Revenue Forecasting

Stop Planning Blind.
Forecast What's Coming Next.

The same forecasting methods used at Google and Uber—without hiring a data scientist. Upload your sales data and get Prophet time series forecasts, seasonal decomposition, and confidence intervals in minutes.

Why Spreadsheet Forecasts Fail

Linear trendlines miss the patterns that matter most.

Seasonality Is Invisible in Excel

Your business has weekly, monthly, and annual patterns. Excel's FORECAST draws a straight line through all of them. Prophet decomposes each seasonal layer separately, so your Q4 forecast actually reflects Q4 behavior.

One Number Isn't a Forecast

A real forecast includes confidence intervals—the range of likely outcomes. "Revenue will be $120K–$145K with 80% confidence" is actionable. "Revenue will be $132K" is just a guess with false precision.

Trend Changes Go Undetected

Growth rates shift. Market conditions change. Prophet automatically detects changepoints—moments where your growth trajectory shifted—and adjusts the forecast accordingly instead of blindly extrapolating old trends.

Enterprise Forecasting Methods, Zero Setup

MCP Analytics automatically selects the best method for your data

Prophet
Developed at Meta

Handles seasonality, holidays, and trend changes automatically. Best for business data with strong weekly and annual patterns. Robust to missing data and outliers.

ARIMA
Classical time series

The gold standard for stationary time series. Captures autocorrelation patterns and produces tight confidence intervals. Best for stable, well-behaved data with consistent patterns.

Trend Decomposition
Visual + analytical

Separates your data into trend, seasonal, and residual components. See exactly what's driving revenue: underlying growth vs. seasonal effects vs. random variation.

Upload. Forecast. Plan.

From historical data to forward-looking projections in 3 minutes

1

Export Your Sales Data

Download revenue or sales data from your POS, accounting software, e-commerce platform, or CRM as CSV. Include a date column and a revenue column. More history means better forecasts—12+ months is ideal.

2

Upload Your CSV

Drop the file into MCP Analytics. The system auto-detects date and numeric columns, identifies the time series structure, and prepares the data for forecasting. No manual configuration needed.

3

Get Your Forecast

Receive an interactive forecast with projected revenue, confidence intervals, seasonal patterns, and AI-written insights about growth trajectory and risks. Share the report with your team or export as PDF.

What's In Your Forecast Report

Every analysis produces a shareable, interactive report

Revenue Forecast

30, 60, and 90-day forward projections with upper and lower confidence bounds. See the range of likely outcomes, not just a point estimate.

Seasonal Decomposition

Break revenue into trend, weekly seasonality, annual seasonality, and residual. Understand what's driving your numbers at each time scale.

Growth Rate Analysis

Month-over-month, quarter-over-quarter, and year-over-year growth rates with trend direction and acceleration/deceleration detection.

Changepoint Detection

Automatically find the moments where your growth trajectory changed. Launch effects, market shifts, and inflection points identified and dated.

Anomaly Detection

Flag unusual days or weeks that don't fit the pattern. Understand whether a spike was a real trend shift or a one-time event.

Holiday & Event Effects

Quantify the impact of holidays, promotions, and events on revenue. Know exactly how much Black Friday or a product launch contributed.

Segment Forecasts

Forecast by product line, region, customer segment, or any category in your data. Plan at the granular level, not just the top line.

Trend Analysis

Linear and exponential trend fits with R-squared values. Simple, interpretable view of where revenue is heading and how fast.

AI Insights

AI-written narrative explaining what the forecast means for your business: risks, opportunities, and recommended actions in plain English.

See What You'll Get

Real output from a time series forecast on retail demand data

TS
Time Series Forecasting — Retail Demand
ETS model selected from 3 candidates • 4,826 daily observations • 95% confidence intervals
ETS
Best Model
7
Seasonal Period
4,826
Observations
95%
Confidence Level

Key Insights

Strong weekly seasonality detected

STL decomposition reveals clear day-of-week patterns in demand. The model captures these cycles so your forecast reflects real buying behavior, not just a trend line.

ETS selected as best model from 3 candidates

ETS, ARIMA, and Prophet were compared on held-out test data. ETS produced the lowest forecast error, automatically accounting for level, trend, and seasonal components.

Residuals pass normality check

Model residuals are well-behaved, meaning confidence intervals are trustworthy. You can rely on the upper and lower bounds for inventory and staffing decisions.

MCP Analytics vs Excel Forecasting

What you gain beyond FORECAST() and trendlines

MCP Analytics
Excel / Google Sheets
Forecasting
Prophet, ARIMA, and ETS — automatically selects the best model for your data
Forecasting
FORECAST() draws a straight line through your data
Uncertainty
"Revenue will be $120K–$145K with 80% confidence" — a range you can plan around
Uncertainty
"Revenue will be $132K" — a single number with no indication of how wrong it might be
Seasonality
Decomposes weekly, monthly, and annual patterns separately so Q4 forecasts reflect Q4 behavior
Seasonality
No built-in seasonal handling — your Christmas spike distorts the trendline year-round
Trend Changes
Detects changepoints automatically — knows when your growth rate shifted and adjusts
Trend Changes
Assumes constant trend forever — a product launch and a normal week look the same
Output
Interactive report with charts, decomposition, anomalies, and AI-written business narrative
Output
A column of numbers you then have to chart and interpret yourself
Time to Forecast
Under 60 seconds
Time to Forecast
Hours — if you know the formulas
3
Forecasting methods
AES-256
Data encryption
<60s
Time to forecast
Free
To get started

Revenue Forecasting FAQ

How accurate is revenue forecasting?

Accuracy depends on your data quality and business volatility. For businesses with consistent patterns, Prophet forecasts typically achieve 85–95% accuracy on 30-day horizons. Every forecast includes confidence intervals so you can see the range of likely outcomes, not just a single point estimate.

How much historical data do I need?

At minimum, 3 months of daily data or 12 months of weekly data. For best results, 2+ years of data allows the model to capture annual seasonality patterns. The more data you provide, the better the model can distinguish genuine trends from noise.

What forecasting methods does MCP Analytics use?

Prophet (developed by Meta, excellent for data with strong seasonality and holiday effects), ARIMA (classical statistical method for time series), and simple trend analysis (linear and exponential fits). The system recommends the best method for your data automatically.

Can I forecast by product, region, or segment?

Yes. If your data includes category columns (product line, region, segment), you can generate separate forecasts for each. This lets you plan at a granular level rather than relying only on top-line projections.

How is this different from forecasting in Excel?

Excel's FORECAST function is a simple linear extrapolation. MCP Analytics uses Prophet and ARIMA, which model seasonality, holidays, trend changes, and outliers. You also get confidence intervals, anomaly detection, and AI-written explanations of what's driving the forecast.

Ready to See What's Coming Next?

Upload your sales data and get a revenue forecast with confidence intervals in under 3 minutes. No credit card required.