Imagine you're a sales manager at a retail company,
and you want to predict future sales for a specific product based on historical sales data.

You have past year sales
such as date of sale, quantity sold, and sales revenue.

You also have additional datasets containing external factors that could influence sales,
such as advertising spend, weather data, and holiday dates.

Using Alteryx, you can leverage its predictive modeling capabilities
to forecast future sales based on historical patterns and external factors.

Here's how you can do it:

1. First,
you would import and clean your historical sales data and external factors data in Alteryx.

2. Next,
you would identify relevant features from your datasets,
such as sales trends over time,
seasonality, and the impact of external factors like advertising spend and weather conditions.

3. Then,
you would use Alteryx's predictive modeling tools to build a forecasting model.
You could use techniques such as time series analysis, regression analysis,

4. Once the model is built,
you would evaluate its performance using techniques
like cross-validation or
holdout validation to ensure its accuracy and reliability.

5. Finally,
you would use the trained model to make predictions for future sales
based on the latest data and external factors.