Why All Deals Aren’t on the Same Path to Closure

Are two deals in the same stage really on the same path to closing by the end of the quarter?

When it comes to forecasting, many sales teams fall into the trap of applying a single conversion rate across the entire pipeline based on deal stage or forecast status. Often, judgments made by AEs or managers when forecasting an opportunity to close in-quarter lack an easy reference for validation. For accuracy and better outcomes, it’s essential to understand the mix of opportunity types within your pipeline and how various deal characteristics affect win rates and time to close. Let’s explore the key factors that impact forecasting and some practical adjustments you can make to forecast with greater confidence and improve your quarter-end results.

1. Key Characteristics Impacting Forecasting Accuracy

Deal types vary widely in their likelihood of closing, and certain deal characteristics can significantly impact both win rate and close time. These include:

• Deal Size: Larger deals often have lower win rates than smaller ones due to the additional scrutiny they face prior to purchase.
• Deal Type / Opportunity Source: New customer sales typically yield lower win rates than cross-sell or upsell opportunities with existing customers, where a relationship already exists.
• Product or Solution Offered: Established solutions with strong reference cases tend to have higher win rates than new or unproven offerings in competitive markets.
• Deal Aging: Both the time elapsed since the opportunity was created and the time a deal spends in its current stage can affect its probability of closing.
• Geography: Regions with new markets or strong local competitors often show lower close rates.
• Sales Channel: Channel deals might appear to have higher win rates, as partners may not log early-stage losses in your CRM, while direct deals offer complete visibility.
• AE Tenure: Deals managed by newer AEs generally have lower win rates than those handled by experienced AEs, although very tenured AEs may encounter challenges with adapting to evolving buying habits.

Given these factors, segmenting your forecast into cohorts of similar deals before calculating conversion rates enables more precise forecasting that reflects your pipeline's unique composition.

2. Addressing Variability at the Deal Level

CRM systems generally allow only one value per variable for each opportunity, making it difficult to account for variability. Here are some approaches to handle deal-level uncertainty and make forecasts more accurate:

• Deal Value: In SaaS, deal values are often measured in ACV or ARR, which helps reduce variability due to contract term lengths. However some software companies may need to forecast in TCV or deal with a mix of perpetual and on-premise. Where multiple options are offered, separate opportunity records may need to be used to address this variability.
• Win Probability: Instead of relying on fixed stage-based probabilities, calculate win probability using historical rates for similar deal cohorts. This shift to take into account deal types, rather than just stage-based estimates, improves accuracy.
• Win Timing (Close Date): Forecasted close dates are often optimistic. Use historical time-to-close data per deal type cohort as a guide, and consider marking certain opportunities in future quarters with a "Current Quarter Upside" flag rather than adjusting close dates prematurely. This reduces quarter-end “slippage” and offers a clearer view of in-quarter pipeline conversion.

3. Recommended Fields for Enhanced Forecasting

To improve forecast transparency and accuracy, consider adding these fields to your opportunity records:

• Deal Win Probability: A calculated probability of closure, regardless of timing, based on historical data for similar opportunities.
• Most Likely Close Date: The most probable close date, based on timing data for similar deals at the current stage.
• Current Quarter Win Probability: The likelihood of the deal closing within the current quarter. Comparing this probability to overall probability will help sanity check forecast close date.
• Forecast Status: Defaulted to a probability-based status that can be overridden by AEs based on additional data and insight.
• Current Quarter Upside Flag: To use for deals forecasted in future quarters with potential to close earlier (i.e. pull-in target opportunities).
• Weighted Pipeline Values: Both for current and future quarters, accounting for realistic probabilities and avoiding double counts.

With these fields, managers can better understand both the AE’s judgement and how deals align with historical data. Deals with low probability but a current quarter close date may need scrutiny, while future quarter opportunities could potentially be pulled in.

While qualitative insights like prospect engagement and joint action plans can further inform accuracy, these quantitative data points should form the foundation—especially for early-stage and future-quarter pipeline.

Weighted pipeline approaches are designed to work at a macro level and should be just one of several forecasting tools. However, they are useful for understanding pipeline coverage, identifying conversion rate improvement areas, and determining the level of investment needed in new pipeline generation activities.

4. Key Questions for Ongoing Improvement

To maximize the value of this approach, consider these questions:

1. How feasible is it to calculate probability and timing with your historical data? Can filtering levels be adjusted to avoid volatility when historical data pools are too small?
2. Could early detection of potential issues lead to more proactive solutions that increase the probability of deal closure and allow the forecast to increase?

• Consider offering greater support to Rookie AEs, like added manager oversight or pairing with a more tenured AE who gets a cash only bonus if they help close it.
• Introduce promos, particularly on new solutions to improve in-quarter win rates and increase future reference cases.
• Drive new pipeline generation in quick closing deals to make up shortfalls in-quarter.
• Focus on high-chance opportunities and assess alongside data like engagement activity.

By segmenting opportunities, defining probability and timing estimates, and adding key fields to opportunity records, you better equip AEs with valuable intelligence to assign realistic forecast status and close dates. Accurate, consistent information at the opportunity level creates a stronger foundation for building forecasting models, reducing quarter-end surprises and improving future quarter pipeline coverage.

Let me know your thoughts and what other approaches you’ve used to improve forecasting accuracy in your pipeline!