Machine learning & predictive analytics
Predict what happens next.
Phantom Analytics builds machine learning and predictive systems that turn your business data into decisions — before the outcome happens, not after.
Predictive modeling · Churn & retention · Forecasting · Anomaly detection · ML infrastructure
Predict what happens next
Custom models trained on your data forecast the specific outcomes that matter — who will churn, what demand looks like, which leads will convert.
Understand why it happens
Predictions come with the drivers behind them, so decisions are grounded in the patterns in your data, not a black box.
Build systems that turn predictions into action
Models are shipped into the workflows and systems your team already uses, so predictions reach the people who need them, on time.
What we build
Machine learning systems for prediction, not generic data consulting.
Predictive Modeling
Forecast the specific outcomes that matter to your business.
Churn Prediction
Identify which customers are likely to leave, before they do.
Lead Scoring
Rank prospects by their likelihood to convert.
Revenue & Demand Forecasting
Project revenue, demand, and inventory with real drivers.
Anomaly Detection
Catch irregular patterns as they emerge, not after the fact.
Customer Analytics
Segment and understand behavior to inform key decisions.
Data Pipelines & ML Infrastructure
Get models into production and keep them running reliably.
Have a prediction problem worth solving?
Services
Machine learning systems built around a single outcome: better predictions.
Each engagement is built around your data and the decision it needs to inform — not a generic analytics package.
Predictive Modeling
Custom statistical and machine learning models trained on your historical data to forecast the specific outcomes that matter to your business — a number, a probability, or a classification tied directly to a decision you need to make.
Model selection is driven by the shape of your data and the decision it feeds, not a default algorithm applied everywhere.
Outcome: a working model that predicts, and evidence for why it works.Churn Prediction
Identify which customers or accounts are at risk of leaving, ranked by likelihood and timeframe, along with the behavioral and account signals driving that risk.
Built to plug into retention workflows so at-risk accounts reach the right team before they churn, not after.
Outcome: an early-warning system for customer loss.Lead Scoring
Rank prospects and inbound leads by their likelihood to convert, using firmographic, behavioral, and engagement data your systems already collect.
Scores are built to sit inside your CRM or sales workflow, so reps can act on them directly.
Outcome: sales effort focused on the leads worth pursuing.Revenue & Demand Forecasting
Forecast revenue, unit demand, and inventory needs with models that account for seasonality, trend, promotions, and external drivers relevant to your business.
Forecasts are delivered at the granularity your planning process actually needs — by SKU, region, or account.
Outcome: planning built on a forecast, not a guess.Anomaly Detection
Detect irregular patterns in transactions, operational metrics, or system behavior as they emerge — flagging what deviates from expected patterns in your data.
Thresholds and models are tuned to your baseline, so alerts stay meaningful rather than noisy.
Outcome: irregularities caught while they're still small.Customer Analytics
Segment and analyze customer behavior — usage patterns, lifetime value drivers, and response to pricing or product changes — to inform decisions across the business.
Built on your own customer data, not benchmark assumptions borrowed from another industry.
Outcome: a clearer picture of who your customers are and what drives them.Data Pipelines & ML Infrastructure
The data pipelines, feature stores, and deployment infrastructure that get models out of a notebook and into production — reliably, and on a schedule your business can depend on.
Includes monitoring for data drift and model performance, so predictions stay accurate as your data changes.
Outcome: models that keep running after the project ends.Not sure which service fits your data?
Use cases
Where predictive systems change decisions.
Patterns we build for, across industries. Every engagement is built on your own data — these are the shapes of problem our services are designed to solve, not case studies.
Retail & E-commerce
DEMAND · CHURN · SEGMENTATIONProject SKU-level demand ahead of purchasing and inventory cycles.
Flag lapsing shoppers before they stop buying.
Group customers by behavior to guide pricing and offers.
SaaS & Subscription
CHURN · LEAD SCORING · USAGEPredict which accounts are at risk of not renewing.
Prioritize trial and inbound leads by conversion likelihood.
Detect unusual usage patterns that signal risk or opportunity.
Financial Services
ANOMALY DETECTION · FORECASTINGSurface transactions that deviate from expected patterns.
Forecast cash flow and volume ahead of planning cycles.
Score accounts or applications against modeled risk factors.
Operations & Supply Chain
FORECASTING · ANOMALY DETECTIONForecast volume to plan staffing, inventory, and capacity.
Catch process or equipment irregularities as they emerge.
Flag disruptions to delivery and lead-time patterns early.
Sales & Marketing
LEAD SCORING · CUSTOMER ANALYTICSRank open opportunities by likelihood to close.
Predict which segments respond to which campaigns.
Estimate customer value to guide acquisition spend.
See how this applies to your data.
About
We build prediction, not dashboards.
Phantom Analytics is a machine learning and predictive analytics company. We build systems that turn business data into predictions — and turn those predictions into decisions.
Most data work stops at description.
We build past that point.
Dashboards and reports summarize what already happened. Our models estimate what's likely to happen next, explain why, and connect that estimate to a system your team acts on.
That distinction shapes every engagement. We're not a general data consultancy that occasionally builds a model — predictive modeling is the entire discipline we practice.
Specialists, not generalists
We focus exclusively on predictive and machine learning systems — not general analytics, BI, or reporting.
Grounded in your data
Every model is built on your own data and validated against it. We don't apply borrowed benchmarks or off-the-shelf assumptions.
Built to explain, not just predict
A prediction without a reason is hard to trust and hard to act on. Our models are built to surface the factors behind their output.
Engineered for production
A model that only works in a notebook doesn't change decisions. We build for deployment, monitoring, and maintenance from the start.
Roadmap
How an engagement unfolds
From first contact to a model running in production — and staying accurate after that.
Onboarding
Align on goals and success criteria, confirm data access, and get both teams set up on a shared timeline and communication channel.
Discovery & data assessment
Review the data available against the decision it needs to support, and confirm a predictive approach is viable.
Model development
Build and iterate on models suited to your data and the outcome being predicted.
Validation & interpretation
Validate against held-out data and make the reasoning behind each prediction explicit.
Deployment & integration
Ship the model into the systems and workflows your team already uses.
Monitoring & iteration
Track data drift and model accuracy, and retrain as your business changes.
Let's talk about what your data can predict.
Contact
Discuss a Machine Learning Project
Tell us about the decision you're trying to improve and the data you have. We'll follow up to talk through whether — and how — a predictive system fits.