Welcome to The Revenue Engine Podcast. I'm your host, Rosalyn Santa Elena and I am thrilled to bring you the most inspirational stories from revenue generators, innovators, and disruptors revenue leaders in sales, in marketing and, of course, in operations. Together, we will unpack everything that optimizes and powers the revenue engine. Are you ready? Let's get to it.

What are the key benefits of forecasting? Do we even need it? I've been at companies where the team doesn't really have a robust forecast process. Why is forecasting so challenging? Well, it's a little like predicting the future, right? Unless you can predict the future or you have a magic crystal ball, it can be challenging.

Forecasting can be a mix of art and science. Which leads to subjectivity, right? The goal is to really remove as much subjectivity as possible and add as much actual data as possible. And this actual data will include historical performance and insights. And what you know about the account or the deal, then you build the right processes to capture the data points that help predict the likely outcome.

So, what are the things that organizations get wrong? I think by taking a look at what things people are doing wrong, it'll help you formulate what to do, right? So here are 10 things that make forecasting challenging

1: teams working in silos without having the right visibility and the right information at the right time.

2: not having the right insights or looking at the wrong insights to really help drive that forecast accuracy

3: not having accurate or complete data, as we say, everything starts with data. So everything starts with having the right data.

4: having unclear objectives. If you don't have clear goals, how can you measure performance and track towards those goals?

5: focusing on the wrong metrics or different metrics across the teams.

6: having processes that are not well-defined or they're just too subjective. Think about how many times you have sales stages that are unclear or they're subjective. And what does that, what happens? They lead to inconsistency in the pipeline.

7: having processes that are just way too complicated or too complex. How many times do you have way too many forecasting categories? And then what happens is people are confused, right? What belongs in what category? It's so unclear. So users end up just picking one.

8: manual processes, manual process that can lead to errors and lack of adoption. If the process is super manual users will tend to take shortcuts or they skip steps.

9: lack of governance or oversight in the process, not having those clear policies and guidelines or having really inconsistent processes.

10: unclear expectations. So specifically the who, the what, the where, the when, the why, and the how right when you have unclear deadlines or unclear deliverables or unclear roles and responsibilities that makes forecasting super challenging.

And number 11, I'm going to give a bonus. Number 11, not having the right level of training and enablement on those processes, policies, definitions, metrics, objectives, and the system. So with these 10, well, 11 things that can go wrong, you can start to look at your forecasting process and start to eliminate these from your organization.

And yes, you need accurate forecasting. Why? Because revenue predictability is more important than ever before. Having the right level of visibility and insights into the pipeline, enables the team to focus on the right deals at the right time and to pivot or adjust strategies as necessary. And that allows the organization to plan early to invest early and to make the right business decisions. And lastly, accuracy helps build confidence with the board, with shareholders and with investors, which is what we all want and need. So good luck with your forecasting process.