The churn nobody flagged until the cancellation email.
The account had been slipping for months. Logins down, support tickets sharper, the champion who used to reply in an hour gone quiet. Nobody saw it as a pattern, because nobody was looking for one, the signals lived in three different systems and no one connects them until renewal week, when the cancellation email arrives and it's already too late to save.
The same shape repeats everywhere: the stockout nobody forecasted, the fraud pattern nobody flagged until the chargebacks piled up. The data to see it coming already existed. Nothing was built to turn it into a forward signal before the decision window closed.
Predictive analytics, by the numbers
01
- to a validated first model, then iterate
- ~10 wks
02
- rated by clients on Clutch
- 4.9/5
03
- shipping software and AI systems since
- 2015
Predictive analytics is the third of four types of analytics work. Descriptive analytics answers what happened (that's business intelligence). Diagnostic analytics answers why it happened (that's data analytics). Predictive analytics, this page, answers what's likely to happen next, and with what confidence. Prescriptive analytics, what to do about it, gets built into the operational system the prediction feeds.
The direct cost of reacting to churn is the revenue you lose. The indirect cost is the marketing spend trying to replace it. The cost of reacting to a demand spike is the stockout and the expedited shipping. The cost of missing a fraud pattern is the dispute rate.
Research from Bain & Company (Frederick Reichheld) found that increasing customer retention rates by just 5% increases profits by 25% to 95%, depending on the industry. That's an old, well-established finding, not a fresh statistic, but the mechanism hasn't changed: a small improvement in catching problems before they compound has an outsized effect on the bottom line.
In August 1998, Long-Term Capital Management, a hedge fund co-founded by two Nobel laureates, lost $4.6 billion in under four months. Its trading models were calibrated on roughly five years of historical correlation data, a window that happened to exclude the kind of shock the 1997 Asian financial crisis and Russia's 1998 default turned out to be. Historian Niall Ferguson's assessment: the loss was, by the models' own assumptions, "so unlikely that it ought never to have happened in the entire life of the universe."
The lesson isn't "don't trust models." It's that a model is only as good as the range of reality it was trained to expect. A churn or demand model trained on 12-18 months of data will break the same way the first time something outside that window happens, unless it's built to be monitored and retrained, not just deployed and left alone.
Predictive analytics doesn't eliminate uncertainty. It shifts the balance, from finding out after the fact to having enough signal to act before the cost lands.
Predictive analytics pays off when a real decision is waiting on the answer.
Everything on the left should already be true for your use case. Even one thing on the right, and a simpler dashboard or manual process covers what you need for now.
A fit0112-18 months of historical data on the outcome you want to predict, plus the signals that plausibly influence it.
02A specific, recurring decision the prediction would change, who to call before renewal, how much stock to hold, which transaction to flag.
03A team ready to act on a probability, not just a data point, and willing to trust a confidence score enough to change behavior.
Not a fitYou want a dashboard that describes what already happened, that's business intelligence or data analytics, not this.
The underlying process is closer to random than patterned, no model will reliably predict it, and we'll tell you that during discovery.
Nobody on your team will change what they do based on the prediction, a model nobody acts on is a wasted build regardless of its accuracy.
Capabilities
What we build
Demand forecasting models for inventory replenishment, staffing, and capacity planning: the decisions where being wrong by 20% has direct cost. Trained on your transaction history plus seasonality, promotional calendars, and external signals like weather, with the model selected to fit your data rather than applied by default. Forecasts flow into your inventory or scheduling systems via API, with confidence intervals so your team knows when to trust them. Go deeper: demand forecasting.
- Built with
- Prophet · SARIMA · LSTM
02Customer churn prediction
Churn risk scoring for every customer or account, refreshed weekly or monthly, so your retention team works from a current prioritized list rather than gut feel. Models learn from your churn history and the behavioral signals that predict it: login trends, feature usage breadth, support ticket sentiment, and payment delays. Scores land in your CRM on every account dashboard, and segment analysis shows which customer profiles churn earliest. Go deeper: churn prediction.
- Built with
- Salesforce · HubSpot · Gainsight
03Sales and revenue forecasting
Pipeline-to-close probability models trained on your historical CRM data and win/loss outcomes, not the default stage percentages your CRM assigns. The model learns what predicts closes in your sales motion: deal age at each stage, engagement signals, and rep conversion rates. Feature attribution explains why each deal got its score, and forecasts aggregate to account, region, or rep level with confidence intervals leadership can plan against.
- Built with
- SHAP
04Anomaly and fraud detection
Statistical anomaly detection for the operational data streams where unusual patterns signal a problem worth investigating: equipment sensor drift, account compromise, and process bottlenecks, alongside dedicated fraud-transaction scoring. The model is chosen based on whether anomalies are point, contextual, or collective. Alerts route to your operations or fraud team with the specific signal and supporting context, not a generic flag that needs expert interpretation. Go deeper: fraud detection.
- Built with
- Isolation Forest · Autoencoder · LSTM
Equipment failure prediction for manufacturing lines, logistics fleets, and facility systems, wherever unplanned downtime has material cost. Models learn from vibration, temperature, and current-draw sensors combined with your maintenance records and failure history. Remaining useful life estimates enable condition-based scheduling instead of fixed intervals or run-to-failure, and integration with your CMMS creates work orders automatically when RUL drops below threshold. Go deeper: predictive maintenance.
- Built with
- IBM Maximo · SAP PM
06Recommendation and personalization models
Recommendation systems that surface the most relevant products, content, or next actions for each user based on behavior history, with content-based models for cold starts and hybrids weighted by context. An A/B testing framework with holdout groups validates real lift in conversion before full deployment, not just good offline metrics, and it integrates with your e-commerce platform or mobile app via a recommendation API.
- Built with
- Collaborative filtering · Shopify · Magento
The right model depends on the decision waiting on it. Each use case needs its own data, hits a different accuracy ceiling, and refreshes on its own clock. The table below is the honest baseline; we scope your specific case from there.
| Use case | Data it needs | Typical accuracy | What it drives | Refresh |
|---|
| Churn prediction | 12-18 months of usage, support, and billing history with labeled churn events | 75-85% precision at 80%+ recall | Who the retention team calls before renewal | Weekly or monthly |
| Demand forecasting | Order history, seasonality, promo calendar, and external signals | 90-95% at weekly granularity for stable categories | How much stock to hold and staffing levels | Daily or weekly |
| Fraud and anomaly detection | Transaction or event history with labeled fraud cases | 85-95% precision to keep false positives manageable | Which transaction or event to flag for review | Real-time or near-real-time |
| Predictive maintenance | Sensor streams (vibration, temperature, current draw) plus maintenance and failure records | Validated per asset class, not a single headline number | When to service equipment before it fails | Continuous |
Industry benchmarks put the payoff of the maintenance case in perspective: predictive maintenance typically cuts equipment downtime by up to 50% and extends equipment life by years (McKinsey, 2017). The other cases follow the same logic, catch the signal before the cost lands.
Most predictive models fail in predictable ways. We design against these from the first data review, not after a backtest looks good and production disagrees.
- Data leakage
- A feature that quietly encodes the outcome (a cancellation flag set the day someone churns) makes the backtest look brilliant and the live model useless. We audit every feature for information that would not exist at prediction time.
- Class imbalance
- Fraud and churn events are rare, so a model that predicts never happens scores high on raw accuracy and catches nothing. We optimise for precision and recall on the rare class, and report both, not a single accuracy figure.
- Backtest overfitting
- A model tuned until it fits history perfectly has memorised noise. We validate on held-out time periods the model never saw during training, so the score reflects future performance, not hindsight.
- Concept drift
- The pattern that predicted churn last year stops holding when your pricing, market, or product changes. That is why monitoring and a retraining cadence are scoped into the build, not sold later.
- Do you have a published predictive-modeling case study?
- Not yet, and we'd rather say so than stretch an unrelated project to imply proof that doesn't exist. Most predictive analytics engagements start with data nobody has modeled before, that's the normal starting point, not a red flag. What's real: the client roster (Vodafone, T-Mobile, Aldi, Nike, Cisco, Lockheed Martin), the 4.9/5 Clutch rating, and the same discovery-first, monitored-in-production process applied to every model we ship.
- How do you know the model will still be right in six months?
- You don't, on day one, and neither does anyone who tells you otherwise. What you get instead is monitoring: prediction accuracy tracked against real outcomes on a rolling basis, an alert when accuracy degrades past a threshold, and a scoped retraining cadence, so drift gets caught by a dashboard, not discovered when someone finally asks why the numbers stopped making sense.
- Is this just a black-box score we have to trust blindly?
- No. Every model ships with the confusion matrix and feature attribution (what specifically drove each prediction), not just a headline accuracy percentage. If you can't see why a score landed where it did, you can't act on it with confidence, and we design the delivery around that.
How we work
From scope to shipped
Every project follows the same four phases. Scope is locked and price is fixed before development starts.
- Week 1
01Discovery and scope
We audit your available data, map the prediction use case, and define the target outcome. You leave week 1 with a written scope document, data requirements, and a fixed-price quote. No model training starts without your sign-off.
- Weeks 2-3
02Data preparation and architecture
We clean, transform, and structure your historical data into training-ready form. Feature engineering decisions made here determine model accuracy. The data pipeline and feature set are locked before model training begins.
- Weeks 4-10
03Model training, validation, and integration
Model training with cross-validation on held-out historical data. Accuracy is validated against real historical outcomes, not optimistic training metrics. Delivery integrated into your CRM, BI tool, or operational system via API or data pipeline.
- Weeks 10+
04Launch and post-launch monitoring
Production deployment with model performance monitoring from day one. Prediction drift and accuracy degradation tracked automatically. 8 weeks of post-launch support included in every project.
Why us
Why teams choose RaftLabs
01Senior engineers build what they scope
The engineers who assess your data problem also build the model. No bait-and-switch, no offshore handoff after the contract is signed. The team you meet in week 1 ships in week 10.
02Fixed price before development starts
We scope the work, calculate the cost, and lock it in writing before any model training starts. A scope change is a change request: priced, agreed, or dropped. It never absorbs into the project and appears on the final invoice.
03Shipping production software since 2015
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record across AI, data pipelines, SaaS, mobile, and enterprise platforms in healthcare, fintech, logistics, and manufacturing.
04Compliance built in from the start
GDPR, HIPAA, and SOC 2 requirements are scoped in week 1, not retrofitted before launch. We have shipped HIPAA-compliant systems for US healthcare clients and GDPR-compliant products for European markets.
We scope every project before pricing it, so you know the number before model training starts. Where you land depends on the number of use cases, data complexity, and delivery requirements.
- Focused model, $20,000-$50,000
- One use case, one data source, model training and validation, and delivery to one target system.
- Multi-model platform, $50,000-$120,000
- Multiple forecasting use cases, automated retraining pipelines, and BI dashboard integration.
What it costs
Predictive analytics, starting at $20,000.
One use case, trained on your data, delivered to the system your team already works in.
Starts at $20,000First model scoped and priced in about a week. Most clients start with one use case and expand once the first model is proving itself.
We scope the use case, calculate the cost, and lock the first phase in writing before model training starts.
No hourly billing
Once we scope your first model, that price is locked in writing, no surprise invoices, no change fees you didn't agree to.
Monitoring included, not sold separately
Drift tracking and a retraining cadence are scoped as part of the build, not a paid add-on you discover you need six months after launch.
Tell us what you want to predict.
Use case, current data sources, and the decision you want to improve. We'll design the model and give you a fixed cost.
