BlogSales forecasting - 5 reasons not to use Excel

B2B sales forecasting is often clumsy for a simple reason: most B2B companies lack the thousands of datapoints required by quantitative forecasting methodologies. Sales forecasting is thus handled in Excel, either directly or following extracts from a CRM system.

Excel is a fantastic tool (you can run a trading floor on Excel), but using Excel for B2B sales forecasting is a bad idea, for 5 main reasons.

 
1. CRM integration

Judgments matter in B2B sales forecasting. It is therefore important to (i) document “stories” (dialogues with prospects), (ii) record numbers (amounts, dates, probabilities), and (iii) perform analyses in a single place.

That is difficult with Excel, since designing a spreadsheet to log prospect conversations usually ruins it for number crunching. Excel-addicted sales managers often juggle with two sets of spreadsheets, for hard and soft data. That is very time consuming…

 
2. The time factor

In the absence of large data sets, B2B companies must rely on pipeline analysis to generate forecasts. That means monitoring not just volumes (e.g. number of opportunities, amount of opportunities) but also flows (e.g. conversion rates, durations). And monitoring flows means (i) recording a complete photograph of the sales pipeline every day, and (ii) running algorithms on these daily photographs. Doing that in Excel requires VBA programming. Good luck…

 
3. Amounts x Probabilities

Excel is good at handling bi-dimensional tables, and thus naturally encourages forecasts based on opportunity amounts x closing probabilities. Such sales forecasts are popular with B2B companies.

They are unreliable, for two reasons:

• They rest entirely on human, hence fallible judgments. A sales manager’s ability to overwrite the input of his sales reps does not change this problem.
• The closing probability is continuous (from 0% to 100%) but the event it represents is binary (winning or losing the deal). A single lost opportunity affects the entire forecast.

Pipeline dynamics are key to avoiding the Amounts x Probabilities pitfall, but as explained above, Excel is ill-suited to studying them.

 
4. Collaboration

Thanks to behavioural economics and decision theory, our understanding of judgmental forecasts has much improved during the past 30 years. One lesson of both academic studies and practical experiments is that multiple points of view always improve the corresponding forecast. In sales forecasting as in sales management, collaboration pays. And Excel is not a collaborative tool.

 
5. Multiple methods

Another important lesson of recent judgmental forecasting research is that combining sales forecasting methodologies always improves forecasts. In Excel, that would almost certainly translate into combining spreadsheets. Number of forecasting methodologies x number of sales reps x number of product lines = big headache for the sales manager…