Managing a large pipeline of potential deals can be a daunting task. Deal teams often struggle to prioritize targets effectively and stay focused on the highest value opportunities. However, by leveraging first- and third-party data with Midaxo Deal Scoring, dealmakers can more efficiently focus their pipeline management, prioritize targets based on their deal thesis, and make informed decisions.
Deal Scoring
Midaxo Deal Scoring empowers dealmakers to define scoring metrics based on their ideal target profiles. This powerful platform capability applies a common scoring model to each target, calculating an overall score based on various factors defined in the model. Metrics can be assigned relative weights and graduated scores, reflecting how closely a target aligns with the desired criteria. The platform then applies these scoring parameters to target data imported from Midaxo Insights, as well as any manual inputs, to calculate an overall score for each target. By organizing targets based on their overall score, teams can consistently and objectively identify the targets that best align with their dealmaking goals.
Midaxo Insights
One powerful platform capability that can enhance your team’s pipeline management is Midaxo Insights. By integrating with popular business data sources such as ClearBit, Crunchbase, and Pitchbook, Midaxo Insights further simplifies the deal-screening process. The ability to populate target profiles with comprehensive data — including revenue, headcount, prior funding, and location — enables a more informed evaluation of potential deals and saves significant time and effort for deal teams.With the help of an open API, Midaxo Insights can also integrate with other data sources, expanding the depth of information available to dealmaking teams.
Calculated Fields
To further enhance scoring accuracy, Midaxo Deal Scoring has introduced calculated fields. These fields allow dealmakers to define complex scoring metrics based on multiple data points from a target’s profile. For instance, metrics like “Market Position” can be calculated based on a target’s size and revenue growth compared to the overall market. By incorporating multiple data inputs, calculated fields enable a more nuanced assessment of deal opportunities.
Greater Visibility and Improved Focus
Deal Scoring provides deal teams with greater visibility into their pool of potential acquisition targets. It helps filter out irrelevant targets, enabling teams to focus on those that best align with their deal thesis. This streamlined approach reduces deal risk by standardizing the target filtering process and deprioritizing targets that fall outside of the organization’s risk parameters. Additionally, Midaxo Deal Scoring allows for flexibility, enabling teams to adjust scoring metrics and incorporate machine learning to drive continuous improvement in the dealmaking process.
Use Cases
These capabilities are particularly beneficial for active acquirers with large pipelines because the data intelligence helps them focus on the most suitable targets and avoid overlooking potentially valuable opportunities. It also serves organizations with specific and complex target profiles, allowing them to identify targets that might be easily missed with manual efforts alone. Moreover, dealmakers looking to implement continuous improvement in their deal processes can leverage Midaxo Deal Scoring’s integration with machine learning to update their scoring parameters based on past deal outcomes.
Conclusion
The Midaxo Cloud offers a smart and efficient approach to prioritize targets in a deal pipeline. By leveraging Midaxo Insights and Deal Scoring capabilities, corporate development teams can save time, streamline their screening process, and make well-informed decisions based on their ideal target profiles. With improved visibility and focused efforts, organizations can maximize the value derived from their dealmaking activities. To learn more about Midaxo Deal Scoring and experience the benefits, schedule a demonstration of the Midaxo corporate dealmaking platform today.
