Table of Contents
Tools Needed
- Google Sheets – for organising data.
- ChatGPT (GPT-4 Turbo) – for generating predictive insights.
- Power BI – for advanced data visualisation.
- Zapier – to automate data flows.
- BigML – for machine learning-based forecasting.
Step 1: Centralise Historical Data
1) Consolidate data from relevant sources into Google Sheets using Zapier for automation:
- Sales data (e.g., revenue, unit sales).
- Marketing performance (e.g., ad spend, click-through rates).
- Seasonal trends or external market data.
2) Structure the sheet with columns like:
- Date/Time Period.
- Performance Metrics (e.g., Revenue, ROI).
- External Factors (e.g., Market Growth, Seasonality).
Step 2: Identify Key Metrics Using ChatGPT
Input your structured data into ChatGPT to identify metrics that impact performance the most.
Sample Prompt:
Our business operates in [specific industry], targeting [audience demographic, e.g., small businesses or individual consumers] with [product/service type, e.g., SaaS solutions, retail products]. The primary revenue streams are [describe, e.g., subscription plans, eCommerce sales]. Here is the historical sales data: January: Revenue $50,000, Ad Spend $10,000, Market Growth 2%. February: Revenue $XXX, Ad Spend $XXX, Market Growth XX%. The primary goal is to understand which factors most influence sales in this context. Key metrics include [list additional metrics, e.g., conversion rates, average order value]. Analyse this data to identify patterns or correlations and recommend which metrics we should prioritise for future forecasting. Provide actionable insights tailored to our industry and target audience.
ChatGPT will generate actionable insights based on the data provided.
Step 3: Build Predictive Models With BigML
1) Upload your organised data from Google Sheets into BigML to build machine learning-based forecasting models.
2) Select key drivers identified by ChatGPT (e.g., Ad Spend, Market Growth) as input variables.
3) Train predictive models in BigML to generate forecasts:
- Predict sales, market trends, or other outcomes based on historical data.
- Simulate different scenarios (e.g., increased marketing budget or economic changes).
4) Export the predictions back to Google Sheets for reference and further processing.
Step 4: Visualise Forecasts In Power Bi
1) Import the forecast data from Google Sheets into Power BI.
2) Create interactive dashboards to visualise:
- Predicted vs historical performance.
- Key drivers’ impact on outcomes (e.g., how Ad Spend affects Revenue).
- Risk factors and growth opportunities.
3) Add filters for dynamic exploration by time periods, markets, or segments.
Step 5: Automate Data Updates And Forecasting
1) Use Zapier to automate workflows between tools:
- Sync new data from your CRM or analytics tools to Google Sheets.
- Automate updates to BigML models and Power BI dashboards whenever new data is added.
2) Schedule recurring workflows to ensure forecasts are updated in real-time.
Step 6: Monitor Accuracy & Iterate
1) Compare actual results against forecasted values in Power BI to identify discrepancies.
2) Input deviations into ChatGPT or BigML for refinement:
Sample Prompt:
"Here is the forecasted vs actual data for the last quarter: January Forecast: $XX, Actual: $XX. February Forecast: $XX, Actual: $XX. Analyse discrepancies and suggest adjustments to improve forecasting accuracy."
3) Update BigML models based on these insights to enhance predictive capabilities.