Churn Prediction
Identify customers most likely to leave before they do. Score every customer by churn risk and trigger automated retention actions for high-risk accounts.
TelcoBankingSaaSRetail
Predictive Analytics & AI Insights
Predictive Analytics & AI Insights That Turn Data Into Decisions.
Oxydata delivers predictive analytics — conversational analytics, ML forecasts, churn models, and Tableau / Power BI dashboards your leaders actually use.
We help SMEs and enterprises extract intelligence from the data they already have — without ripping out existing BI investments.
Microsoft Technology PartnerTableau & Power BISince 2006
★ Signature CapabilityAsk Your Data a Question.
Ask Your Data a Question.
Get an Answer.
Most dashboards show you the past. Our Intelligent Analytics layer lets you query your business data in plain English and get AI-generated answers backed by your actual numbers.
“Why did churn spike in Q3?”
“Which salespeople are at risk of missing target?”
“What drove the cost overrun in operations last month?”
“Which customers should we prioritise for retention calls?”
The 3-Layer Analytics Stack
Layer 1
Visualisation
Tableau & Power BI dashboards — your data made visual and accessible
Layer 2
Prediction
ML models that forecast churn, attrition, revenue, and anomalies
Layer 3 ★
Conversation
Ask questions in plain English — AI answers from your own data
Predictive Analytics Use Cases
We build models across every major business function and industry.
Sales Forecasting
Predict revenue with confidence. AI models trained on your historical sales data give sales leaders accurate pipeline forecasts without spreadsheet guesswork.
B2B SalesRetailDistributionFinance
HR Attrition Modelling
Know which employees are flight risks before they resign. Model attrition probability by department, role, tenure, and manager — and act before it's too late.
Enterprise HRShared ServicesBPO
Financial Anomaly Detection
Catch fraud, errors, and irregularities in real time. AI monitors transaction patterns and flags outliers that rule-based systems miss.
BankingInsuranceFinanceAudit
Operations & Supply Chain
Optimise inventory, predict demand, and reduce operational waste. AI models that learn from your logistics and operations data to improve efficiency over time.
ManufacturingLogisticsRetailFMCG
Custom Predictive Models
Have a unique business problem? We design and deploy custom ML models tailored to your data, your industry, and your specific prediction challenge.
Any industryAny use case
2–4 weeks
To first predictive model in production
From data assessment to live deployment
Plain English
Query your data conversationally
No SQL, no data team needed
Any stack
Works on top of your existing BI tools
Tableau, Power BI, or custom dashboards
All industries
Banking, telco, retail, healthcare, manufacturing
Use cases across every sector
FAQ
Predictive analytics FAQ
What is conversational analytics and how is it different from a normal dashboard?
A normal dashboard shows charts and numbers — you interpret them yourself. Conversational analytics lets you ask questions in plain English like "Why did sales drop in March?" or "Which customers are most likely to churn?" and get an AI-generated answer backed by your actual data. We build this intelligence layer on top of your existing Tableau or Power BI setup.
What predictive analytics use cases do you support?
We support customer churn prediction, sales forecasting, HR attrition modelling, financial anomaly detection, demand and supply chain optimisation, and custom ML models for industry-specific problems. We work across banking, telco, retail, manufacturing, healthcare, and professional services.
Do you work with our existing Tableau or Power BI setup?
Yes. We layer AI intelligence on top of your existing Tableau or Power BI investment — you keep your current dashboards and we make them smarter with predictive models and conversational querying. No need to replace your current BI tools.
How do I know if my data is ready for predictive analytics?
Most organisations underestimate their data readiness — and overestimate the problem. We run a data readiness assessment as the first step of every engagement. If your data needs cleaning, structuring, or enrichment before modelling, we handle that as part of the project scope. You don't need a perfect data warehouse to start.
How long does it take to implement a predictive analytics model?
A focused model — such as churn prediction or sales forecasting — typically takes 4–6 weeks from data assessment to production. More complex multi-model implementations take 8–12 weeks. We always assess data readiness before committing to a timeline so there are no surprises mid-project.
How is predictive analytics work priced?
Analytics projects are scoped and priced per engagement based on data complexity, model count, and integration requirements. We'll give you a clear estimate after an initial discovery session.
Related services
Explore related Oxydata capabilities
Predictive analytics
Ready to make your data work harder?
Tell us where you want to start — a churn model, forecasting, or conversational analytics — and we'll build a practical plan around it.