Recently, I joined forces with Abe Awasthi, Senior Manager at Deloitte, for the webinar “Sales forecasting fundamentals,” hosted by the Sales Management Association, the first in a three-part series on sales force effectiveness. In the session, we walk through some recommended best practices for sales forecasting and discuss some roles that predictive analytics and machine learning are starting to play in the sales forecasting process. As Abe describes, there is more disruption today than at any other time in modern history; intelligent companies are figuring out how to disrupt themselves before someone new comes in and does it to them.The importance of Sales Forecasting Sales forecasting, which Abe defines as “a measure of how the market will respond to your go-to-market efforts,” is a dynamic exercise that adds value across your organization. For example: finance relies on your forecasts to develop budgets for sales capacity planning and hiring. Production uses your sales forecasts to plan their cycles. Your forecasts help sales ops with territory and quote planning, supply chain with material purchases and production capacity, and sales strategy with channel and partner strategies.These are only a few examples. The key insight here is that at many companies, these processes stay disconnected, and that disconnection can produce adverse business outcomes. If information from your sales forecast isn’t shared, for example, product marketing may create demand plans that don’t align with sales quotas or sales attainment levels. This leaves you with too much inventory, or too little inventory, or inaccurate sales targets—all mistakes that hurt your bottom line. Committing to regular, quality sales forecasting can help avoid them.The components of a quality sales forecastBecause they touch so many other aspects of the organization, your forecasts should be as dependable as possible. As Abe explains, the way to do this is to make sure your sales forecasts exhibit three characteristics. That is, your forecasts should be:
- Reliable. This depends on two things: the way you collect data and the rigor of your analyses.
- Collaborative. This includes not only work flow, which should include all necessary stakeholders, but also data quality, which comes from solid business processes and collective discipline—fundamentally collaborative activities.
- Actionable. This goal may seem obvious (why else have a forecast?), but sales forecasts are often not presented in a way that makes them actionable. To make them so, Abe offers two recommendations. First, you should provide ‘what if’ modeling capabilities to your sales reps, which encourages them to take a more holistic view of their deals and account plans. Second, deploy dashboards that can present different levels of insight to different stakeholders in the organization.
- Stakeholders. Who is going to inform the forecast (sales reps, managers), and who will be using it (sales leadership, sales operations, finance, supply chain)?
- Time horizons. How far in advance will you be forecasting? The current quarter? Rolling 12 months?
- Frequency of refresh. How often will you need to update your forecast? This can depend on the stability of your market and the length of your sales cycle; the less stable the market or the shorter your cycle, the more often you will need to refresh.
- Granularity. How specific do you need your data to be? Do you need to drill down to segments, cross segments, sub-segments, product lines, or even SKUs?