To help sales teams qualify their deals more easily, we first understood their existing qualification process. Then we developed a machine learning model that learned what successful deals looked like, based on thousands of historical deal profiles of which the outcome was already known. The model is now used to score open opportunities based on their likelihood of being won. This enhancement is fully embedded into the sales team’s existing process, ‘simply’ offering a small improvement in the form of a decision support tool.
Through a dashboard embedded directly in the CRM tool, sales teams are able to attain a ‘second opinion’ on their deals’ likely success. Additionally, the sales leader can look at all sales teams’ deals together, and get a ‘second opinion’ on their overall forecast. For example, in the below graph, we can see insufficient opportunities in the higher-win category to achieve the order intake target, which indicates a risk.
Further filtering can be done by forecast category, business segment, sales stage, team, and expected order date to create a top list of deals requiring attention, which can then be discussed with the relevant teams and targeted support given where needed.