Neil Patel

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Data analytics in M&A enable dealmakers to arrive at decisions based on facts and numbers. Relying on insights driven by data can prove invaluable during the decision-making and due diligence cycles. Quantitative intelligence ensures accurate predictive analysis of the potential success or failure of the deal.

Deploying data analytics has become all the more crucial for keeping pace with the competition and ensuring long-term synergies. Buyers and sellers are better equipped to realize maximum value from the transaction.

Using Artificial Intelligence (AI) tools to analyze the data delivers quick and precise results that can speed up M&A aspects. Dealmakers who pass on these tools lose out on the exceptional benefits that can streamline the merger process.

Statistics indicate that just 8% of companies leverage data analytics to conduct activities. However, they could potentially generate $9.5T to $15.4T in value by investing in AI tools. Particularly tools that enable advanced data analytics in M&A.

M&A deals have different objectives and motivations. Diversifying the product portfolio, achieving economies of scale, efficiency in operations, IP acquisitions, and acquihires are only some of them. Instead of basing their decisions on intuition, dealmakers should rely on hard facts and data.

Extensive research, efficient data compiling, and proper data management and storage empower parties on both sides of the negotiation table. Getting insights into the financial health and stability of the targeted company is only one of the many benefits.

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    Why Leveraging Data Analytics in M&A Decision-Making is Advisable

    Leveraging data analytics in M&A has several benefits for dealmakers. Technological advancements have led to organizations generating loads of valuable data on a daily basis. This data comes from various sources, such as:

    • Financial transactions with vendors, suppliers, customers, and third-party service providers.
    • Communication via different digital platforms, including customer interactions on social media sites, comments, feedback, and more
    • Managing customers via different channels such as email, chat, and in-person calls
    • Scanning barcodes and digital files to extract information

    As long as the data is raw and unstructured, it offers little value to the organization. That’s because it is hard to comprehend or use, which is why owners must have expert data analysts on board. Using advanced tools, the professionals convert the data into legible information.

    The company’s top executives can then deploy the data to make strategic and well-informed business decisions. That’s how they can ensure efficiency in the company’s operations and enhance its bottom line and performance. Several AI and software tools that can streamline the process are available.

    Data Analytics Through the Merger Process

    Data analytics is particularly helpful when dealmakers must evaluate options for strategic alliances. Typically, high costs are involved during due diligence and assessing the financial health of the target. Purchasing firms also involves significant spending, especially when buyers must raise funding for the deal.

    As a result, acquirers are keen on extracting full value from the deal. They must also deliver on the commitments they make to the stakeholders. And data analytics in M&A can get them there. Data can prove invaluable at every stage of the M&A process.

    Starting from evaluating the target company as a good candidate for acquisition, due diligence, and through the closing. Executing the post-merger integration is also easily done using workforce and cultural data, not to mention IP-related and financial metrics.

    Thanks to the low cost of data compiling and storage and efficient tools for processing, dealmakers can use the information. Scrutinizing both internal and external data and discussing it on the table is crucial before making decisions.

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    Data Analyses Answers Crucial M&A Questions

    Efficient data analysis speeds up the due diligence and evaluation, shortening the time for the deal to close. It is also helpful for meeting deadlines with processing documentation and integration.

    Dealmakers use data analytics in M & A to answer key questions that can streamline the deal. For instance:

    • What are the core drivers propelling the company’s growth?
    • What are the success rates for retaining customers?
    • Which are the products, customer demographics, or locations that influence negative trends?
    • What are the potential risks of the deal?
    • Which are the employees, third-party entities, customers, and vendors that should be covered by non-compete agreements?
    • How will changes in management and ownership affect the legacy company after the deal closes?
    • How can revenues and profit margins be improved? What are the new terms and conditions that can be offered to customers and vendors to
    • make that happen?
    • Will the surviving company’s risk profile change after the merger?
    • How will the decisions in the purchase agreement impact the company’s net performance?

    Keep in mind that in acquisitions, mergers, or fundraising, storytelling is everything. In this regard, for a winning pitch deck to help you here, take a look at the template created by Silicon Valley legend Peter Thiel (see it here) that I recently covered. Thiel was the first angel investor in Facebook with a $500K check that turned into more than $1 billion in cash.

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    Types of Data Analytics in the M&A Process

    Mergers and acquisitions need four different types of data analytics and tools, such as:

    Descriptive Data Analysis

    Descriptive analysis typically targets historical data compiled from company dashboards. Adding up and analyzing past data indicates metrics like KPIs or Key Performance Indicators, new customers acquired, and total revenues.

    Monthly or quarterly revenue reports, the number of leads generated per marketing strategy, and average revenue per sale are other metrics. The company’s stock performance within a given time frame is also a variable that dealmakers want to examine during evaluation.

    Descriptive data analysis helps understand past trends, patterns, and relationships. The data traces the outcomes of a particular event or occurrence, and studying this information helps executives make future decisions.

    Diagnostic Data Analysis

    Diagnostic analysis builds on the compiled descriptive data and studies it to understand the causes that trigger the event. This evaluation is about the factors that resulted in the descriptive data. It works to build valid connections between the data and business patterns and trends.

    When conducting diagnostic data analytics in M&A, experts seek to answer the question: Why did this event happen? To go with the earlier examples, analysts identify the effective advertising strategy that generated a spike in sales.

    Or, that generated the maximum number of leads or inquiries. If the outcome is monthly revenues, the data mining will focus on the particular product design with maximum demand. If the website traffic is lower than in previous months, the analysis will focus on the changes in web design.

    Or, any other tweaks the web designers may have made that resulted in fewer visitors landing on the pages. The core techniques used in diagnostic data analysis include data mining, data discovery, drill down, and understanding correlations.

    Predictive Data Analysis

    As the name suggests, predictive data analysis is crucial to predict possible outcomes. Using detailed statistical modeling, analysts are able to make precise and logical deductions about how future events are likely to play out.

    Descriptive and diagnostic data analysis is effective for understanding the target’s performance and the sequence of events leading up to that performance. While this data helps evaluate the target’s viability as a candidate for acquisition, predictive analysis estimates its future potential.

    Analysts compile results to predict future sales, estimated revenues, team productivity metrics, and stock prices. At the same time, they also account for external factors that can influence the company’s success.

    For instance, market trends, macroeconomic factors, and other industry-specific conditions. Arriving at the results allows acquirers to predict the target’s profit and synergy potential. Accordingly, they can choose to move forward with the deal or terminate the acquisition.

    Prescriptive Data Analytics

    Prescriptive analytics leverage advanced and highly sophisticated mathematical algorithms and AI technology. This analysis is the culmination of the three earlier analytics: descriptive, diagnostic, and predictive.

    Companies and dealmakers may have to invest significant resources toward this analysis but can expect valuable outcomes. The results of this analysis help them work out the best course of action to resolve the risks and issues the target may be facing.

    Predictive analysis equips acquirers to make informed decisions about the risks they’ll face if they move ahead with the deal. They will have a better handle on how to resolve the target’s issues and the possible hurdles to achieving synergy.

    Using Data Analytics in M&A for Evaluating Targets

    Identifying the Right Targets for Acquisition or Merger

    Using the different types of data analysis, dealmakers can identify the risks, benefits, and synergies the seller offers. Analyzing the information allows them to assess the deal’s viability. The statistical models provide a clear overview of the corporate entity that will come into existence post-merger.

    The business model and the direction of the surviving company and its potential for success or failure are other results. Most importantly, the analysis could help predict how the markets and customers are likely to view the merger. And if they will continue to support and purchase the brand.

    Conducting data analysis in M&A in the early stages not only shortens the time frame for closing the deal. But also ensures that dealmakers save on the billions of dollars they might spend on the due diligence. And that includes verifying the accuracy of the information the seller provides.

    It is not unusual for acquirers to incur huge losses after they walk away from unsuccessful mergers. Or move forward with unsatisfactory mergers if only to offset the costs they have already invested in the evaluation.

    AI-driven data analytics can eliminate this risk since it can review and assess thousands of business documents in shorter times. Not to mention achieve precision and accuracy without the possibility of human error.

    Data Analytics Ensures Quality Information is Used

    High-quality data is crucial when conducting audits on the target company, including legal, financial, operational, and environmental compliance. Acquirers also need to evaluate the company’s assets and liabilities, including contracts, customer base, and intellectual property assets.

    Cutting-edge data analysis tools have the capability to mine new datasets from external and internal sources for more accurate insights. Not only does the new data provide additional information not available from internal sources, but also identifies misinformation and errors.

    Such errors can prove to be costly in terms of money and time. Dealmakers can also use the data in the transaction document for behavioral analysis to understand stakeholder psychology. These insights can prove invaluable when designing their negotiation moves and strategies.

    Uncovering previously overlooked opportunities for synergy to derive maximum value from the deal is another positive of using top-notch data. Navigating the due diligence process quickly enables buyers to close the deal quickly and move on to the integration.

    Data Analytics for Post-Merger Integration

    Data analytics in M&A are excellent tools for achieving synergies and integration. Compiling and studying cultural data can help eliminate cultural and personality clashes that typically occur after mergers and acquisitions.

    Using data to identify potential challenges HR is likely to face can help them prep to deal with them. Managers can organize better training and orientation programs for the employees. Company owners can zero in on the essential leadership courses for managers to fill the gaps in supervisory skill sets.

    Employee productivity metrics can direct dealmakers on the core talent they need to manage more effectively. They can also use the statistics to identify the skills they need to hire for long-term success and scalability.

    The Takeaway!

    In a business landscape dominated by cutthroat competition, dealmakers need to move fast when they need collaboration. To achieve their objectives, they must find the right targets for M&A deals and conclude the due diligence quickly.

    For this reason, the average time frame for an M&A transaction has reduced from several months to a year. Deals now close quickly, within three to six months, and dealmakers are recognizing the pros of data analytics in M&A.

    Not only can they compile data, but they can also process the raw data quickly using software tools and AI. Hiring the services of expert data analyst teams is becoming a common practice. Leveraging data analytics can help avoid errors that can cost millions and sometimes billions of dollars.

    Relying on the experts to determine accurate pricing, secure sensitive data, and conform with antitrust laws is always advisable. Data analysis has effectively transformed the corporate ecosystem with technological advancements that streamline how M&A deals occur.

    You may find our free library of business templates interesting as well. There, you will find every single template you will need when building and scaling your business completely for free. See it here.

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    Neil Patel

    I hope you enjoy reading this blog post.

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