Predictive Analytics Services and ML Models

Predictive analytics for a repeated decision, not a probability nobody uses.

A forecast earns production access when it beats a simple baseline, reaches the person who can act, and stays measurable after release. We start with one outcome and decision window, test whether the history carries useful signal, and state what the model cannot know.

See our work

Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.

The brief

Start with what is not working.

Good software decisions begin with the constraint, not a list of features or a preferred technology.

01

Does the team learn about churn, demand shifts, or operating risk after the useful response window closes?

02

Is a proposed machine-learning model missing a named decision, baseline, or owner?

Plain answer

Predictive analytics services use historical data to estimate a future outcome that informs a repeated decision. RaftLabs defines the outcome and decision window, tests a simple baseline, validates candidate models on unseen history, integrates predictions into the operating workflow, and monitors drift. A focused first model starts at $20,000.

The score arrived after the team had already made the decision.

The model ranked every account by churn risk. Renewal calls still followed the old calendar because the score lived in a notebook only the data scientist opened. The prediction worked. The operating path did not.

Predictive analytics is a decision system with a model inside it.

Adjacent retention-data proof

LoyaltyPass product delivery
14 weeks
Recorded case-study duration
higher enrollment
60%
Case-reported result vs app-based programs
repeat-visit increase
3x
Case-reported outcome

The LoyaltyPass case study documents an operating product that records visit, redemption, and churn signals. It does not document a deployed predictive model. RaftLabs does not yet publish a named predictive-model outcome, so every proposed model must prove itself against the buyer's own historical baseline.

Build a predictive model when an earlier signal can change a repeated decision.

A model without a stable outcome, useful warning window, or operating owner is an experiment, not a production system.

A fit
01

The outcome and eligible population can be labelled consistently.

02

Signals exist before the decision window closes.

03

A team can act on the prediction and record what happened next.

Not a fit
01

The need is a current dashboard or one-time diagnostic analysis.

02

The process changed so completely that history no longer represents it.

03

Nobody owns the response, threshold, or fallback path.

Predictive model vs descriptive or diagnostic analytics

Predictive modelBI or data analysis
QuestionWhat is likely to happen next?What happened, what is happening, or why?
ValidationUnseen historical periods and live outcomesSource reconciliation and reproducible analysis
Operating needThreshold, response, monitoring, fallbackGoverned reporting or a bounded finding
Use whenEarlier probability changes a repeated decisionThe team needs a trusted fact or explanation

Use cases

Choose the prediction by the decision it changes

How it works

From prediction question to monitored decision support

  1. Phase 1
    01

    Frame the decision

    Define the outcome, horizon, eligible population, baseline, user, response, and cost of each type of error.

  2. Phase 2
    02

    Test the history

    Trace labels and candidate signals, then check missingness, leakage, sample size, change over time, and intervention timing.

  3. Phase 3
    03

    Validate the model

    Compare simple and advanced approaches on unseen history and choose an operating threshold with the people using it.

  4. Phase 4
    04

    Integrate and monitor

    Deliver predictions into the live workflow, observe outcomes and drift, and document when to retrain, pause, or fall back.

Risk

What a model card should make explicit

Target and horizon
State exactly what outcome counts, who is eligible, and how far ahead the prediction is made.
Baseline and validation window
Compare against the current method on periods the model did not use for training.
Error and intervention cost
Choose the threshold using the real capacity and harm of false positives and false negatives.
Drift and fallback
Define what change pauses automation, triggers review, or returns the workflow to a simpler rule.

Scope and price

A focused predictive model starts at $20,000.

Start with one outcome, one population, a baseline, validation on unseen history, delivery into one workflow, and post-release monitoring.

No accuracy ceiling is promised before the data audit. Production use is recommended only if the model beats the agreed baseline at a useful operating threshold.

Starting investment

Starts at $20,000

A focused first model usually takes eight to twelve weeks. Data repair, real-time scoring, rare outcomes, regulated evidence, and multiple populations can extend the plan.

Baseline before complexity

We test a simple rule or statistical baseline before approving a more complex model for production use.

Monitoring belongs in scope

The release includes data freshness, drift, outcome tracking, and a documented retraining or fallback decision.

Predictive analytics use cases

Common questions

Predictive analytics services use statistical or machine-learning models to estimate a future outcome, such as churn risk, demand, fraud likelihood, or equipment failure. The service includes target definition, data assessment, baseline testing, model validation, workflow delivery, monitoring, and a fallback when live conditions move outside the evidence.

Business intelligence reports recurring current and historical measures. Data analytics investigates a bounded question about what happened or why. Predictive analytics estimates a future outcome for a repeated decision. A dashboard may display the prediction, but a chart alone does not create or validate the model.

There is no responsible universal minimum. The useful amount depends on event frequency, seasonality, outcome rarity, feature coverage, business changes, and the decision horizon. We first check whether enough independent examples exist to build a meaningful validation set and whether the signals arrive before anyone must act.

Accuracy cannot be promised before testing your data and decision cost. We agree the baseline and decision-relevant measure first, then evaluate on unseen historical periods. Production use is recommended only when the model improves on the current method at an operating threshold the team can absorb.

A focused first model starts at $20,000 and usually takes eight to twelve weeks. Scope grows with data repair, rare outcomes, real-time scoring, regulated evidence, multiple populations, intervention experiments, and integration depth. Model monitoring and the retraining decision are included in the first production scope.

Work with us

Bring the outcome you want to see earlier.

Share the decision, history, current baseline, and useful warning window. We will tell you whether a model is justified or the next step is better data collection.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.