Dealmakers today have more tools than ever at their disposal to make their deal flow faster and more efficient. Advanced negotiation tactics, innovative technology, virtual conferences — the list goes on.
But for many dealmakers, the most reliable and powerful tool in their toolbelt is underutilized: data. Learning to use data to their advantage is how modern teams and firms will eclipse their competition and succeed in today's highly competitive environment.
To help dealmakers understand the benefits of M&A analytics, the types of data often used, and how to get started with M&A analysis, we've collected our best tips into this guide. Let's dive in!
Before we detail common types of merger and acquisition data and the steps to using M&A analytics, it's important to understand why data is such a powerful tool. For most organizations, the power of becoming a data-driven operation is three-fold:
Productivity is measured as the relationship between the number of tasks completed and the time it takes to complete those tasks. Data is the key to unlocking greater productivity by objectively highlighting where inefficiencies exist. For example, if one of your dealmakers follows a particular process and completes 10% more deals on average, your firm should consider amending all dealmakers' processes to follow this faster method.
M&A analytics can also help your firm make post-transaction improvements. With the right models, your firm can gain insight into where integration processes may be stalling or how your firm can help improve portfolio companies’ add-on initiatives, thereby increasing operational efficiency (and usually, revenue).
A proprietary advantage in dealmaking refers to a particular metric, model, or methodology that helps pinpoint the right opportunities for your organization quickly and accurately. It’s something totally unique to your firm that no one else has — think of it as a “secret sauce.”
Properly used, merger and acquisition data can give way to proprietary insights like the ability to estimate revenue for private companies and determine whether they are in the ideal growth stage for an organization to invest. Proprietary insights can also benefit portfolio companies by helping them refine add-on criteria or generate predictable ROI.
It is incredibly unlikely that your firm will be the only one chasing an opportunity. The number of private equity firms alone nearly tripled from 2010 to 2022, and dry powder continues to hit new record amounts each year, exerting massive amounts of pressure to transact.
With improved operational efficiency and proprietary insights, your firm can increase the gap between you and your competitors thanks to M&A analytics. Uncover investment opportunities before anyone else, develop deep and meaningful domain expertise, recognize trends before they become mainstream, and so much more.
M&A analytics requires having volumes of fresh, quality data at your fingertips. While different organizations leverage different types of data, for the purposes of this blog post, we’re bucketing it into four broad categories: company, market, employee, and deal flow data.
Company information can mean anything related to a particular business, including founding year, geographical location, revenue (if available), recent announcements, product offerings or intellectual property, patents, etc. For dealmakers, it can also include important contacts and existing connections, events attended, meetings conducted, and more.
The amount of publicly available company information will greatly vary depending on the type of businesses your organization invests in. Public companies, for example, are required to publish large amounts of information about themselves. Meanwhile, private companies have no such obligation, so finding information on private companies is much more difficult.
Pro Tip: Tools such as deal sourcing platforms can provide valuable insights by using data signals such as employee growth over time, recent hires or announcements, etc., to help dealmakers more easily and accurately estimate revenue, growth potential, and deal readiness.
Understanding the wider market is a critical part of first crafting a reliable investment thesis or theme, and then estimating a company's (and deal's) ultimate value. It’s also key for developing the level of domain expertise that top targets search for in an investor.
Market information includes leaders and other competitors in the industry, current and potential market share of a company, industry averages (such as organization size, revenue, length of time in business, and even valuation multiples), and much more. Organizations typically use this data to build market maps that help them visualize all the players and their relative positions in a given space.
From headcounts and tenure to turnover and satisfaction, employee information is a key part of accurately judging a company's health. Additionally, private companies — especially startups —often give equity to their employees as a method of attracting and retaining talent, so financial information regarding employee compensation is especially important when performing M&A analysis.
While individual employee information is not something firms will generally collect past what directly affects the business' financials, information on notable hires, including both past and present board members and executives, are critical data points that assist with deal analytics.
Unlike our other three buckets, data in this category is internal facing. It focuses on the dealmaking processes and outcomes within a firm or organization, and ranges from how many cold calls each business development representative makes on a daily basis to how many deals actually close each quarter.
Deal flow data provides the M&A insights teams need to optimize their processes and achieve operational excellence. Perhaps the most critical tool for capturing and analyzing this type of information is a customer relationship management platform, or CRM, such as Salesforce or Dynamo (more on this later).
Key performance indicators, or KPIs, are determined by analyzing an organization’s current performance and setting milestones for the future. Since every firm has its own investment theses, portfolios, partners, structure, and more, specific KPIs will vary — the same goes for the various opportunities these firms analyze.
That said, there are many common KPIs and metrics that your organization can and should use to guide your M&A analysis and measure and improve results throughout your M&A pipeline.
The first step in the deal flow process is determining what good opportunities look like for your firm and then sourcing them. Each firm has its own tactics, and figuring out what are the most valuable and efficient sourcing methods means analyzing your dealmakers' activities. From here, your firm can understand how much time and money is spent sourcing deals to refine your organization's processes and continuously improve.
For M&A analytics, however, firms must also collect data and use metrics related to the investment opportunities themselves, not just their employees and productivity. Accurately judging whether an opportunity is worthwhile requires careful consideration based on key data points to ensure it fits your organization's investment thesis.
Example Metrics
Example KPIs
Most of the important metrics in this phase will be used to identify the most effective tactics to increase the number of deals that successfully advance through the M&A pipeline — and how quickly they progress. This is something that should be analyzed at every stage.
These baseline metrics can then be used to test new methods for making contact, further refine your investment thesis, or otherwise improve your processes to reduce costs and increase revenue.
Example Metrics
Example KPIs
Perhaps the most difficult stage of the M&A pipeline, due diligence is often a long, arduous process. For many dealmakers, due diligence often follows the same steps, but timelines and costs can vary wildly based on the target investment company's level of organization, data quality, and communication. As such, metrics and KPIs for this phase must be set based on each organization’s and deal's particular circumstances.
Example Metrics
Example KPIs
After due diligence is completed, there's often a lag period until the transaction actually closes and money exchanges hands. Tracking how long this takes helps to more accurately forecast the team's activity, as well as build more reliable deal timelines. This helps not only balance workloads for the firm's employees but also helps set expectations with third parties (including the target investment, Limited Partners (LPs), etc.).
Example Metrics
Example KPIs
After the transaction, an organization's work is far from over. Realizing a return on the investment often takes far more work than securing the investment in the first place, and metrics and KPIs can help ensure everything stays on track and circumvent any future integration problems. While the examples below are particular to M&A analytics, your organization may also wish to track additional data points to help with the operation of your new acquisition.
Example Metrics
Example KPIs
You now understand the various types of data that matter during the M&A process, as well as the kinds of metrics and KPIs that are useful at each deal flow stage. But turning data into valuable and actionable insights isn’t exactly a straightforward process, so we’re breaking it down into five steps that your organization can follow to become more data-driven.
Your data journey must start at the source — your data sources, that is. If your organization was relying on manual research before, bringing on data sources will bring about a new era of research for your dealmakers. Good data sources, such as a deal sourcing platform, will often use thousands or hundreds of thousands of its own sources to find all available company data.
Of course, not all data sources are created equal. The importance of good data quality cannot be overstated. Imagine you went to a partner meeting with inaccurate deal analytics or portfolio valuations. At the very least, you'd be embarrassed in the meeting when someone else pointed out your mistake. But at worst, if those inaccurate figures were used to make business decisions, the results could be disastrous.
Each data source you find will have a wealth of data. Most will often tout having millions of data points available. However, the unfortunate truth is that not all data is useful. Some merger and acquisition data is interesting. For instance, knowing how much revenue your portfolio generated last year is an interesting data point. It helps you understand the health of the portfolio at large and whether you've hit your goals. But alone, that data point doesn't help you know what your next steps are.
Instead, if you knew that your portfolio revenue had hit your goals but was trending downward over the past year, especially in the energy sector, you now know of a problem that needs to be fixed. Data is only actionable and insightful when it is in the right context. But every firm will have its own signifiers of business health.
For your firm to be successful, you must sit down and map out what insights are most important to you. Financials will always be important, of course, but if your portfolio companies rely on labor, employee satisfaction and headcounts should also be of great importance and tracked carefully.
Another tenet of data is that it's only useful when shared. Siloed data is a rampant problem in both companies and firms alike, so care must be taken to feed data into a centralized spot so that it can then be shared with and used by the entire firm.
For organizations at the beginning of their digital transformation journey, this spot will likely be a customer relationship management system (CRM). However, more technologically advanced organizations use data warehouses. Data warehouses house and connect massive amounts of data from across all the software in your tech stack, from enterprise resource planning systems (ERPs) to marketing automation platforms (MAPs) to CRMs.
Regardless of your firm's chosen method for storing and sharing data, one of your key concerns should be promoting data hygiene. If you do not enforce good data hygiene practices, all the work you did finding good data sources and determining their value will be wasted, hampering your M&A data analytics efforts.
Once you have your sources and are transporting data to the right places, it's time to put it to work. Data needs to be used to be helpful, and this usually comes in the form of reports and dashboards.
Depending on your tech stack and what systems are integrated, your first foray into report-building may be to use standard, out-of-the-box reports and dashboards. Most systems will also offer the capability to build bespoke reports using their internal builders, which can help your firm generate more helpful insights.
If you want to be serious about data — and let's be honest, data should be part of your foundational processes moving forward — you need to invest in an analytics platform (e.g., PowerBI, Looker, and Domo) and the people who can make the most of it: data analysts and scientists.
Data has the potential to be the most powerful tool in your firm's toolkit if you can uncover the valuable insights hidden within all that’s available to you. The platforms and employees that specialize in handling data appropriately are how your firm finds those insights and builds a proprietary advantage.
The bottom line is that proper data utilization and M&A analytics can launch your firm to levels beyond what you previously thought possible.
The first step to making it happen is finding the best data source: a deal sourcing platform. To help your firm evaluate and separate the best from the rest, download our buyer's guide to deal sourcing platforms today.
