November 12, 2019
Your sales forecasts are more than a set of numbers showing what you think your B2B lead generation efforts will achieve.
They’re a window into how your business is performing. They tell you how much control you have over your business, as well as being a signpost of where you need to go to fix things.
Few people know more about forecasting than Rory Brown. Rory is co-founder and CCO at Kluster, the community-driven revenue analytics and forecasting platform. We sat down with Rory to talk all things forecasting. Here are five great pieces of advice that he gave us.
How do you build your B2B sales forecast? What data are you using and where are you getting it from? What are the criteria for your forecast? These questions are often overlooked in sales organisations - the system and the process behind forecasting - but it’s essential that you get something in place.
Forecasts where the process is not clear can cause significant issues. Often, the numbers will come from different teams and are built on different criteria. You will also have different parts of the business interpreting the projections in different ways. You need a framework that standardises forecasting across the company.
Define the criteria for your forecasting. For example, what are the timescales or budget? Think of how you are going to implement these changes in your organisation.
Once you have defined your process, you need to communicate it across your company. For sales forecasting to work, you must have trust and buy-in from the rest of the business.
Changing the forecasting process isn’t just about managing B2B data. It’s about managing people and their expectations too. Some salespeople may be worried that these changes can affect their earning power.
Create some internal champions for your new forecasting process. Talk to your sales leaders and bring them into the process. They can be your advocates, working alongside you to gain buy-in and trust.
Sales forecasting only works if people can be held accountable for it. Choose people in your sales team who are responsible for forecasting - leaders, for example. They will be your point of contact for everything related to forecasting. If something goes astray and the forecasting is inaccurate, they are the people you need to speak to. This sort of accountability helps you improve accuracy.
Monitor each team that contributes to your forecast. Who were the most accurate team in a specific time period? Who were the least accurate? Dig into your data - once you have identified which teams are inaccurate with their forecasting, you can take steps to help them. Can upskilling or training help them forecast more accurately in the future?
Want a clearer picture of the B2B sales KPIs that matter? Look no further than our free infographic. Simply click below to view it!
Too many companies employ just one forecasting system – this is the wrong approach. Why? It’s because sales forecasting can only be accurate at a certain point in time. As time goes by, your forecasting may become inaccurate. We call this a ‘visibility gap’.
Forecasting should provide confidence to your business. Rory recommends using more than one forecasting model to boost accuracy. He identified three models you can try.
Category forecasting is a common method of forecasting in sales teams. It’s where salespeople identify ‘best and commit’ in the CRM.
The flaws in category forecasting are that you need the total pipeline to be visible if you are going to make judgements on that pipeline. You also have to wait until each salesperson has made their judgements on the B2B pipeline.
Weighted forecasting is applying historic stage conversion rates as a weighting to your live sales pipeline.
This model is effective because, assuming your weightings are statistically sound, you can be confident in your forecast and that it will stand up to scrutiny. You also do not have to wait for the entire sales team to judge the pipeline to build an accurate picture.
On the other hand, you still have to wait for the entire pipeline to be added before the weighting is accurate.
In machine learning forecasting, you take a multitude of B2B data points and historic trends, then make a prediction about where your revenue is likely to end up.
This model removes the visibility gaps that you get in category and weighted forecasting. This is because machine learning can use past data to predict how much pipeline you are going to have in your sales funnel before the time period starts. Then, it can make a prediction for the forecast.
It can’t be 100% accurate, but you can be more confident in machine learning forecasting in the early stages of the time period than you can with any other model.
You can find out more about these models in this article that Rory wrote for the Kluster blog.
Use two, or better yet, all three of these methods to improve accuracy and get a clearer picture of sales trends.
Keep an eye on your forecasting so you can improve it going forward.
Think about questions such as: at what point in the quarter does each system become fairly accurate at predicting revenue? Then, define why. It’s essential that people in your business know when the forecast becomes accurate, so they can take steps to address any issues (e.g. increasing sales team headcount).
Sometimes sales teams lose sight of why they are being asked to produce forecasts. It’s so they can plan ahead. This is critical for business. It’s important the forecasting is credible and aligned to your wider sales strategy.
We hope you found this article useful. Thanks to Rory for sitting down with us. We can’t wait to do it again soon!
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