The M&A world continues to evolve as transactions are becoming more diverse and complex. Timelines are getting shorter and acquirors have less time to assess their targets but more pressure to justify their acquisitions. Acquirors must simplify the process of acquiring a target, while simultaneously improving the accuracy of their predictions about the acquisition's profitability. Could data analytics be one of the solutions?
More data is being created today than ever before. Generally, there are two kinds of data relevant to M&A transactions. The first is data created by companies spontaneously (e.g. social media chatter, CRM data, user behavior, and transaction-related data). The second is external data, such as demographic information, geographic data, etc.
Analyzing both data types provides a more comprehensive overview of the target, and this information can be compared to the analysis conducted by management of the target. According to a 2019 Accenture Strategy report, advanced analytics can add hundreds of millions in value to M&A deals. Data analytics can improve all stages of a transaction, including deal identification and screening, pre-close planning, and post-close integration.
Deal identification and screening
Acquirors may use automated data analytics to identify and screen a short-list of potential M&A transactions by analyzing financial and non-financial data, including social media sentiments or "hidden" patterns within news articles. Such collected data may be analyzed by itself, or incorporated into analytical tools that can have great utility across the stages of an M&A transaction. Predictive tools, for example, can analyze data to quickly highlight worthy M&A deals, and create an investment hypothesis based on the analysis. The findings can then direct the due diligence process into proving the hypothesis, and then serve as a roadmap for value realization post-acquisition. Such analytical tools can reduce time spent from traditional methods by 50%-60%.
Data analytics can also better gauge the value of a deal by broadening the number of factors considered. This includes identifying more sources of value from acquisitions, seeing more synergies and savings holistically, and reducing redundancies. And with advances in machine learning, AI can dynamically adjust those screening parameters based on market conditions and competitor behaviour.
Pre-close execution and planning
Data analytic tools can process a target's raw transaction-level data to provide helpful insights on the business' revenue and margin performance, as well as other insights such as the target's customer base, regional impact, and its product mix's impact on margins.
Data analytics can also assist an acquiror in preparing for integration. For example, talent management and retention post-acquisition is often a challenge. Companies that do not undertake retention efforts may lose up to 70% of their senior managers in the first five years after a merger, something which data analytics can alleviate.
Advanced analytics can assist in talent acquisition by providing better insights on the available market, and which skill sets are missing from the target's current employees. Similarly, acquirors can use data analytics to prepare for talent retention by determining which roles are most critical to value creation, assessing employee performance, and using company and external data to highlight which employees are most at risk of leaving. This information can help design targeted retention plans as opposed to blanket incentives to all employees.
Post-close integration
Companies using analytics can design new combined organizations in up to half the time it traditionally takes as the predictive tools discussed above can provide the acquiror with the foresight to prepare for integration earlier on.
Additionally, advanced analytics can improve the asset effectiveness of the post-transaction company's assets, including machinery, equipment, or even employees/functional groups. During integration, advanced analytics can be used to create a model that assesses the likelihood of equipment failure, allowing companies to develop solutions in advance. In sales, advanced analytics may be used to address coverage overlaps by using data on territories and travel patterns.
Data analytics can also be used towards continuing diligence and data gathering post-close. This data can be included in the company's business intelligence platforms and processes, allowing the company to continuously improve its business outlook.
Conclusion
Data analytics raises numerous considerations including the potential need to employ data scientists, the initial large costs associated with creating bespoke analytic tools, and the need for compliance counsel that can advise on (among other things) the privacy and anti-competition aspects of data gathering/analysis. Given the changing M&A landscape, companies that can effectively embrace innovation to improve both the speed of the acquisition and the accuracy of relevant information may gain an advantage over their competitors.
In a future post, we will explore data analytics in M&A further, including deal types that may especially benefit from incorporating such tools, and novel considerations for buyers and sellers arising from utilizing them in their deals.
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