Forecasting the Future of FP&A

Forecasting the Future of FP&A

Recently, we sponsored a webinar with IMA on Machine Learning Forecasting. We wanted to learn how different companies, in a variety of industries, are dealing with the increasing demands of financial planning & analysis, specifically forecasting.

We asked our 1200 webinar attendees three questions that related to their company’s current ability to forecast. The results gave us some surprising insight into current & future trends in FP&A.

First, we asked our listeners to share – How frequently does your company forecast today?

32.6% of respondents said they forecast every quarter, 13.5% every six months, 34.8% every month, 6.6% every week, and 12.4% less than once a year.

These results match up with current industry standards, which suggest companies should forecast at least every month. Ideally, all respondents should be forecasting every week, which was true for a small minority of our listeners (6.6%).

Next, we polled our viewers and asked – How does your organization use business analytics or performance management solutions?

In response, 16.5% of attendees said they use it for data aggregation and reporting to gain insights into the past and answer the question, “What has happened?”; 32.3% use it for forecasting and planning to understand the future and answer questions about “What could happen?”; 16.1% use it for modeling, combining financial and non-financial data across to answer the question, “Why did this happen?”; and 35% of people said they don’t have a formal solution today and are still living in Excel.

For many attendees, their finance team still lives in Excel, which significantly limits their ability to forecast with granularity. It was great to see that 32.3% of organizations are using a performance management solution for forecasting. This is a clear indicator that these companies are well-positioned to address the increasing demands of FP&A.

Machine Learning forecasting is well-positioned to help the 83.9% of companies who are not already combining financial and non-financial data to ask, “Why did this happen?” Machine Learning should help these organizations identify key business drivers and develop confidence in their resulting forecasts.

Finally, we were curious to know – What is your forecasting horizon?

We found out that 23.1% of attendees forecast to the end of the quarter, 51.5% to the end of the fiscal year, 15.9% a rolling 18-month period, and 9.5% a rolling 24-month period.

Ideally, more companies would be doing 18- and 24-month rolling forecasts. The 25.4% of companies with this forecast horizon speaks to an increased urgency to forecast longer periods with more specificity.

Overall, we’re by the position that many organizations are in with regards to their forecasting. For many, the increasing popularity of Machine Learning forecasting will allow their companies to better address the question, “why did this happen?”

Interested to learn more about Machine Learning forecasting? Watch the full webinar.


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