There are limitations when generating forecasts from a CRM that will often lead companies to use spreadsheets in Microsoft Excel to run a forecasting process, and it’s easy to see why. Excel is generally available and most people have sufficient familiarity with it, which makes an Excel-based approach seem simple.
Given that most organizations don’t have a better “at hand” solution, it’s not surprising that the drawbacks from using Excel are often ignored or overlooked. This blog will explore four specific drawbacks that contribute to forecast inaccuracy when spreadsheets are used.
1. Data Preparation
To run a forecast process in a spreadsheet, the data has to first get to the spreadsheet. Data is typically extracted from the CRM into a spread in a manual operation, but in a more complex organization, data is extracted from multiple spreadsheets. As soon hits the spreadsheet, it is potentially out of date. If the forecast process takes days or even weeks, then the spreadsheet data will inevitably become stale long before the forecast is complete. Using out of date data doesn’t contribute to forecast accuracy.
2. Collaborative Forecasts
Spreadsheets are typically documents located on one person’s computer. Collaboration involves huddling around the computer, placing the spreadsheet on a shared drive or exchanging documents via email. It’s hard for people to work together and collaborate on a forecast in a spreadsheet because that’s not what spreadsheets are designed for.
None of these methods are ideal or even efficient. The emailing of spreadsheets is perhaps the most common method, but this inevitably results in versioning and timing problems. Spreadsheets remain as silos of disconnected information.
3. Forecast Aggregation
When the task of completing a forecast has been split between a number of spreadsheets, there is inevitably a need to aggregate the spreadsheets so an overall forecast can be produced. This manual task is prone to errors, and various reports suggest 9 out of 10 spreadsheets contain undetected errors.
When models are complex, errors are much more likely to happen—no matter how skilled the user. If the goal is to create an accurate forecast, spreadsheet errors are particularly unwelcome. If all potential errors in a spreadsheet model are considered, these same numbers used for the forecast become increasingly questionable.
4. Managing the Forecast Process
A forecast typically has a start date and a due date. In more complex organizations different parts of the organization will have different due dates. A regional manager can start their forecast but probably can’t complete their forecast until all their direct reports have completed their forecasts.
What this means is there needs to a forecasting process that is established and followed. Spreadsheets don’t do that, making the process likely managed through emails, which is not a process tool either. All in all, it’s hard to run an efficient process without the right tools.
Are you still using Excel to run your forecasting process? It’s time to breakup with Excel and use an integrated and collaborative forecasting solution. Contact us today for a live demo with Vortini.
Jess is a communications professional and Vortini’s lead content/web developer. Her current interests lie in the intersection of sales technology and machine learning. In her free time she reads a book-a-week, practices yoga, and is an avid gardener.