Non-financial data affects your business every day
If we take a restaurant's non-financial data as an example, one regular data point that should be logged and measured is customer numbers. The number of customers that come in for an evening service should be counted on every shift and compared to each other, compared to the week before, a cross-compared with the revenue generated. This gives the business another KPI, average spend per head. In this trade, if you can focus your waiting staff's efforts on up-selling, driving average spend up by a small increment, can have hugely positive impacts on hospitality businesses.
Using non-financial data for forecasting
Forecast your variables
Think units produced, the average cost of those units, staff levels, expected future client numbers. Your list will be endless.
Working out the financial implications of changes in each of these variables is not only time consuming, but it also increases the window for error. Especially if, for instance, forecasted sales are a product of average price and production schedules but those production schedules are directly affected by staffing levels.
A better option would be to use this numeric data directly. To build a forecast that reads this and does everything automatically, behind the scenes, driving all of your reporting. This is exactly why cashflow forecasting in the cloud with Futrli will make your life so much easier.
The power of Formulas
Let's look at a simple example.
We need to upload ‘Units Produced’ and ‘Average Cost per Unit’ as non-financial data. Then using Futrli formula method we can multiply these together to get an accurate forecast of the Cost of Goods Sold. As time goes on, if the production schedule needs to change this data just needs uploading without any manual recalculations needed.
We can build on this to increase the relevance.
Let’s say that the units are sourced from the US and priced in USD. Then let’s say there has been some kind of significant event affecting the foreign exchange. We can incorporate this, adjusting the cost to incorporate the FX change.
The bigger picture: Financial & Non-Financials Working as One
These Forecasts shouldn't reference non-financial data in isolation. It's when you combine this with your actual financial data, simultaneously, that you get the most powerful results.
Again, let's use an example.
A company sells paint-balling days out and all sales are generated by salespeople. Due to the seasonality, it is good to base a forecast on the same period last year and adjust this for the number of salespeople relative to last year:
Here we take last year’s sales figure (£100k) and multiply it by the forecasted number of sales staff (8) divided by last years no of sales staff (5):
£100k X (8 / 5) = £160k
This can be taken further by adding a price increase to last year’s sales, let’s say a planned 5% increase in prices is expected to not affect sales:
(£100k X 105%) X (8 / 5) = £168k
There are countless ways we can use this non-financial information when forecasting. The time saved and increased accuracy are invaluable when planning to take your business to the next level.