Where did the time go?
It’s September, it’s the end of Q3, and we have three months left to prepare for the next roller coaster year.
2022 was no ordinary year, with market downturns, war, and a looming recession. If you’re scratching your head, wondering how to create accurate sales forecasts after likely missing the mark this year, you’re not the only one.
I’m Erin Gondeck, Solutions Consultant at Pigment and expert sales modeler. I answered your key end-of-year forecasting questions in an AMA on the RevOps Co-op Slack community. I also coordinated with Hanyul Lee, North America Sales Leader at Pigment, to get insights on how sales leadership approaches sales forecasting.
As a recap of the AMA, I’ll take you through our best tips for accurate forecasts and pitfalls to avoid as you approach 2023.
What are some forecasting methods and approaches to improve sales forecasts?
Short answer: leverage all data available to you and practice cross-functional collaboration to significantly improve your sales forecasts.
It may seem like a no-brainer that better data leads to better forecasts, but lack of data quality plagues businesses and costs companies $9.7 million a year; the US as a whole faces a loss of $3 trillion due to substandard data.
Data comes from various sources and isn’t restricted to numbers. It can include opinions from subject matter experts across the organization.
You arrive at the highest quality data sets through a combination of historical data, automated data flows, and gathering inputs at different levels in the hierarchy. Prioritize always having timely data from your CRM to have an up-to-date view of opportunities.
Make sure to use scenario planning to chart out the best case vs worst case outcome, while reviewing seasonal and historical trends, win rates, and period-specific sales team performance to derive the most comprehensive, well-rounded sales forecast possible.
Finally, cross-functional collaboration provides fresh perspectives on your forecasts, for example, seeking input from customer support teams on expand opportunities. The right data combined with the right set of eyes increase the probability of high-accuracy forecasts.
How can Sales and Finance teams collaborate on forecasting?
The relationship between Sales and Forecasting typically follows this pattern: Sales plans and Finance cuts back, often by 25% or more.
How can Sales teams create more realistic forecasts? More importantly, how can Sales and Finance teams collaborate and build a better working relationship?
The answer lies in the location where all that work takes place.
Having a single tool or platform in which Sales and Finance build the forecast will help add both teams' inputs to the forecast, work out of the same datasets, and agree on goals, KPIs, and dashboards to share.
Forecasting for a range of outcomes — including best case, worst case, and optimistic forecasts — can be achieved through what-if scenarios which help communicate much more realistic expectations while allowing space for a stretch goal.
It can also help for Sales and Finance to build trust by tracking the historical forecasting accuracy of both groups to enforce logical, data-driven decisions and forecasts.
What are common pitfalls to avoid in end of year forecasting?
End of year forecasting can feel chaotic as the company rushes to close last minute wins and meet revenue goals. It’s important not to compromise on levers such as gathering insights through executive touch-points early on in the process. Here are other practices to avoid during end of year sales forecasting:
- Using out-of-date or stale data to build your forecasts
- Not reaching alignment on key metrics to use through the year
- Relying too heavily on intuition-based guesses and decisions
- Forecasting too far ahead (two quarters or more) during economic instability
What types of data goes into sales forecasting?
The type of data and inputs that go into the sales forecasting process vary from company to company, but here are some examples:
- Start with Opportunities from the CRM
- Incorporate Historical Win Rate, Target Segment, and Industry
- Pipeline Coverage Ratio
- Average Time to Close and Time in Stage
Make sure to include commentary from front line sales leads, commit inputs, and also incorporate MEDDICC or other sales methodologies to help qualify deals and other key considerations in a sales cycle.
Renewals can be included in forecasting — use deal types to distinguish between new and expand metrics. A few key Renewal Forecasting KPIs commonly tracked are:
- Renewal date
- Commercial terms such as auto-renewal or renewal uplift
- Net Promoter Score (NPS)
- User activity such as log-ins
What's the best approach to setting sales quotas in the sales forecasting process?
It’s best to approach sales quotas with the mindset that the quota is the output.
The number of reps needed and the territories assigned to them act as inputs fed into the quota setting process to extract the resulting quota as a whole.
By taking this prescriptive approach to headcount needs and territories, you allow the organization to recognize the quota setting process as a margin exercise as well as a revenue exercise.
How should you approach sales pipeline coverage?
The sales pipeline coverage is the ratio of your total sales funnel value to your revenue targets for a specific period. In other words, your pipeline coverage quantifies the excess pipeline you have as compared to your total quota.
The best practice is to track the sales pipeline coverage ratio starting at the beginning of the year to avoid scrambling to meet targets at the end of the year.
However, be mindful not to look at pipeline in isolation. Rather, look at the broader 3-part picture of activities, opportunities, and attainment. Ensure a healthy level across the three categories, focusing especially hard on activities such as meetings and attainment towards the end of the year.
What’s the best tool to consolidate data points for sales forecasting?
Without hesitation, Pigment!
Pigment offers all the features needed to create highly accurate sales forecasts that are integrated with all your data sources. The platform also allows for better cross-functional collaboration.
Pigment has native data connectors with Salesforce, HubSpot, NetSuite, and other sources including data warehouses. This makes the platform a great central location to consolidate all your data and model forecasts.
The native one-click scenarios feature enables you to prepare best vs worst case what-if scenarios that address all possible future outcomes. Also, data flow is a cinch with the ability to schedule your data imports and update changes in your model into connected spreadsheets and presentations with the click of a button.
