Reproducible model lineage
Every model ships with data, code, and hyperparameters versioned in MLflow, plus a validation report, holdout metrics, and a rollback path your auditors can trace.
Our predictive analytics clients act on forecasts they actually trust metrics
Two recent predictive analytics engagements. Model accuracy and backtests under MNDA; full walkthrough on a scoping call.
Cloud and Data engagement,
Forecasting and propensity modeling lead
Dcrayon rebuilt our churn and demand models around the decisions our operators make weekly. Every forecast now ships with a confidence range and a backtest finance can check.
MNDA
Forecast error reduction shared on call
90 days
Churn model precision under MNDA
Predictive analytics engagement: a senior data scientist, feature engineering on the client's own transaction history, and gradient-boosted models with monthly drift checks.
Read Cloud and Data engagement's Case Study
Mid-market cloud and data brand,
Demand forecasting and churn analytics lead
Our forecasts drifted for over a year and no one trusted them. Dcrayon retrained the models on clean features and gave us AUC and MAPE numbers we can defend to the board.
MNDA
Revenue at risk flagged, on scoping call
90 days
Backtested lift shared under MNDA
Predictive analytics engagement paired with the client's cloud data warehouse. A baseline model and holdout test in the first sprint set the accuracy bar.
Read Mid-market cloud and data brand's Case StudyHOW DCRAYON PREDICTIVE ANALYTICS WORKS
How a Dcrayon predictive analytics program sequences its first 90 days
A short walkthrough of a Dcrayon predictive analytics engagement, from the week-one data and model audit to a retraining pipeline running and monitored in production.
What we engineer into every predictive analytics model
What comes standard on every Dcrayon predictive analytics engagement
Each capability below is built and validated by the data scientist assigned to your account, then handed over with documentation your team can run and extend.
Live drift monitoring
DcrayonAI watches feature and prediction drift, flags when input distributions shift, and opens a retraining ticket before model accuracy quietly decays.
Inference cost tracking
We tag training and inference cost per model and report cost per thousand predictions, so an expensive model that adds little lift gets retired early.
Compliance-aware modelling
Feature pipelines mask PII, every scored decision is logged for audit, and predictive analytics financial services models keep documented reason codes for each score.
Three methods we bring to every predictive analytics engagement
Score, Sequence, Repair. A data and model audit, a written build plan, and an internal toolkit we run on every predictive analytics project.
Dcrayon Score (predictive analytics axis)
A 150-factor readiness review. The predictive analytics part checks your data pipeline health, feature quality, forecast accuracy against a holdout window, model drift monitoring, and how far scores reach into daily decisions. Free on every proposal call.
Dcrayon Growth Formula (predictive analytics sequencing)
A build plan that ties every model back to one decision you name, whether that is reducing churn, forecasting demand, or ranking credit and fraud risk.
DcrayonAI (predictive analytics toolkit)
An internal toolkit that profiles your datasets, checks for leakage and class imbalance, and lists the model work worth funding first.
How we run predictive analytics work
Three repeatable steps that keep predictive analytics models accurate as your data changes.
Step 1: Score
A free Dcrayon Score readout, ready in a business day. We backtest your current forecasts against held-out data, flag label leakage and stale features, and hand you one 0-100 number plus the fix list. No follow-on commitment.
Step 2: Plan
A written 90-day plan built around one forecast you name: demand, churn, or cash flow. A senior data scientist owns the model, backtests it before it ships, and every SoW stays cancellable. No annual lock-in.
Step 3: Compound
Weekly working sessions with your data scientist plus a monthly readout your finance team can act on. The work stacks: clean features in month one sharpen the model in month two, and monitored retraining holds accuracy steady in month three.
Other Cloud and Data solutions
Sibling Dcrayon services inside the Cloud and Data category. Programs clients often layer alongside Predictive Analytics.
Why choose Dcrayon for Predictive Analytics
A senior data scientist on every account
The data scientist who scopes your churn or forecasting model is the one who builds and monitors it, not a junior handed the ticket later.
A data and model audit before we quote
A written diagnostic of your data quality, leakage risk, and monitoring gaps, with a fixed estimate, before any build commitment.
MLOps built in from the start
Every model ships with a retraining pipeline, drift alerts, and versioned experiments in MLflow, not a one-off notebook that rots after handover.
Readouts your finance team can read
A monthly report that shows model lift against the decision it supports, in money and units, without machine-learning jargon.
Dcrayon has built forecasting and scoring models for brands since 2016, run from our Delhi HQ and Sheridan, Wyoming office. Our predictive analytics services pair AWS, Azure, and GCP-certified architects with data scientists who ship models to production, not slide decks. AI-first since the 2024 pivot.
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The predictive analytics Score is free. The written model plan is yours to keep.
Across our 60+ active solutions retainers, teams running on gut-feel forecasts instead of tested models tend to misread demand and cash. The 12-month cost we measure lands in the Rs 6L to Rs 90L band. Book the scoping call to size yours.
Free predictive analytics Score that audits your data and any existing models
Written plan tied to one decision you pick, from churn to demand forecasting
Mutual exit clause in every SoW, no annual lock-in
A senior data scientist on your account from the first working session
Predictive Analytics FAQs
Kickoff depends on data access and scope. Once we have read access to the relevant data and a named decision to model, the first audit and feature work begins.
Both. Some clients hand us the full modelling function; others use us as senior data science support and escalation for an in-house team. We scope per account.
Most predictive analytics engagements start at Rs 4 to 8 lakhs per month in India or USD 6 to 15 thousand per month globally. Audit-only engagements cost less.
Yes. Every proposal call includes a free predictive analytics Score that reviews your data and existing models. No follow-on commitment required.