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.
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.
Does the team learn about churn, demand shifts, or operating risk after the useful response window closes?
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.
The outcome and eligible population can be labelled consistently.
Signals exist before the decision window closes.
A team can act on the prediction and record what happened next.
The need is a current dashboard or one-time diagnostic analysis.
The process changed so completely that history no longer represents it.
Nobody owns the response, threshold, or fallback path.
Predictive model vs descriptive or diagnostic analytics
| Predictive model | BI or data analysis | |
|---|---|---|
| Question | What is likely to happen next? | What happened, what is happening, or why? |
| Validation | Unseen historical periods and live outcomes | Source reconciliation and reproducible analysis |
| Operating need | Threshold, response, monitoring, fallback | Governed reporting or a bounded finding |
| Use when | Earlier probability changes a repeated decision | The team needs a trusted fact or explanation |
Use cases
Choose the prediction by the decision it changes
- 01
Churn prediction
Rank customer risk before renewal and deliver reasons into the retention workflow. - 02
Demand forecasting
Estimate demand by useful planning grain and compare the model with the current forecast. - 03
Fraud detection
Score transactions or claims at the point where review, hold, or approval can still change the outcome. - 04
Predictive maintenance
Estimate failure or degradation early enough to schedule inspection, parts, or downtime. - 05
Anomaly and operational risk
Surface unusual behavior only when an owner, threshold, and investigation path are defined.
Facilities and building operations
A CMMS owns assets, maintenance plans, work, labour, parts, and history. The BMS owns current signals and alerts. Predictive analytics estimates a defined future event or priority from evidence. Many facilities need stronger asset identity, monitoring, and work-order outcomes before a model can add value, and the main risk of monitoring alone is alarm noise with poor follow-up.
The failure mode is familiar: the BMS records a high temperature, a technician replaces a component, and the CMMS closes the task under a generic reason. Six months later the same pattern appears, and the systems still cannot connect signal, asset, intervention, and outcome. Prediction requires that connection. A dependable threshold with an owned work-order path beats an impressive score nobody can validate.
A credible facilities analytics release starts with one asset class, one failure or degradation event, one decision, one useful horizon, and one intervention owner. Building and maintenance data is mapped across BMS, sensors, historian, CMMS, work orders, inspections, weather, occupancy, and energy sources. Units, timestamps, quality, source, maintenance windows, replacements, and configuration changes are preserved. The proposed model is compared against a simple rule, schedule, or current practice, with data split by time and asset so future information cannot leak into training, and lead time, misses, false alerts, and cohort performance reported honestly. Review is built as a maintenance handoff. The signal, contributing evidence, uncertainty, asset context, and recommended review go to authorised facilities staff, who accept, reject, defer, or annotate it. A CMMS work order is then created or enriched through a controlled interface.
The honest first release is often monitoring, not prediction. When representative history for the target event is missing, begin with monitoring, data quality, and CMMS workflow rather than promising forecasts. Predictive maintenance pays only when alerts become work orders: choose the path that closes the loop to a completed repair, because value comes from acting on predictions, not monitoring. The model supports maintenance judgment; it does not authorise safe operation.
How it works
From prediction question to monitored decision support
- Phase 101
Frame the decision
Define the outcome, horizon, eligible population, baseline, user, response, and cost of each type of error.
- Phase 202
Test the history
Trace labels and candidate signals, then check missingness, leakage, sample size, change over time, and intervention timing.
- Phase 303
Validate the model
Compare simple and advanced approaches on unseen history and choose an operating threshold with the people using it.
- Phase 404
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
Monitoring belongs in scope
Predictive analytics use cases
- 01
Churn Prediction
Customer-risk scoring tied to retention capacity, intervention timing, and CRM delivery.
- 02
Demand Forecasting
Time-series forecasts tied to planning grain, inventory decisions, and forecast overrides.
- 03
Fraud Detection
Risk scoring where false positives, review queues, and decision latency shape the model.
- 04
Predictive Maintenance
Failure prediction tied to sensor history, inspection capacity, parts, and downtime planning.
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.
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.
The three common failures are no decision owner (the score arrives but nobody is accountable for acting on it), no warning window (the prediction comes too late to change the outcome), and silent degradation (the world changes, the model does not, and nobody notices for a quarter). We treat the operating loop — owner, timing, monitoring — as part of the build, not as a handover concern.
Every production model ships with monitoring on input drift, prediction distribution, and outcome feedback. When metrics move outside agreed bounds, the runbook triggers investigation before retraining — because retraining on corrupted or shifted data bakes the problem in. The retraining decision, its cadence, and its owner are defined in the first production scope, not discovered six months later.
A useful project needs stable asset identity, a path for current signals, and records of maintenance decisions or outcomes. Prediction also needs representative history for the target event and horizon. If that evidence is missing, begin with monitoring, data quality, and CMMS workflow rather than promising forecasts.
No system can guarantee prevention. Analytics may identify patterns worth inspection or intervention, but misses, false alerts, unseen failure modes, maintenance quality, parts, weather, occupancy, and human response all affect outcomes. Facilities and engineering owners decide what to inspect, repair, defer, or shut down.
It pays only when alerts become work orders. Choose the path that closes the loop to a completed repair; value comes from acting on predictions, not monitoring. Predictions should flow straight into work orders through a controlled CMMS interface instead of adding another monitoring dashboard.
Not always. Building-focused tools can analyze existing BMS and meter data with no new hardware; industrial tools need retrofit vibration and current sensors, so factor in hardware and installation. Confirm BMS protocol support, BACnet, Modbus, OPC, MQTT, before you buy. Cheap predictable servicing: preventive. Costly, invisible failures: predictive. Blend wins: preventive schedules as the baseline for the bulk of assets, predictive layered on the critical few.
Almost nobody publishes list pricing, and pricing models are not comparable: enterprise credits, per-sensor subscriptions, fixed-scope engineering. Compare cost to first result and cost per additional asset instead of headline price. Price the data work separately from the modeling. Always model three-year total.