Deciding between a planning and forecasting tool?
Which's right for you?

Can it provide a consolidated view of both internal data (like historical demand and sales figures) and external data (market trends, macroeconomic factors)?

Why is this important to you if you're working in Market Intelligence?
Economic fluctuations, political changes, technological advancements, and other external factors can significantly impact forecasts, and you want to be able to see how it will impact your market.

In certain industries, regulatory changes can significantly impact market dynamics. You want to be able to both preempt it and hedge for it. Being able to conduct a scenario analysis arms you to understand and prepare for potential outcomes.‍

Achieving a 100% forecast accuracy is a pipe dream, but working with as small of a confidence interval as possible is the next best thing. Does the tool deliver this consistently?

This can be undoubtedly challenging to ascertain without usage of the tool. By conducting a proof of concept, it can provide you with a clearer scope of how it performs on exactly your data.

With easy access to the performance of your past forecasts, you can quickly view how your forecast has historically performed. Has it been consistent? If not, can you easily identify where it deviated?

Does the tool employ correlation to identify your relevant market drivers? The fallacy with correlation is well-known and it essentially robs you of identifying new drivers that could be more effective at predicting your sales.

Only using univariate models to forecast?
All, if not most, forecasting models built into planning tools (even the newest AI-based planning solutions) are solely based on or restricted to using straightforward univariate models. 

While these univariate methods are valuable, they don't account for external factors that might influence the variable being forecasted.

Why is this important to you if you're working in Market Intelligence?
In modern business environments, relying solely on univariate methods might not be enough for comprehensive forecasting, especially when external factors play a significant role. It's often beneficial to combine these methods with multivariate approaches or to use them in specific contexts where they are most appropriate.

A robust tool with the capacity to handle a variety of statistical and machine learning models greatly increases the chances of deriving accurate forecasts. (or at the very least, a minimized confidence interval).

In fast-paced markets, the software should be capable of ingesting real-time data and update your forecasts accordingly.

According to a study, 64% of annual forecast targets are outdated after four to six months, and research shows that only about 1% of businesses forecast with a 90% accuracy when forecasting one month ahead. The benefit is clear, but is the software equipped to ensure your forecasts can easily and efficiently factor them in.

Being able to nowcast and factor in higher-frequency data into your monthly forecast could potentially give you an advantage. (Some examples are key economic indicators such as PMI, Consumer Confidence, and CPI.) This means you could expect more precise forecasts.