Developing a Revenue Forecasting Model for a Fast-Growing Clinical Trial SaaS Company
Problem
A rapidly-growing software as a service (SaaS) business serving the clinical trial management needs of biopharmaceutical companies needed support to better forecast bookings to their board of directors which included directors from their primary owner, Goldman Sachs Growth Equity.
Given the complexity of their global target market, varying in customer segments and channels, the company was struggling to forecast monthly unit and revenue bookings, putting pressure on revenue and cash forecasting.
Approach
Excelerate first set out to understand the sales process and buying behaviors. In understanding both how the sales team worked and how customers made decisions, Excelerate could identify bookings segments as well as drivers for each of those segments. To further unpack the identified drivers, Excelerate dug into the client’s CRM data to uncover trends in win rates, booking curves, probabilities by stage, and decision trends by segment.
With this knowledge, Excelerate encouraged the client to break up the model assumptions based on five key segments. Excelerate found significantly different behaviors between direct sales to BioPharma companies versus more indirect sales to Contract Research Organizations (CROs). Furthermore, within BioPharma, Excelerate uncovered three distinct groups – Small Biotechs, Mid-Sized Pharma, and Large Pharma. Within CROs, Excelerate identified two subsegments in small (niche – usually focused on specific indications and therapeutic areas) CROs versus large global CROs.
Identifying these five key segments proved critical as they all behaved differently in the sales cycle, showing different historical trends around win rates by stage, booking curves, opportunity growth and, most importantly, timing of the booking (which also meant the first opportunity to recognize revenue).
Results
Excelerate ran significant analysis across the segments using CRM data to develop booking curves, win rates, deal creation rates, pricing bands, probabilities by stage, and various attributes to inform timing of won deals. Timing of won deals proved particularly important as it served as a data driven way to, in some cases, override the salesperson’s often optimistic view of deals in the pipeline to help make the forecasted booking month more accurate.
The resulting model created a consistent +/- 10% variance on monthly unit and revenue bookings with most months hitting a +/- 5% variance. This built an increased level of trust between the company's executive team and their board as revenue and cash modeling became significantly more reliable.