With ever-increasing troves of underutilized data and information, a result of the pervasive use of technology that permeates our personal and corporate digital lives, the time is now to integrate data analytics and artificial intelligence (AI) into your mergers and acquisitions (M&A) deals.
By combining sophisticated algorithms and machine learning, these technologies can efficiently and quickly analyze vast quantities of data, extracting deeper insights that have immense impacts on M&A, divestitures and other transactions. The result: better decisionmaking, faster. Deal professionals should take note of this potential value and familiarize themselves with this burgeoning technology.
In a recent real-time survey of executive dealmakers from some of the world’s top companies, over 76 percent of attendees said AI is the technology most likely to disrupt deal strategy in the near term.1 And while many companies have yet to take full advantage of such technologies, it is clear that data analytics and AI will play an increasingly important role in deal strategy and execution moving forward.2 AI’s value is not limitless, however, and should not be a substitute for the expertise of legal counsel. But it can make possible more effective use of valuable legal resources. As such, it is becoming a critical tool for dealmakers that enables greater confidence in forecasts and synergy estimates ahead of a deal decision.
Discerning Advantageous from Superfluous Data
Most deals require the review of enormous amounts of data, and the expanding amount of information available has both benefits and drawbacks. According to Deloitte, the amount of data companies produced doubled between 2015 and 2017.3 It is on track to double again by the end of 2019, and worldwide annual data creation is expected to grow by ten times the amount of data produced in 2017 by 2025.4 With greater data volumes, an acquirer can now know more about the target than ever before.
However, the entire M&A process can also be complicated by the amount of information necessary to review during a deal. According to Gartner, Inc., a global research and advisory firm, more than 80 percent of company data today is unstructured, made up of nonfinancial data such as contracts, social media comments and emails.5 This makes it difficult to review and extract the value from this information, so it can easily go unused. With AI-powered analytics, however, it is possible to review and analyze this otherwise-underutilized data to improve decision-making. Its utilization can provide unmatched visibility and transparency throughout the entire process.
Using Data Analytics and AI During the Deal Life Cycle
Sourcing
In the beginning of the M&A process, data analytics and AI can help companies predict and identify M&A targets.6 Today, firms are applying statistical algorithms to analyze companies and target third-party data, speed up deal execution, and inform M&A decision-making. By using an algorithmic or holistic approach to deal sourcing, potential acquirers can distill a large list of active and relevant targets into specific subcategories or segments. This has the potential to result in higher returns on investment, because each segment can be targeted using a different strategy. When this process is implemented, it must be reviewed, evaluated and improved to ultimately become replicable. But this frontend effort can yield long-term gains because the analysis helps dealmakers decide what to purchase and how much to pay by scrutinizing companies’ strengths and weaknesses to spot potential investments, taking into account past performance and predictive expectations of the future. And while this technology is unlikely to dissolve the necessary human connections when companies are finding deals, it should at least improve, for example, how private-equity firms leverage their professional networks when sourcing and developing investment ideas.
By using an algorithmic or holistic approach to deal sourcing, potential acquirers can distill a large list of active and relevant targets into specific subcategories or segments.
Moreover, data analytics and AI can be used to extract value from information currently underutilized. For example, they can be used to properly vet and review board members, C-suite executives and employees of targeted companies by sorting, analyzing and summarizing their social media posts in ways that are not otherwise feasible or efficient for traditional deal teams conducting due diligence. Social media platforms such as Facebook, Instagram and Twitter produce a relatively new and abundant source of data that is increasingly essential to the deal as part of proper diligence and vetting. Inadequate review can result in onboarding problems that do not present themselves until it is too late, and that may negatively impact a deal’s expected value.
Likewise, the use of data analytics and AI enables comprehensive and comparative use of online company reviews on sites such as LinkedIn, Yelp, Glassdoor, Indeed and Vault that may be useful for sourcing or due diligence. By collecting and sorting qualitatively subjective digital records, such as online posts and reviews, acquirers can better understand a company’s reputation with its customers and identify issues with key systems of the target’s processes that, if improved, can deliver added value postacquisition. Such analysis is made possible by sentiment trackers such as Astute Hootsuite Insights, RapidMiner and Quick Search, which aggregate large volumes of data to concentrate acquirers’ attention on important areas such as a target company’s competitive market position or whether its online systems work well for customers.
Due Diligence
The due diligence process is particularly suitable for the use of data analytics and AI because it can rapidly review vast amounts of information to uncover potential red flags during reputational and commercial due diligence. In lieu of large teams, data analytics and AI platforms such as eBrevia, Kira and Luminance can search an unlimited number of uploaded contracts to help review large amounts of text and data points to present important issues to legal advisors and due diligence teams. This can streamline human efforts without sacrificing accuracy. And with robust analytics, firms can dive deep into a company’s data to gain insights and identify potential problem areas before it is too late.
A ready example is compliance and risk management in which data analytics and AI technology allow targets to be more thoroughly vetted by analyzing information that is traditionally not reviewed, which can minimize the risk of taking on compliancerelated liabilities in a merger or acquisition. For example, Neota Logic, a “digital intelligence” business that provides enhanced due diligence and human resources screening, uses cyber intelligence skills, machine learning technology and human analytics to run background checks on management teams by searching the internet, including public records and social media, as well as the deep web and dark web, for any potential risk or compliance issues. This is crucial given the characteristics and range of compliance liabilities both domestically and abroad.
Negotiation and Contracting
In the negotiation and contracting phase, data analytics and AI can likewise provide valuable insights. Because AI systems can extract the wealth of data cached in the lines of negotiated contracts, including dates, timelines, prices and returns, it can relieve the burden on legal resources that would otherwise need to dissect the clauses.
This is particularly valuable when contracts span a wide variety of subjects, as it allows negotiators and deal teams to quickly parse information across diverse business areas. Likewise, data analytics and AI can review publicly filed deal data, such as U.S. Securities and Exchange Commission EDGAR filings, to assemble crucial data point comparisons to give contract negotiators an upper hand, especially when third-party research does not provide such market data or such research is otherwise not readily available.
Data analytics and AI are even aiding contract drafting by saving time and reducing errors with systems such as Contract Express, Neota Logic and High Q. Such data analytics and AI may even be able to identify points of inefficiencies or value loss so that contracts can be improved upon in the future. For example, if a company is acquiring a retailer with numerous brick-and-mortar storefronts, data analytics and AI could be utilized to analyze the voluminous and differing rental contracts and identify average price per square foot, efficiency of space and sales per zip code, providing insights for future deals, recommendations and negotiation goals.
Post-Merger Integration
Post-acquisition, buyers are turning to analytics to help identify potential synergy opportunities, risks and hurdles to prepare for integration and execution.7 Data-driven solutions allow for the optimization of business activities and identify other potential valuecreation opportunities between the merged companies. At Troutman Sanders eMerge, a legal technology and eDiscovery subsidiary of Troutman Sanders LLP, the complex data analytics of Relativity© were recently coupled with experience and industry knowledge to provide innovative “reverse discovery” during the post-integration process. It reviewed in-house contracts and other data to identify competitive information required to be destroyed in order for the company to meet post-closing noncompete requirements and comply with certain laws and industry regulations. Likewise, data analytics and AI can be used to review and then compare acquirer and acquired institutions’ contracts to identify which entity has the best outside vendor terms and to leverage such terms enterprise-wide post-merger.
AI-driven automation can also enable the integrated company to rely less on undocumented processes and institutional knowledge that may be lost in the staffing disruptions that can occur after a deal. Data analytics and AI can play a particularly important role for repeatable tasks by consistently executing those tasks following updated policies and procedures. As such, data analytics and AI are useful to better capture expected synergies or other efficiencies post-merger to deliver forecasted value creation.
Conclusion
Moving forward, M&A-focused data analytics and AI use will continue to broaden across industries and the deal process over time, as this use effectively generates and presents opportunities to leverage deal insights. Moreover, the complementary use of data analytics and AI with legal counsel and deal teams can better manage workflows and make the most efficient use of resources. Deal makers should continue to monitor these technologies and should anticipate their quick adoption, as they will continually change the deal process.
