Evolved360 AI
Decisions Built on What
Will Happen — Not What Did.
Demand Forecasting. Churn Prediction. Risk Modeling. Time-Series Analysis.
Business planning based on last quarter's numbers is reactive by design. Predictive analytics shifts your operating model forward — giving operations, finance, and sales leadership advance notice of what's coming rather than a detailed picture of what already happened.
Your Analytics Partner
Most business forecasting is trend extrapolation. ML-powered forecasting accounts for the signals trends miss.
Spreadsheet forecasting takes last year's numbers and applies a growth rate. It doesn't account for seasonal demand patterns, external signals like competitor pricing changes, leading indicators that precede customer churn, or non-linear relationships in your data. ML-based predictive models learn these patterns from your historical data and produce forecasts with accuracy significantly better than linear extrapolation — with documented confidence intervals that make planning decisions defensible.
40–60%
Forecast accuracy improvement
10+ yrs
Data science experience
90 days
To first production model
SOC 2
Type 2 certified team
What Changes
What planning looks like when forecasts account for the signals trends miss.
Demand Planning Accuracy
Inventory, staffing, and production decisions based on forecasts that account for seasonality, promotions, and leading indicators — not just last period's actuals.
Revenue Forecast Confidence
Sales pipeline models that weight deals by predicted close probability, adjusted for historical conversion patterns and current engagement signals.
Proactive Risk Identification
Credit, churn, equipment failure, and supply chain risk scored before the event occurs — giving operations time to intervene rather than respond.
Resource Optimization
Staff scheduling, capacity planning, and budget allocation informed by predicted demand — reducing both shortfalls and excess cost in volatile environments.
What We Model
Predictive models built around decisions that matter to your business.
Demand & Sales Forecasting
Time-series models for SKU-level demand, regional sales, and revenue prediction that incorporate seasonality, external signals, and promotion effects.
Customer Churn Prediction
Propensity models that score existing customers by churn probability 30–90 days out — with enough lead time for customer success teams to intervene.
Inventory Optimization Models
Safety stock and reorder point calculations that balance stockout risk against carrying cost, adjusted by lead time variability and demand uncertainty.
Financial Risk Modeling
Credit risk scoring, collections probability models, and cash flow forecasting built on your customer payment history and financial data.
Equipment & Maintenance Prediction
Predictive maintenance models for manufacturing and logistics equipment — using sensor data and maintenance history to forecast failure probability before breakdown.
Forecast Dashboards & Reporting
Executive-ready dashboards that surface forecast outputs, confidence intervals, and scenario comparisons. Integrated with your existing BI tools or deployed as standalone reporting.
ML forecasting models typically improve demand prediction accuracy by 40–60% compared to spreadsheet-based extrapolation — directly reducing overstock and stockout costs.
Book Analytics AssessmentFull AI Coverage
Predictive models work best integrated with your full analytics stack.
We connect forecast outputs to your BI dashboards, ERP systems, and operational workflows so predictions drive planning decisions automatically.
What Changes
What planning looks like when your forecasts are built on patterns, not assumptions.
Client result
“We were carrying 22% excess inventory as a buffer against forecast error. After the demand model went live, we reduced safety stock by 14 percentage points with fewer stockouts than before. The inventory carrying cost reduction paid for the entire analytics engagement in four months.”
VP Supply Chain · Distribution Company · ETG client since 2023
The Case for Predictive Analytics
Why the gap between your current forecasts and ML-based forecasts is almost certainly larger than you think.
Most business forecasting has two characteristics: it's based on trends in historical data, and it doesn't account for the leading indicators that precede trend changes. A demand forecast built by extending last year's seasonal curve doesn't account for a new competitor entering your market, a raw material price change affecting customer purchasing decisions, or a product launch that will shift category mix. ML-based forecasting can incorporate these signals — if the data exists — because it learns multi-variable relationships rather than fitting a single curve.
The ROI of predictive analytics is most cleanly measured in inventory, staffing, and risk. Inventory forecast improvement translates directly to working capital reduction and service level improvement. Staffing forecast improvement translates to scheduling efficiency and overtime cost reduction. Churn prediction translates to customer lifetime value recovered through timely retention effort. These are not soft benefits — they are costs that can be calculated before a model is built and measured after it runs.
The infrastructure requirement for predictive analytics is lower than most businesses assume. You don't need a data warehouse or a data science team to implement a demand forecasting model. You need clean historical data — typically 2–3 years of transactional records — and a defined planning process that will consume the forecast outputs. We build the model, deploy it to a scheduled pipeline, and deliver outputs in the format your planning team already uses.
“The businesses that benefit most from predictive analytics aren't the ones with the most data — they're the ones with the clearest question. 'What will our demand be by region by SKU next quarter?' is a specific, answerable question with measurable business value. 'What insights are in our data?' is a fishing expedition. Start with the question.”
Kevin Nishimura, CTO — Evolved Technology Group · SOC 2 Type 2 Certified · 10+ Years Data Science
Common Questions
Frequently asked questions.
Ready to plan around what will happen instead of what already did?
Book a free analytics assessment. We'll review your current forecasting process, identify the highest-value prediction problem to solve first, and show you what a model deployment looks like for your data situation.