Many sales managers dread the term “revenue forecasting” because, for them, it typically implies:
- Hours of tedious manual data manipulation
- Grappling with messy, inconsistent data submissions by sales reps
- Scrambling to find lost versions in email chains
- Dealing with time-consuming error corrections
Consequently, they might spend a significant portion of their workweek validating numbers and correcting errors and still have results they can’t trust.
But usually, all these only happen because they’re committing one of the cardinal sins of revenue forecasting: working from a spreadsheet.
The problems with using spreadsheets for revenue forecasting
Using spreadsheets for revenue forecasting is a common practice, but it has several limitations. Here are five of them:
- Spreadsheets can lead to version control problems when multiple team members are involved in updating and editing the document. It’s challenging to keep track of changes, which often results in errors and discrepancies in the data.
- Spreadsheets are not designed for real-time collaboration. More often than not, collaboration via spreadsheet involves emailing files back and forth, making it challenging to maintain a single source of truth. That usually means communication gaps and delays in decision-making.
- Revenue forecasting typically involves a lot of manual data entry and is prone to human errors. These errors can have a significant impact on forecast accuracy, potentially leading to financial mismanagement.
- Spreadsheets lack the automation capabilities required for complex forecasting tasks. Creating and updating forecasts often involves repetitive, time-consuming manual processes, which can be inefficient.
- Spreadsheets may struggle to handle large datasets efficiently. They can become slow, unresponsive, and prone to crashes when dealing with extensive data, which limits the ability to perform in-depth analysis.
The fragmented, disconnected nature of spreadsheets makes it difficult to get a comprehensive view of your sales pipeline and forecast trends. As a result, you might be stuck collating data from multiple sources to try and make sense of it all.
While spreadsheets offer basic tabulation and mathematical functions, they fail to provide the specialized revenue forecasting capabilities many sales teams need today.
They lack features like automatic data validation, outlier identification, trend analysis, customizable models, and forecast accuracy tracking. Without these, producing reliable forecasts is an uphill battle.
So what’s the better alternative?
Meet adaptive forecasting.
Step up your revenue forecasts with adaptive forecasting
Adaptive forecasting uses advanced analytics and machine learning to adapt revenue forecasts based on the most up-to-date data continuously.
This approach combines historical pipeline and closed deal data, rep input, and predictive insights to create a forecast that evolves in real-time alongside your business.
The best way to implement true adaptive forecasting is by using a purpose-built Revenue Operations (RevOps) platform. They provide the data foundation, analytics engine, and seamless workflows to enable adaptive forecasting across the revenue organization.
But how do you know you’re ready for a RevOps solution? You’ll know it’s time to make the switch when you need to:
- Gain a complete, 360-degree view of your revenue pipeline by connecting data from your CRM, email, calendars, and other sources. This holistic visibility allows for more accurate forecasting.
- Automatically update forecasts as deals progress through the pipeline based on stage changes, activity, and predictive signals. This keeps forecasts dynamic vs static.
- Enable your teams to easily run what-if analyses to model different deal scenarios and their impact on the forecast. This facilitates data-driven forecast updates.
- Provide transparency into the health of every deal and segment of the business so teams can course-correct early when needed.
- Reduce the time spent on manual data manipulation and number crunching so you can focus on high-value activities like coaching reps.
- Support complex sales motions like usage-based/consumption revenue models that are challenging to forecast accurately without intelligent automation.
RevOps platforms add structure and continuity to the forecasting process through automation. This saves time while enhancing accuracy and providing visibility into pipeline health.
It can leverage AI and machine learning to synthesize data from CRM, email, calendars, and more to generate predictive insights.
Other revenue forecasting mistakes to avoid at all costs
Going back to the cardinal sins of revenue forecasting, besides using a spreadsheet, there are several other mistakes organizations commonly make that can severely compromise their efforts. Avoiding these pitfalls is critical.
Let’s look at five of them.
1. Not validating forecast accuracy enough
Many organizations fail to validate forecast accuracy over time properly. They rarely go back and compare past forecasts to actual revenues realized.
The result: blindly accepting inaccurate forecasts month after month.
Without regularly checking forecast precision, it’s impossible to pinpoint where your forecasting model might be missing the mark. You’ll struggle to understand exactly what’s skewing forecasts — whether it’s a data, methodology, or personnel issue.
That means you can’t course correct and will allocate resources and make plans based on flawed projections, which will only come back to bite you later.
To address this, sales leaders should establish a consistent back-testing and validation process to routinely evaluate forecast accuracy. This process entails tracking forecast versus actual quarterly performance and scrutinizing any significant deviations.
By analyzing accuracy across various dimensions, such as by representative, segment, product line, and region, you can pinpoint areas where your projections are falling short.
2. Failing to account for seasonality trends
Another common pitfall is not accounting for seasonal fluctuations in revenue. Many forecasting models rely heavily on historical data to predict future performance. But if you do not factor in seasonal dips and spikes, your forecasts will be off-base.
Most markets have seasonal cycles. Ignoring seasons means your forecasts will miss predicting revenue swings tied to annual changes in market activity and customer budgets.
So, when these fluctuations eventually happen, you’re caught unprepared by the dramatic rise and fall in revenues and scrambling to mitigate sudden revenue shortfalls.
On the flip side, when revenue surges unexpectedly, you might find your organization understaffed and unable to capitalize fully.
To mitigate this issue, it is essential to analyze historical sales data to identify any recurring seasonal patterns. Additionally, integrating key dates like major holidays and annual budget cycles into forecasting models is crucial.
Regularly updating assumptions related to seasonality as the business evolves is equally important.
By incorporating seasonal adjustments into models, teams can foresee revenue changes and set expectations accordingly.
3. Relying too heavily on rep input for bottom-up forecasting
A common mistake companies make is relying too heavily on sales rep optimism or pessimism when creating bottom-up forecasts. While rep input is invaluable, over-indexing on rep sentiment alone can distort predictions.
Reps naturally have a bias — some might be perpetually optimistic, while others are overly cautious. Factors like the deal stage, commission incentives, or recent wins may also cloud a rep’s judgment of their pipeline.
As such, relying too much on rep input can lead to forecasts that are either too bullish or too bearish.
To create more accurate bottom-up forecasts, you must balance qualitative rep input with quantitative data analysis and good old judgment. Some best practices include:
- Implementing standardized deal scoring criteria that reps use to evaluate opportunities based on objective factors like lead source, deal progression, and buyer actions. This minimizes subjective opinions.
- Conducting regular pipeline analysis using historical win rates, sales cycle length, and other data to check rep forecasts for realism. Data provides an objective counterbalance.
- Rigorously validating rep forecasts through deal reviews. Your input balances rep optimism/pessimism.
- Blending rep bottom-up forecasts with statistical models based on historical performance. This balances qualitative and quantitative insights.
In all, the point is to leverage rep intuition while minimizing individual bias. A multilayered approach with rep input, data analysis, and management review creates more accurate and balanced forecasts. Relying solely on rep optimism or pessimism is a recipe for distortion.
4. Not updating assumptions regularly
Failing to update the assumptions that underlie your revenue forecasts can be quite costly.
Assumptions about pricing, market growth rates, competitive dynamics, and other factors often become outdated quickly in today’s fast-changing business landscape.
However, many organizations establish revenue forecasts and projections based on a static set of assumptions without revisiting them over time. As a result, the forecasts disconnect from reality as market conditions evolve.
This issue can be avoided by having a regular cadence for sales operations and finance teams to review and update key assumptions, such as quarterly collaboratively.
Documenting the rationale behind any assumption changes is also important for maintaining transparency and integrity in the forecasting process.
Overall, routinely refreshing your forecasting assumptions ensures alignment with current market trends and helps produce more accurate projections. This is a vital best practice for mastering revenue forecasting.
5. Lacking alignment between sales leadership and finance
One of the biggest challenges in accurate revenue forecasting is misalignment between the sales team — which sets targets — and the finance team — which handles revenue recognition.
When these two functions don’t collaborate effectively, it can lead to inaccurate forecasts that erode trust in the numbers.
To address this issue, organizations should take steps to better align sales and finance. Maintaining open and frequent communication between sales leadership and finance is crucial.
One way to do this is to establish cross-functional forecasting committees with representatives from both teams. This provides a forum for discussing pending risks, outliers, or assumption changes that could impact the forecast.
Another strategy is enabling earlier revenue recognition by establishing clear, data-driven milestones that sales reps must meet for deals to be counted. For instance, requiring completed demos or proposal approvals before counting a deal in the pipeline. This gives finance clearer visibility earlier in the sales cycle.
Overall, closing the loop between sales targeting and finance revenue recognition is invaluable for preventing discrepancies that undermine the integrity and accuracy of revenue forecasts.
Organizations that foster collaboration and communication between these teams through forecasting committees, open dialog, and data-driven milestones are better equipped to produce reliable forecasts.
How to choose the right revenue forecasting model
Selecting the optimal forecasting model is a crucial decision that impacts revenue projections and business strategies.
While senior leaders like CROs and CFOs often decide on company-wide models, sales managers may need to choose forecasting approaches for their teams in certain cases.
- For decentralized companies or large organizations with autonomous divisions, sales departments might have more independence. As such, the sales leader might be responsible for forecasting models tailored to their market, offerings, or customer profiles.
- In other cases, those leading specialized sales teams catering to distinct markets may have the expertise to determine the most fitting forecasting method.
- Additionally, in innovative cultures encouraging experimentation, sales managers might test different models to find the best fit.
- For fast-growing businesses with fluid roles, sales leaders might need to adopt forecasting models aligning with evolving conditions proactively.
With dozens of forecasting models available, choosing the right model can be challenging.
To make the right choice, you must consider factors like:
- The purpose of the forecast and key variables to include
- Data availability and relevance
- Required accuracy and time horizon
- Costs versus benefits
For example, causal models like regression analysis require abundant historical data. Time series models can capture patterns over time, even with limited data. Qualitative methods are preferable when quantitative data is lacking.
Understanding the pros and cons of forecasting models allows you to choose the right one for your situation.
That’s another point in favor of RevOps platforms.
An adaptive forecasting platform uses advanced algorithms to test out different combinations of models, automatically optimizing for accuracy based on your unique business drivers. This enables dynamic projections that continuously improve as new data emerges.
The table below provides an overview of common forecasting models and when to use them:
| Model | Type | When to Use |
| Straight-line method | Time series | When you expect sales to increase or decrease at a steady rate over time |
| Moving average | Time series | To smooth out fluctuations and reveal trends in data |
| Exponential smoothing | Time series | When your data shows trends but also variability |
| Trend projection | Time series | When you have historical data showing a clear linear trend to extend into the future |
| Simple linear regression | Causal | To understand the linear relationship between one independent variable and the dependent variable you want to forecast |
| Multiple linear regression | Causal | When forecast depends on multiple factors that relate linearly to it |
| Market survey | Qualitative | When historical data is unavailable, but industry expertise can inform forecasts |
| Sales force opinion | Qualitative | To supplement quantitative methods with insights from sales teams |
| Delphi method | Qualitative | To gain a consensus forecast from a panel of experts through iterative surveying |
| Visionary forecasting | Qualitative | For long-term forecasts based on expert visions of the future |
| Panel consensus | Qualitative | To combine insights from a diverse set of experts |
Overall, the best approach is often to use quantitative and qualitative methods.
Time series models like moving averages are useful for short-term forecasts, while causal models like regression help predict further into the future based on market conditions. Qualitative methods provide expert insights to complement the data.
Consider your unique business needs and resources to determine the right mix of models.
Conclusion
Revenue forecasting is a challenging but mission-critical process.
Avoid common pitfalls like relying on spreadsheets or failing to validate accuracy. Instead, leverage next-gen technology like Clari to enable adaptive forecasting. This gives you the insights needed to drive revenue confidence.
Remember to analyze your unique business conditions to select the right forecasting models. And don’t go it alone — connect with an expert to see how a purpose-built RevOps platform can transform forecasting for your team.