Brooke Hattie · May 21, 2024 · Case Studies
About this engagement
After a large shift in the market, our client's forecast failed badly. They lowered it mid-quarter and still closed with less than 30% of what they had originally forecast, and the miss cost them trust.
They brought us in to make their sales managers' forecasting systematic, data-driven and reliable.
Our partnership enabled the following results:
The Challenge
The client forecast by taking blended conversion rates from past quarters and applying them to the current pipeline. That relied on historical data from a different market. With a downturn under way and marketing changing its strategy, those rates no longer described how their pipeline would convert, and the forecast stopped being accurate.
The Solution
We found the root cause of the misses and rebuilt how the pipeline is measured, so the CRO and sales managers forecast from how each channel actually converts and can see their revenue gaps, the deals that need attention and where to put resources.
"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
At a high level, here is what changed:
| Before | After | |
|---|---|---|
| Blended conversion rates from a different market | → | Conversion rates by channel, adjusted as each channel performs |
| Every MQL treated the same | → | A dynamic SQO stage, set where a lead historically reaches at least a 25% win rate |
| A forecast built by hand | → | A forecast automated in Salesforce, updated in real time |
| A fixed model | → | A model updated regularly from analysis and feedback |
And now, here is how we got there.
Finding the root cause
We started with how the client forecast and why it had been so far off. The analysis showed two problems: a reliance on historical data from a different market, and past conversion rates applied without allowing for the changes in the market and in marketing's strategy.
A standard pipeline with dynamic SQO stages
Different lead channels were converting at different rates, so we introduced Sales Qualified Opportunities to make the pipeline standard and predictable. The SQO stage is dynamic: it sits at the point where a lead has historically reached at least a 25% win rate, which predicts more accurately which leads will convert and standardizes pipeline coverage.
Forecasting by channel
We broke the pipeline down by channel, such as demo requests, content marketing and sales outbound, and gave each its own conversion rate, so the forecast adjusts with how each channel performs.
Automated in Salesforce
Flows and custom fields in Salesforce track the SQOs and automate the forecast, so it updates in real time. We then kept updating the model from ongoing analysis and feedback, so it stayed accurate as market conditions changed.
The Results
Within a month of the project finishing, the client closed the following quarter within 5% of their initial forecast. With an accurate forecast earlier in the quarter, the team had the time and resources to close any revenue gaps, which made their decisions more strategic.
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