Data-Driven Research during Mergers and Acquisitions [M&A] Part 2 : Advanced Analytics
M&A is often a core component within a business’s strategy, whether a business is seeking out new distribution channels, expansion into adjacent products, or innovation — purchasing is often cheaper and much more efficient than building.
According to Axios, the value of global M&A rose 64% over the first half of 2017, but the actual number of deals fell by nearly 10%. With so many buyers in the market, managers are looking for faster, more efficient ways to complete diligence during the mergers and acquisitions process.
Part 1 of this series: “Data-Driven Research during Mergers and Acquisitions [M&A] Part 1”
Big data is a critical part of the private equity deal process.
Companies such a EY have even productized the service, calling it “transactional analytics”.
When purchasing a company, asset managers are now relying on advanced analytics to reinforce their decisions. To properly do so, strategist combine the target’s data, the acquiring company’s data, 3rd party data, statistical algorithms, and quantitative analysis in an effort to help mitigate any risk associated with a potential deal.
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Nearly always, the first step of a merger or acquisition is determining a target. Data analytics can allow buyers to visualize a wider playing field, allowing for comparisons, combinations or cutting of duplicate resources to be made to help maximize revenue and minimize costs.
Due diligence time frames have become increasingly shorter and shorter due to the amount of competition within the marketplace.
Data-driven diligence allows managers to take a quicker, yet closer look at the potential risk associated with deal.
In most cases, managers reduce offer prices or walk away from deals due to a lack of information regarding the target or the market. By taking an analytical approach, managers are able to surface key insights from new data sets. In most cases this grants them with enough information to concept an informed hypothesis regarding a potential deal’s outcome.
Data also allows managers to merge companies quicker and more efficiently once deals are completed.
In the same way managers are using data to help inform deal strategies, companies are acquiring targets to access the data they contain.
The goal of data-driven M&A shouldn’t be simply to gain large volumes of data, but rather used to enhance and enrich the company’s own first-party data. When Microsoft closed its purchase of LinkedIn in December it was largely to access the enterprise social network’s unique data sets and gain deeper insight into the professional community. But to make the most of this acquisition and create monetisation opportunities, Microsoft must link the user data from its own products — such as Office 365 — with social and content consumption data from those same users’ LinkedIn profiles. From: Capitalising on Data-Driven Acquisitions in 2017
Data-driven analysis can be an enabler to protecting and growing the target organization’s customer revenue.
Customer retention modeling, cross-sell and up-sell propensities, and lifetime value modeling all provide valuable customer insights that enable the acquirer to generate a greater understanding of an investment thesis.
In the last 10 years, industry leaders have made a commitment to analytics and data-backed decision making in order to predict revenue and improve profitability.
M&A research should be no different.
By applying the same strategy and data analysis, asset managers and/or corporate development teams can position their organization to minimize post-deal risk while ultimately seeking out the highest level of value possible.