Despite bold goals and much effort expended, our client was struggling to grow. Acquisition was a primary strategy, however, they lacked an effective means to prioritize efforts across hundreds of potential M&A targets – resulting in significant wasted time and energy. First, we helped identify the causal factors that would make potential acquisitions more likely to be successful fits. Next, we developed a strategic data integration and assessment tool to identify and prioritize targets using a wide range of structured and unstructured information, knowledge of causal drivers, and encoding the expertise of their best evaluators.
We used natural language processing and machine learning tools to build a dataset of potential M&A targets from structured and unstructured public data, including news sources and government databases. The dataset included characteristics of the organizations (customer base, revenue, regulatory events, public incidents) and their surrounding communities (population & economic growth, demographics, etc.), and integrated sensitive qualitative information and assessments.
We built a Bayesian ranking engine, informed by multiple data sources and pre-processing, causal logic, and encoded expertise, to classify and prioritize M&A targets. Leaders at multiple levels were able to directly interact with and interrogate the model’s results and recommendations in-depth using a dashboard created in a lightweight COTS data visualization tool, use it to prioritize efforts, and also contribute information to enhance and update rankings.
The insights gained in analyzing and prioritizing targets had further uses. We worked with the CEO and operations and sales executives to refine their sales strategy and process. The predictive model and data assets were integrated into their Salesforce platform to give leadership greater visibility on performance and outlook and provide front-line sales teams with actionable information to improve their productivity.