What this help guide covers:
Sales Forecasts
Sales forecasts are generated from your historical sales data and form the foundation for demand-based rostering in Tanda. These predicted sales are then used to calculate a recommended number of staff to roster, helping you match labour to demand and avoid over/under-staffing.
You can also adjust predicted sales to reflect real-world factors like public holidays, weather conditions, or special events, ensuring your recommended staff counts stay accurate even when trading conditions change.
To learn about importing sales data to Tanda, refer to our help guide Configure your Sales, Revenue & Demand Data (Data Streams).
Forecasting Methods
Tanda’s primary forecasting method is Average of Past Dates, which uses sales data from the previous three weeks to estimate future demand. If you already use an external forecasting process, you can also choose to apply your own sales forecasts instead.
Check your forecasting method in Settings > Integrations > Demand Forecasting:
Some POS integrations automatically import a predicted sales value alongside actual sales data. When these values are available, Tanda will use them in place of the selected forecasting method.
If you prefer to provide your own forecast, you can import predicted sales via CSV, SFTP, or API by selecting Use Your Forecast on this page.
To modify your predicted demand data, see Modifying Predicted Sales in the Roster below.
Configuring Recommended Staff Counts
Recommended staff counts use a ratio system to determine how many staff should be rostered based on the revenue amount. Think of it as '1 staff member in this team can handle this amount of sales in a 15-minute period'. Once the value entered has been exceeded for that 15-minute interval, an extra staff member will be recommended.
To set this up, follow the steps below:
Navigate to Settings > Integrations > Manage Data Streams and choose the data stream that contains your sales data.
Create a new join for a team that should be rostered.
Choose how many sales should be received to recommend rostering an additional staff member in that team.
In the above example, for a 15-minute segment with less than 80 sales, 1 staff member will be recommended. For a 15-minute segment with more than 80 sales but less than 160 sales, 2 staff members will be recommended, and so on.
The minimum number of staff that will be recommended is the number of different team joins configured.
Entering a value of 0 will not recommend any staff.
💡 Tip: If you’re finding it difficult to set the right ratios when configuring recommended staff for the first time, a useful approach is to go to a day where staffing aligned well with sales, and adjust the team join amounts until the recommended amounts are similar to the actual roster amounts.
Viewing Forecasted Sales and Staff Counts
Roster: Daily View
Viewing the roster in Daily View will give you the clearest view of your predicted sales and recommended staff.
In this view, you'll see a graph showing you the predicted sales across the day, the recommended number of staff (if configured in the data stream), and the actual number of rostered staff for each 15-minute segment in the day.
Additionally, you can view the total recommended vs actual number of rostered staff hours for each team on the schedule below the graph:
Sidebar & Weekly Views
Overall predicted sales and recommended staff hours can also be viewed in the sidebar of the roster.
Statistics such as Total Hours & (Recommended Hours) and Sales Forecast & (Wage % Revenue) compare your overall roster cost against predicted revenue and the recommended number of hours.
You can also view the Total Hours/Recommended Hours stat at the top of each day:
Modifying Predicted Sales in the Roster
In some cases, predicted sales may not accurately reflect expected trading conditions due to factors such as weather, major sporting events, or outlier days like public holidays. To account for this, you can modify predicted sales directly in the roster by increasing or decreasing them by a set percentage, or by selecting alternative historical dates to use for the sales average.
In the roster sidebar, navigate to Tools > Demand Predictions.
From there, choose the date that you would like to adjust the predicted sales for.
This shows how the average for the selected day is calculated. Unless you're importing your own predicted demand data, this will default to the same day of the week for the last three weeks
Deselect days you don't want to include, or click Select a Date to include a new date. You'll see the new total on the right under Total predicted stats for this day:
If you know sales are going to be generally higher or lower on a specific day, you can make use of the Modifier to adjust the predicted sales amount:
Either enter a specific amount in New Total, or adjust the percentage modifier. To modify the demand for only part of the day, select Switch to Fine Tune. This allows you to adjust the demand percentage at any point in the day.
Click in the modifier line and drag the point to make adjustments:
Ensure you click Save and Close to apply your changes to the roster.
Live Insights
You can also use the Live Insights widget in the dashboard to see your actual and predicted sales data against the rostered and actual staff counts for the day:
Learn more about the Live Insights widget in this help article.