We build predictive analytics models — churn prediction, demand forecasting, pipeline scoring, and anomaly detection — trained on your historical data and embedded directly into your existing Power BI, Tableau, or Zoho Analytics dashboards. Predictions where your team already works.
Each model is trained on your historical data, validated against holdout sets, and embedded into your existing dashboards — not deployed as a separate platform your team has to learn.
Reactive decisions, discovered churn, missed forecasts — these are the patterns that predictive models replace with forward-looking intelligence.
A customer cancels. The account manager is surprised. Looking back at the data — usage dropped 40% six weeks ago, support tickets spiked, the last QBR was missed. All the signals were there. Churn prediction surfaces these signals as a risk score weeks before the event, when intervention is still possible.
How much stock to order next quarter? How much capacity to allocate? How many resources to hire? These decisions get made from last year's actuals and someone's intuition. Demand forecasting replaces intuition with statistically validated predictions from your actual historical patterns.
140 active deals in the pipeline. Sales time allocated based on deal size and squeaky-wheel dynamics — not on which deals are actually likely to close. Pipeline scoring tells the team where to focus by assigning win probabilities based on historical patterns, not optimism.
Revenue dropped on Tuesday. Discovered Friday at the review meeting. Four days of compounding impact that could have been addressed on Tuesday morning. Anomaly detection flags deviations within hours of occurrence — issues surface before they compound.
Customer acquisition spend distributed evenly across channels without knowing which channels produce customers who stay and spend more. LTV prediction by acquisition source enables budget allocation that maximises long-term value — not just conversion volume.
Equipment maintained every 90 days whether it needs it or not. Some maintained too frequently (wasted cost), some failing between scheduled intervals (unexpected downtime). Predictive maintenance from sensor and usage data schedules intervention based on actual condition signals.
Data assessment first. Model selection second. Deployment into your existing dashboards — not a separate platform.
Before recommending any model, we audit your historical data — volume, quality, completeness, and the signals it contains. We identify which features (variables) have predictive power for your specific question and engineer additional features where the raw data needs transformation. This step determines what's actually achievable — we won't promise 90% accuracy on data that can't support it.
We select the right model type for your prediction task — classification for churn, time-series for demand, regression for pipeline scoring. Models trained on your historical data, validated against holdout sets your team has never seen, and tested for performance across different time periods and data segments. Accuracy benchmarked against your current baseline — you see the improvement before deployment.
Predictions deployed directly into your Power BI, Tableau, or Zoho Analytics environment — not a separate ML platform requiring a new login, new training, and new adoption journey. The churn score appears alongside the account record. The demand forecast appears on the operations dashboard. The pipeline probability appears on the sales rep's deal view. Predictions where your team already works.
When a churn score crosses a defined threshold, an alert fires to the account manager. When a demand forecast deviation exceeds a configured range, procurement is notified. When an anomaly score triggers, the relevant manager receives an instant notification. Predictions that drive action — not just predictions that sit in a dashboard waiting to be checked.
Predictive models degrade over time as business patterns change. We configure model performance monitoring — tracking prediction accuracy against actuals on a rolling basis. When performance drops below defined thresholds, we retrain. Model accuracy is always visible in the dashboard alongside the predictions it's producing.
A churn score of 78% means nothing without an explanation of which factors drove it and what action to take. We build interpretation layers alongside every model — the three factors contributing most to the risk score, the recommended intervention, and historical evidence of whether that intervention works. Predictions that can be explained and acted on.
Churn, demand, pipeline, and anomaly detection — applied to different industries and data environments.
As part of the larger Power BI engagement for the restaurant chain (NDA), we built a customer churn analysis layer on top of the sales and customer dashboards. The challenge was defining "churn" in a restaurant context — unlike SaaS, there's no explicit cancellation. We defined churn as customers whose visit frequency had dropped below a statistically significant threshold relative to their historical pattern. The model identified at-risk customer segments by location, demographic, and visit behaviour — enabling targeted retention campaigns before those customers were lost.
For subscription and SaaS businesses on Zoho, we build churn prediction models that use Zoho CRM and Zoho Desk data as the signal source — login frequency, feature usage patterns, support ticket volume and sentiment, contract renewal proximity, payment behaviour. The churn score is embedded directly in the CRM account record and in the Zoho Analytics dashboard, so account managers see risk level alongside their pipeline without switching platforms.
A sales organisation with 140+ active deals in CRM had no systematic way to distinguish which opportunities were genuinely progressing from which were stalled at an optimistic probability manually entered by the sales rep. We built a pipeline scoring model trained on 18 months of historical deal data — win probability assigned to every deal based on actual patterns, not rep estimates. The model surfaces both the probability and the three factors contributing most to it, enabling the sales manager to have a precise conversation about each at-risk deal.
For businesses managing physical inventory or operational capacity — manufacturers, distributors, field service businesses — demand forecasting replaces gut-feel procurement and capacity decisions with statistically validated predictions. We build time-series models on historical sales or demand data, incorporating seasonality, trend decomposition, and leading indicators where available. The forecast is embedded in the operations dashboard alongside current actuals — so the procurement team sees predicted demand for the next 8–12 weeks alongside current stock levels.
Predictive model accuracy depends entirely on data quality and volume. We audit your data before recommending models and before quoting. If your data can't support a useful churn model, we tell you — rather than delivering one that performs worse than guessing.
The biggest failure mode for ML projects is adoption. We deploy predictions into Power BI, Tableau, or Zoho Analytics — where your team already works. The churn score appears on the account record. The forecast appears on the ops dashboard. No new tool, no new login, no training barrier.
A churn score of 78% is not actionable without context. We build interpretation layers — which three factors drove the score, what the recommended intervention is, and what the historical evidence says about whether that intervention works.
Models degrade as business patterns change. We configure performance monitoring so accuracy is tracked on a rolling basis. When performance drops, we retrain. You always know how accurate your predictions currently are.
ISO 27001. NDA before any data is shared. We assess your data before recommending any model.
Data quality assessed first. Accuracy estimated honestly. Deployed into your existing tools. Performance monitored continuously.
We assess your historical data — volume, quality, completeness, and which signals it actually contains. We estimate achievable accuracy honestly.
Feature engineering, model training, cross-validation. Accuracy benchmarked against your current baseline. You see the improvement before deployment.
Predictions embedded into your existing Power BI, Tableau, or Zoho Analytics dashboards. Alerts configured. Interpretation layer built.
Model accuracy tracked against actuals continuously. Retraining scheduled quarterly or triggered when performance drops below threshold.
Customers cancel and the team is surprised. The signals were always there — usage decline, support frustration, missed touchpoints. Churn prediction surfaces these as a risk score weeks before the event, when you can still do something about it.
Your forecast is based on rep-estimated probabilities that are consistently optimistic. Pipeline scoring replaces gut feel with AI win probabilities trained on your actual historical win/loss patterns — forecast accuracy improves and management focus goes to the right deals.
Inventory decisions made from last year's actuals and intuition. Stockouts happen, overstocking happens. Demand forecasting gives your procurement team statistically validated predictions for the next 8–12 weeks — with confidence intervals that show where uncertainty is high.