In the midst of recent turbulent times, dealmakers have navigated through significant fluctuations, experiencing record deal flow despite prevailing uncertainties. However, as we venture into 2023, the convergence of factors like inflation, escalating interest rates, and geopolitical instabilities has prompted a cautious approach towards M&A.

Data functions as the binding agent connecting individuals, processes, and technology, with its significance further magnified in the domain of mergers and acquisitions. In an era characterized by remarkable technological advancement, the landscape of M&A is swiftly evolving. Deal makers are increasingly leveraging deal origination tools, digital data repositories, corporate development search solutions, extensive information repositories, and advanced analytics. These resources collectively expedite the pace and efficiency of transactions.

According to KPMG’s 2018 report, ‘Data & Analytics in M&A: A New Arsenal for Modern Deal Makers,’ data analytics are now harnessed across various stages of the M&A journey. These stages encompass initial opportunity identification and evaluation, meticulous due diligence during the deal-making process, and continuous value optimization after the deal’s closure.

The paradigm of deal sourcing in the private market is rapidly shifting from a primarily relationship-driven approach to a more data-driven strategy. Numerous organizations are deliberating on how to employ technology to streamline processes and automate routine tasks, thereby liberating transaction teams to focus on higher-value endeavors. How can a robust data strategy empower organizations to secure a competitive edge by transitioning towards data-powered deal sourcing?

What is a Data-Driven Deal Sourcing Strategy?

As the name implies, data-driven deal sourcing uses data to identify suitable investment prospects before reaching out to or interacting with them. It is frequently used in outbound efforts and is most effective when combined with relationship-driven and in-person deal sourcing tactics.

Transitioning from a Traditional Deal Sourcing to a Data-Driven Deal Sourcing

Throughout history, the influence of networks has been harnessed to generate a flow of private capital deals, with investment teams relying on the strength of their connections with advisors and intermediaries to introduce high-caliber deals into their pipeline. Customer relationship management (CRM) systems have contributed to better nurturing these relationships. However, investment professionals who exclusively depend on intermediary relationships to initiate top-notch deals are witnessing a decline in their competitive standing.The conventional deal team has traditionally allocated their time across various critical responsibilities.

  1. Scrutinize potential target companies and evaluate data to screen and determine which deals merit progression in the pipeline. 
  2. Engage in due diligence efforts and advance live deals, all the while providing support for portfolio operations and value enhancement. 
  3. Maintain contact with industry experts, management talent, and peer groups.

Achieving success through a data-driven strategy demands a fundamental shift in perspective. Firms now face more pressure than ever to establish robust business systems and processes. 

How to Unlock the Power of Data in Deal Sourcing

Assuming you have a CRM in place, the next stage a company should consider is expanding its ability to collect, prepare, and access information throughout the entire deal pipeline. Companies may need to implement appropriate data management and storage facilities, as well as layering in the technology required to enable deal times to analyze these growing data sets.

  1. Using deal sourcing tools

Data sets are readily available to support firms wishing to move to a data-driven deal sourcing approach. An AI deal sourcing platform like Cyndx Finder provides a means of identifying and analyzing companies of interest. There are also data sets within the public domain which can be useful to support analysis of potential deals. These public sources include social media reports, website traffic stats, online reviews and feedback, and management team employment histories.

Analysis tools can be developed in-house or procured from the markets so there are decisions to be made in relation to buy, versus a build option. Proprietary information feeding the system is the key point, not the technology itself. The algorithm will be a ‘black box’ which uses both internal and external data. The output should be a higher volume of proprietary deals and better-informed decision making on prioritization of targets.

  1. Spend your time wisely on value-add activities

To stay ahead, firms must ensure that the deal team is focused on value-added activities. Elements of the deal process that can be automated or outsourced to third parties, such as identifying ‘sweet spot’ companies, initial screening of companies based on size, industry sector, financials, region, transaction data, and so on, should be done.

Firms that rely on manual analysis will see a drop in deal rate compared to their data-enabled peers, who can handle a larger amount of data pertaining to prospective deals. By eliminating manual analysis, investment teams can devote their time to analyzing pre-screened targets. Due diligence can also be outsourced to third-party market experts, who will report back to investment teams with summary intelligence to analyze.

When it comes to dead deal data and benchmarking, firms should keep track of lost and declined deals, as well as past and ongoing investments. Declined deal data is proprietary and must be used as part of a comprehensive data-driven approach to deal making. This data is critical for allowing machine learning algorithms to learn from pipeline decisions rather than just successful deals. Benchmarking successful deals identified by a system versus those identified by traditional methods will enable firms to better understand which insights are valuable and which are not, and thus incorporate this valuable feedback into the algorithms. Only then will the algorithm be able to provide you with a true and clear picture of future deal opportunities.

  1. Build incrementally from solid foundations

Future success is dependent on embracing the shift from relying solely on networks to leveraging the power of data alongside existing relationships. Firms that want to transition to a data-driven approach must first lay the groundwork in order to gain valuable insights from data and fully leverage it.

Achieving a data-driven approach to deal-making is a step-by-step process, and a long-term strategy. Constant feedback is required to maintain the integrity of these systems and the data output. Investment analysts must assess and provide accurate feedback on the insights they provide in order to improve their effectiveness over time.

Derive valuable data using an AI-enabled deal sourcing platform

Throughout your data-driven strategy, the goal should be to derive valuable insights from data in order to fully leverage the deal pipeline, thereby increasing the quality and quantity of deals and achieving significantly higher returns. 

Cyndx’ Finder’s AI-enriched and dynamic NLP-based platform that can help achieve your data-driven deal sourcing strategy. With a pool of over 25.5 million companies in our platform, 10M of them mapped, you’ll find your competitive edge in identifying niche and hard-to-find companies.

See how the market is changing and capture deal opportunities using Cyndx Finder.  

Request a demo today.