All Case Studies
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.
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 StudyMid-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.
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 StudyWhy choose Dcrayon for Predictive Analytics
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.
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.
HOW WE WORK
How Dcrayon works with the brands we partner with
A short film on how we work: the people you deal with, how we report, and the results we hold ourselves to.
What we build
Three kinds of model work, each judged on the decision it improves rather than its accuracy on a holdout set.
Step 1: Demand and revenue forecasting
Time series in production Prophet and gradient-boosted models backtested against your own history, with confidence ranges planners can act on.
Step 2: Churn and uplift scoring
Who to act on, and why Scores that rank customers by the lift an intervention would create, rather than by how likely they were to leave anyway.
Step 3: MLOps from notebook to pipeline
Retraining and drift Versioned experiments, a retraining schedule and drift alerts, so the model that shipped is the model still running.
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.
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.
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.
Growth Intelligence (predictive analytics axis)
A six-dimension 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 Engine (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 Growth Intelligence 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.
Get a readout of your forecasting setup
We backtest your current forecasts against your own history and send a written diagnostic with a fixed estimate.
Other Cloud and Data solutions
Sibling Dcrayon services inside the Cloud and Data category. Programs clients often layer alongside Predictive Analytics.
WHY DCRAYON
Why teams keep the modelling with us
Four commitments that separate a deployed model from a demo.
Lift on a decision, not notebook accuracy
Every model is scored against the decision it supports and reported in money, because accuracy alone changes nothing.
Leakage is checked before we quote
A written audit of data quality and leakage risk comes first. A model trained on the answer is worse than no model.
The data scientist who scopes it builds it
The person who designs your churn or forecasting model is the one who ships and maintains it.
Retraining is part of delivery
Drift alerts and a retraining pipeline ship with the model, not as a follow-on project after it degrades.
Real client work, with the receipts
200%
Improvement in conversion performance
Proven Water Industries
Proven's growth on Amazon over the engagementDcrayon's work on Proven's Amazon account has run as a long-term growth programme rather...
Keratine Professional
What is in place on Keratine Professional's accountOn Amazon.in today, the brand's lead listings name the product type, the hair concern...
3,400%
Sales growth on Amazon India
GetSetNova
Sales up 3,400% on Amazon IndiaOver the engagement, GetSetNova's sales on Amazon India are up 3,400%, and the brand holds Amazon Best...
McCormick & Company
What changed on McCormick's front endThe front end now loads non-essential scripts after the main content, runs third-party tags through...
Roundglass
What changed for Roundglass's HR team, IT and new employeesAccount creation, group membership and licence assignment now follow the...
GRYT Health Labs
What GRYT shoppers can do on the storeShoppers can check the brand's licence and ownership details, compare single units with packs of...
Daddy of Tastes
What the Daddy of Tastes kitchens and riders work withCustomers can order from the full menu, pin their exact location, pay through...
Instacertify
What Instacertify's visitors could doVisitors could search for a certification from the first screen, see two clear next steps, and move...
Northern Tile Distributors
The storefront delivers a clean shopping experience on both desktop and mobile. Page load times improved significantly, the add-to-cart rate...
RAA Logistics
What RAA's customers and team have nowProspective customers can find the exact service they need, read what it covers, see who runs the...
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.
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