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.
Why you need to be 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 capacity plans 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 quality forecasting
Because 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.
Ideally, some of these goals will reinforce one another—reliable pipeline data often comes from having a collaborative sales culture, for example. Similarly, the more reliable your data is, the more actionable your forecasts become.
Predictive analytics is easier than you think
Predictive analytics is already transforming many areas of business. Sales forecasting is no exception, yet terms like “predictive analytics” and “machine learning” can still be intimidating to many of us. To reduce this anxiety, Abe presents a short example that explains how predictive analytics improves forecasting:
A tech company recently asked Deloitte to produce a predictive model to improve sales forecast accuracy. To create this model, Deloitte leveraged the company’s available pipeline data from the previous few years, with customer and employee names removed. Deloitte then filled in the gaps in this data by extrapolating from historical trends using machine learning. Once it had done this, it then used the data to build two predictive forecasting models, one that calculated the probability that any given deal would close, and another that predicted the time frame in which that close would happen. When combined, these models provided highly actionable, very specific recommendations to the company’s sales team: “push opportunity number five to qualified within the next 10 days or you’re going to lose it!”
Importantly, Deloitte was able to build these predictive forecasts in 8–12 weeks—a timeline that could be feasible for many companies.
Where to begin: building a sales forecast
If your company is interested in transforming its sales forecasting, but not sure how to begin (data? collaboration? predictive analytics?), it can help to assess some requirements, particularly the following:
- 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?
Grappling with these questions can help formulate a roadmap that prioritizes the most essential elements for your situation.
Sales forecasting: a journey, not a destination
Sales forecasting isn’t a one-size-fits-all solution. The choices you make depend on the particulars of your organization, including your company culture, stakeholders, selling methodologies, and the particular data you need.
From a larger perspective, it can be helpful to remember that forecasting is as much a journey as it is a destination. The more you make forecasting a part of your sales processes, the more you’ll start to pull in the right stakeholders, and the more you’ll figure out what you need to prioritize. Keep forecasting, keep iterating, and you’ll disrupt yourself ahead of the competition.
View the full online session or the entire three-part webinar series to learn more about sales forecasting, or to see more ways to improve sales effectiveness.