It’s 5:05pm EST. Bob, CFO of ABC Inc is on an earnings call and is reporting a 20% miss on earnings due to slower revenue growth than forecasted. Company ABC’s stock price is plummeting, down 15% in extended hour trading. The board is furious and investors demand answers on the discrepancies.
Inaccurate revenue forecast remains one of the biggest risks for CFOs. In a recent study, more than 50% of companies feel their pipeline forecast is only about 50% accurate. Projecting a $30M revenue target and coming in short $6M can leave investors and employees frustrated and feeling misguided on the growth trajectory of the company.
In the past 10 years, supply chain has become much more complex with omni-channel distribution and the increasing number of indirect participants that can influence product demand. Advertising and promotions can create an uplift in demand that spikes sales by 20% or more. In addition, different types of customers have different purchasing behavior. These behavior are driven by myriad of underlying indicators and should be modeled individually. Yet, Financial Planning and Analysis (FP&A) has not changed fundamentally despite the changing landscape in the way companies do business. The process is still largely manual and dependent on time-series estimation techniques dating back to the 1980s.
Machine learning is a new technology that uses algorithms to learn from the data and guide us in making more informed decisions. Leveraging the power of machines allow us to consider more scenarios and combine the effects of thousands of indicators to improve forecast accuracy. For revenue forecasting, machine learning excels in the following 3 areas:
1. Trend discovery from unlimited amounts of data
With the advances in big data technologies, computers can crunch through data of all types and sizes. Unlike humans, algorithms can simulate numerous scenarios and recognize patterns that keep re-emerging in the data. It is also not limited to structured data and can examine unstructured data such as emails and logs to extract meaningful indicators.
2. Granularity of forecast
Instead of looking at product line level aggregate sales values, machine learning algorithms can detect patterns at SKU, purchase order and invoice level to discover interesting relationships and dependencies. For example, algorithms may find that the demand of one product (iPhone 6) is a leading indicator of demand for another product (iPhone 6 accessories).
3. Adaptive and Dynamic
Machines can also automatically adapt and re-run forecasting scenarios to adjust to changing market conditions and consumer demands.
Companies such as Flowcast are leading the charge in introducing machine learning techniques to the finance department of organizations.