How we helped our client improve their forecasting accuracy with our fractional RevOps services.
The Challenge
Following a large macro-environmental shift, our client's forecasting method, which relied heavily on historical data from past times, failed dramatically.
After their initial forecast was significantly lowered mid-quarter, they closed with less than 30% of their original forecast.
This miss led to a loss of trust and highlighted the need for a more systematic and data-driven approach to forecasting.
Given the market downturn and marketing's shift in strategy, the client's typical approach of looking at blended conversion rates and applying that to their pipeline was no longer accurate.
The Solution
We helped our client improve their forecasting by making the sales managers' process more data-driven and reliable.
How? We ran analyses to understand the root cause of their forecasting inaccuracies, and uncovered their reliance on historical data from a different macro environment and applying blended conversion rates without considering changes in the market and marketing strategy.
Recognizing that different channels had varying conversion rates, we created a new dynamic SQO stage. This was based on historical win rates of at least 25%, to standardize their pipeline coverage. This allowed the CRO and sales managers to more accurately forecast and therefore also understand where their revenue gaps were, enabling the team to identify deals needed attention and prioritize resources accordingly.
We integrated these processes into Salesforce for real-time updates and continuously improved the forecasting model based on ongoing analysis and feedback.
""Lean Layer is very data oriented and when analyzing a problem, they are able to quickly identify what data should be pulled to uncover what is happening at the root of a problem. During their time supporting my team, Lean Layer helped me reconfigure our forecasting model accounting for changing market conditions and predicting trends in our pipeline coverage for future quarters."
- Director of Sales
The Approach
Learn more about our approach on how we solved this for our client below:
- Analysis: We began by understanding how the client was currently doing their forecasting. We identified that their reliance on historical data from a different market environment was a key issue.
- Evaluation: We performed a deep analysis to find out why their forecasts were so far off. We discovered that they were applying past conversion rates to current operations without considering changes in the market and their marketing strategy.
- Standardization of Pipeline: We implemented Sales Qualified Opportunities (SQOs) to create a standardized and predictable pipeline. Different lead channels were converting at different rates, so we created dynamic stages in Salesforce to account for these variations.
- Dynamic SQO Stages: Recognizing that not all MQLs are created equal, we set up dynamic SQO stages based on when a lead historically reached at least a 25% win rate. This allowed us to more accurately predict which leads were more likely to convert into sales.
- Integration with Salesforce: Using Salesforce, we automated the forecasting process. We created flows and custom fields in Salesforce to track SQOs, which allowed for real-time updates and more accurate forecasting.
- Channel-Specific Forecasting: We broke down the pipeline by different channels (e.g., demo requests, content marketing, sales outbound) and assigned conversion rates to each. This allowed us to dynamically adjust forecasts based on the performance of each channel.
- Continuous Improvement: We regularly updated the forecasting model based on ongoing analysis and feedback. This ensured that the client’s forecasts remained accurate and reliable despite changing market conditions.
The Impact
Within a month of project completion, we were able to help our client improve their forecasting accuracy to close out the following Quarter within 5% of their initial forecast.
This improvement led to more strategic decision-making as the increased accuracy earlier in the quarter meant the team had the time and resources to mitigate against any revenue gaps.
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