This is the first of what is intended to be a 3 part series on how to get quality forecasts. We’d love your feedback; have a read on the impact of weather and restaurant sales below!

At Tenzo we’ve spent a lot of time thinking about how to forecast accurately and wanted to share a couple of the key insights. Typically we have found:

  1. A smart forecasting algorithm can out-perform a store manager in the long run by 25–50%, but it might miss short term events (e.g., roadworks, staff shortage) that only a manager may know about
  2. Weather and events are a critical element in improving a typical forecast
  3. Rain affects sales, but after a certain amount of rain the impact diminishes. Extreme temperatures (vs. normal for the season) are what cause significant deviations in sales.

How do we think about the impact of machine learning?

The below chart compares a 4-week rolling average (e.g., the last 4 Mondays) to a machine learning generated forecast. We typically find a 4-week average this is the best a good manager can do given their memory for events.

MAPE, for a 4-week average forecast vs. a machine learning generated forecast

Forecast Error

As you can see — the computer wins overall. There may be days when a manager knows something the computer does not, but overall results tend to this.

Now, let’s look at a chart showing how rain impacts sales for a given location.:

Daily sales variance per mm of rain

Sales vs Rain

The result is clear — when it start to rain, sales drop-off, but beyond a certain point — people are no longer driven by the rain.

Now, let’s look at temperature:

Daily sales variance per degree of temperature (celsius)

Sales vs Temp-1

Interestingly — this chart is looking at a day in July. A big portion of temperature will be captured in the normal seasonality (which any computer based forecasting system should capture). However, what becomes apparent is how it’s all about extremes — if the day is unseasonably warm or cold it will dramatically affect sales up to a point.

Note: importantly all of these results will be dependent on the specific location, brand and type of business. That’s why you need a machine!

Thanks for reading, we hope we’ve shown:

  1. How weather can be an important factor for your business
  2. The kind of dramatic improvement you can get by looking at it
  3. How a computer can outperform a typical location manager