TFT (Temporal Fusion Transformer) – a transformer designed for interpretable forecasting with covariates. Built‑in variable selection, temporal attention, quantile outputs. Available in pytorch‑forecasting. Requires ~10,000+ records per series for stable training.

PatchTST – splits the series into patches (like ViT for images), capturing local patterns better than classic transformers. Excellent for long‑horizon forecasting (96–720 steps ahead).

N‑HiTS, N‑BEATS – attention‑free neural architectures, faster than TFT, competitive accuracy. N‑BEATS won the M4/M5 benchmarks for tasks without covariates.

How do we deploy TFT in production?

A typical pipeline via pytorch‑forecasting:

training = TimeSeriesDataSet(
    data,
    time_idx="time_idx",
    target="sales",
    group_ids=["store", "sku"],
    min_encoder_length=max_encoder_length // 2,
    max_encoder_length=max_encoder_length,  # 120 days
    min_prediction_length=1,
    max_prediction_length=max_prediction_length,  # 28 days
    static_categoricals=["store_type", "category"],
    time_varying_known_reals=["price", "promo_flag"],
    time_varying_unknown_reals=["sales"],
    target_normalizer=GroupNormalizer(groups=["store", "sku"], transformation="softplus"),
)

A common mistake: the default target_normalizer (StandardScaler) breaks predictions for series with zero values (no sales on weekends). GroupNormalizer with transformation="softplus" is the correct choice for count data.

Case study: retail demand forecasting

A chain of 120 stores, 8,000 SKUs, 28‑day forecast horizon. The original system: SARIMA per series, MAPE 18.4%, retraining cycle – 6 hours. We replaced it with TFT on PyTorch + pytorch‑forecasting: a single model for all series, MAPE 11.2%, retraining – 40 minutes on an A10G. Feature importance via variable selection revealed that day_before_holiday influences more than the holiday date itself. Annual savings on inference alone exceeded $50,000.

Step‑by‑step configuration

1. Data collection and preparation. Handle missing values (mark NaN, interpolate only for technical failures), aggregate to required frequency, engineer covariates (holidays, promotions, prices).
2. Create TimeSeriesDataSet. Set group_ids (store + SKU), time index, forecast horizon. Choose target_normalizer based on target distribution.
3. Train a baseline. Prophet or LightGBM first – to understand complexity.
4. Train TFT. Use TemporalFusionTransformer with loss=QuantileLoss(), tune learning rate and hidden layer sizes.
5. Validate and interpret. Walk‑forward test, analyze variable selection, build attention heatmaps.

How to properly evaluate forecast quality?

RMSE alone is misleading – it over‑penalizes large values. Our standard set:

• MAPE – interpretable, unstable near zero.
• sMAPE – symmetric, avoids division by small numbers.
• MASE (Mean Absolute Scaled Error) – normalized relative to a naive seasonal forecast, ideal for comparing series of different scales.
• Pinball loss – for probabilistic forecasting, inventory management.

We guarantee a model card with these metrics on the validation set and walk‑forward results on at least 6 months of history.

What deliverables do you receive?

• Documentation of chosen architecture and hyperparameter rationale.
• Reproducible training and inference pipeline (Docker + CI/CD + Airflow/Prefect).
• Committed code with unit tests for key components.
• Team training: retraining, output interpretation, deployment of new versions.
• 3 months of post‑delivery support (consultations, bug fixes, fine‑tuning).

The model is deployed via FastAPI or Triton Inference Server. Retraining is scheduled (e.g., weekly) via Airflow with drift validation and automatic rollback if metrics deteriorate.

Process and timeline

We start with EDA: visualization, ADF test, STL decomposition, analysis of missing values and outliers. This takes 2–3 days but often reveals systemic data issues that block forecasting. Then we build a baseline (naive seasonal, Prophet), engineer features for LightGBM, and select a neural architecture if needed. Walk‑forward validation with a realistic horizon. Deployment via API with automatic retraining scheduled via Airflow or Prefect.

Timeline: MVP forecast on one data type – 3–6 weeks. Hierarchical forecasting system with automation – 2–5 months. Cost is calculated individually based on data volume, number of series, and required accuracy.

Our team consists of certified ML engineers (AWS ML Specialty, GCP Professional ML Engineer) with 5+ years on the market and over 50 completed forecasting projects. Contact us for a free analysis of your data – we will assess the task and provide initial recommendations within 1–2 days. Request a consultation to ensure your forecasts work in production, not just in a notebook.