CASE 4 - DATA-DRIVEN DECISIONS IN MERGERS & ACQUISITIONS

BACKGROUND

The merger and acquisition (M&A) landscape is booming. In 2022, global M&A transactions reached an impressive value of $3.4 trillion. Yet, a staggering statistic remains: despite the growth and volume of these transactions, between 70% and 90% fail to add the anticipated value for shareholders. With the rapid evolution of technology, many companies are turning to M&As to acquire innovative assets, intellectual property, and renowned brands to better position themselves in the market. These acquisitions can present uncertainties, especially in the tech-dominated era. To navigate this, many are turning to data analytics, with the latest AI and analytics solutions promising informed decision-making and foresight.

With the wealth of data available, there's a significant opportunity to design a robust scoring system to rate the potential success of an M&A. Much of the current data used for evaluation comes from third-party sources, surveys, or interviews. This data often measures innovation investments (like R&D expenditure) and their resulting outputs (e.g., patents). Our goal is to harness patent data and other relevant datasets to develop a scoring method for M&As. By doing so, we aim to provide stakeholders with a hypothetical score, shedding light on the potential success of an acquisition or merger.

PROBLEM DESCRIPTION

Your team is tasked to design a robust scoring system that can evaluate the potential success of an M&A transaction. Although there are many evaluation methods, a significant part of the data used comes from third-party sources, surveys, or interviews. The team should use patent data and other relevant data sets to develop a scoring methodology for M&A. This can provide stakeholders with a hypothetical score that provides insight into the likelihood of a successful acquisition or merger.

Data is available, but there are still opportunities on how to develop the tools for assessing available data. Datasets aim to capture innovation inputs (e.g. R&D expenditure) and to create practical indicators for measuring innovation outcomes (e.g. through patents). This case is based on using patent data in combination with other sources to develop a data-driven method for assessing mergers and acquisitions.

To develop this method the following approach is suggested.

• Project Specification

  • Identify - Indicators on firm-level innovation activities
    • reference work for finding datasets or metrics.
    • available tools
    • reference projects

• System Architecture

  • Propose a conceptual structure of a possible web scraping solution.

• Development of Minimum Viable Concept

  • Using Figma or Streamlit demonstrate how the system can be deployed.