A tremendous amount of data flows through the lifecycle of a typical investment transaction.

As you work your way through a deal, you may ask yourself questions such as:

• What are the characteristics of the target market for the company's products?
• What is the company's competitive position?
• How should we model growth or attrition for this company?
• What are the learnings from this investment process with regards to growth, costs, management, etc.?
• How is the company performing post-acquisition? How does this compare to our other companies?

The vast majority of investors do a great job digging into all of these, and many other, questions when considering a particular investment. They build detailed Excel models, lengthy PowerPoint presentations, and perhaps summarize their post-acquisition learnings in written reports or documents.

But then this work tends to disappear.

This brief showcases how leveraging data warehouses and Generative AI to build a centralized intelligence repository can streamline your M&A operations and maximize returns by saving countless hours and dollars, reducing the probability of repeat errors, increasing confidence in your decisions, and unlocking unlimited learnings.

A framework for private equity data

Let's put together a quick framework of how information is structured, where it lives, and how it flows through the lifecycle of a typical investment target.

Discovery (Pre-Deal):

For many investors, the majority of this data lives in some sort of CRM in a structured format. Where you perform analysis on this data depends on your firm’s data strategy.

• Investment Targets
  • Which companies fit our investment mandate? What characteristics do these companies possess?
• Investment Brokers
  • Who are some of the brokers that can help us get into an investment process?
• Corporate Development and Investment Process Data​​​​
  • ​Which types of sourcing activities are better suited for specific outcomes?
  • Which employee/team is driving meaningful conversations with investment targets?
  • How many activities does it take before we start an investment process?
  • What is our deal velocity? How well do our deals convert and how long do we spend at each stage of the process? How does this differ by country or industry?
  • How does our offer price differ by team, industry, or geography?
  • Which brokers are inviting us to successful processes? What are we paying buy-side brokers? Do we have different results between brokered and non-brokered processes?
  • Where are our most successful deals? Where and why are we losing deals?

Diligence (Deal):

The vast majority of this data tends to live in Excel (financial models) and PowerPoint presentations (investment memos) in an unstructured format.

• Market Research
  • H​ow many potential customers exist for this company's product? How/where is the market growing?
• Competitive Intelligence
  • How many similar companies exist in a particular market?​ How does the company's product's functionality differ from competitors?
• Company Intelligence​
  • What is the historical growth rate of this company?
  • What is the customer churn rate of this company?​
  • What are the historical price increases?

For metrics such as revenue growth rate or operating margins you’d likely start with historical target company data and management forecasts. You’d then look for relevant processes completed by your firm. This leads to a tedious search through Excel models (hopefully it’s the final version), investment memos, or board presentations.

For industry growth rates you might rely on third-party research, your CRM, or open your favorite data product to see how many new companies are founded per year in this industry and how many people are working at these companies every year. You’d then realize you have no choice but to trust the product’s industry classification as you likely have limited exports so you can’t dig into the data on a company level to build a proper view of the market.

Wouldn’t it be great to have all this data in one place as a reference for similar deals in the future? Or a proper market map?

‍Learn how our Generative AI solutions can help.

Integration and Operation (Post-Deal):

As you scale your M&A operations, you’ll notice the same issues or benefits pop up from deal to deal.

We tend to split these learnings into qualitative learnings and quantitative learnings:

• Qualitative Learnings include management practices/behavior, political or cultural considerations, etc.
  • How do environmental factors or the way this company operates drive particular outcomes?​ For instance:
  • A certain country may have stricter employment laws which may hinder your plan to make organizational changes in your new portfolio company.
  • A certain industry may have such strong competition that the growth rate you’ve modeled may not be feasible.
  • A certain broker may invite you to irrelevant processes just to pad the number of investors at an auction.
• Quantitative Learnings include basic operating results but also offshore R&D costs, salary costs, advertising channel costs, customer acquisition costs, growth by geography, etc.
  • How is this company performing post-acquisition given our chosen KPIs in a particular department? Some (fabricated) examples include:
  • Marketing costs may need to be less than 20% of sales and Google Ads provide the best return on marketing investment.​
  • The average salary for a software development manager in the US is $100K.
  • Healthcare software companies tend to grow faster than construction software companies due to a larger average contract size and the ability to charge more professional services for custom features.

Leveraging this data can be a bit more complicated since:

• Basic operating data tends to live in a structured format in the accounting systems of individual portfolio companies who may send a periodic update to the holding company. Accessing the data for analysis may be tedious even if the holding company has centralized accounting.
• Other quantitative data tends to exist in the portfolio company department that touches the raw data source (i.e. the HR department controls payroll/salary data and the marketing department owns the advertising cost data). Raw data exists in a structured format, but KPIs and other metrics may only exist in an unstructured format.
• Data around qualitative factors tends to live in an unstructured format in PowerPoint presentations or executive briefs around post-acquisition performance.

Many companies do a great job of building their playbook and noting best practices (somewhere).

Few are able serve these learnings at the right time to the right people to minimize the number of times that a certain issue requires time (and likely money) to resolve.

How to leverage porfolio company operations data

Regardless of the structure of your firm, portfolio company operations data should be consolidated in a data warehouse to provide low-cost actionable intelligence to support all stages of your operations.

• Discovery and Diligence:
  • Your existing investments are a great source for reliable data on metrics such as growth or profit margins. Build benchmarks for all your key metrics and ensure that your M&A team can easily pull this data while building financial models.
• Integration and Operation:
  • Provide actionable intelligence to operators to help drive performance across every department of your portfolio companies. For instance:
    • Human Resources – What is the organizational structure of the company? How much are we paying employees in particular countries? What is the cost to hire or provide benefits to an employee? What is our average turnover?
    • Marketing – How much money do our companies spend on advertising platforms? Which platform is returning the highest number of qualified leads? What is the return on marketing spend? How is our traffic and SEO doing?
    • Research and Development – What is the deployment frequency? Which types of projects of tasks are being completed? How long does it take to complete a re-write or develop a new feature/product?
    • Customer Support – How long does it take for us to respond to customers? How much time is spent on various support tasks?

Immense value is created across your entire portfolio when all this data is centralized and served to the relevant stakeholder at the appropriate time.

Implementing this infrastructure will not only identify and create advancement opportunities for top performers but also increase collaboration through the sharing of best practices, stimulate innovation through healthy competition, and ultimately boost confidence and performance through enhanced data-driven decision making.

Every project comes with challenges

Despite the tremendous value from such an implementation, there are nuisances that can complicate or even derail the project.

• Data structure and storage:
  • Data is often stored on many different platforms in different structures and formats. An interesting parallel can be drawn between private equity firms and hospitals who, on average, use 15 different tools just to handle electronic medical records for their patients.
  • Just as a hospital may have to use 15 different EMR tools, a private equity firm with 15 portfolio companies might have:
    • 5 different accounting platforms
    • 12 different HR platforms
    • 15 different marketing platforms
    • 3 different git platforms

Luckily, all cloud data warehouses (either natively or through third-party data transformation tools) have integrations with essentially every data source that exists to ensure that you are able to extract the data, transform it into a common structure, and load it into your data platform for consumption by stakeholders.

While you might have 15 different marketing platforms, each of your marketing executives at every portfolio company will be able to draw insights from the entire portfolio.

• Security, sharing, and scaling:
  • Even though you want to share insights across your portfolio, there may be some data that you would like to keep private. For instance, you may want your HR executives to know average salaries by role, but you may not want to necessarily share the exact salary details of every employee by name. You likely also don’t want an employee from portfolio company A to have full access to the accounting data of portfolio company B.
  • How do you ensure that sensitive data remains at the source, and valuable insights are sent up the chain?

Modern cloud data warehouses have trivialized this challenge through data sharing. Instead of loading all your data from all portfolio companies into a single data warehouse, a separate data warehouse is created for each portfolio company which then shares only the relevant data to a central data platform that creates curated data assets which will be accessed by stakeholders across your entire organization.

Such an architecture also simplifies the integration of any new portfolio companies – simply create a single data warehouse for the new portfolio company, leverage reusable workflows from existing implementations, and share the data to your central data platform.

• Cost:
  • Embarking on such an expedition tends to be costly. To build such a solution in-house, you'd need an entire data strategy department with:
    • Data architects who can design the structure of your data platform, create standardized data models for various datasets, and build a performant and cost-effective solution.
    • Data engineers who will perform the various development tasks associated with constructing your platform such as transforming data from multiple sources into a standard structure, aggregating and joining data to create new datasets, or building benchmarks.
    • Business intelligence engineers to design reports and dashboards to be consumed by your stakeholders.
    • Data strategy consultants who understand your business model and processes, can communicate your needs to architects/engineers, and can discover opportunities to leverage additional data streams (whether first-party or third-party) to improve decision making.
    • Data analysts to either manually log data from your existing Excel models/PowerPoints or write scripts to automatically parse your documents into a structured format.

Verus Ventures is uniquely positioned to provide top-of-class results. We've done this work for billion-dollar investors and as a stand-alone data product to drive deal sourcing for vertical software investors around the world.

In particular, we ensure that:

• Senior leadership is involved in the design and oversight of major implementations
• Competent employees with excellent communication skills are interacting with your team and building your solution - we take your business seriously, have a very high bar for quality, and do not outsource to any offshore teams
• Constant support and maintenance is available to enhance and expand your solution