Financial Services

Private Equity and Venture Capital Deal Sourcing Platform

author Josh Miramant January 10, 2020

PHASE 2: Data Visualization and Integration

PHASE 3: Predictive Analytics and Automation

The Challenge

A leading Private Equity and Venture Capital firm, with over $20B under management, requested an end-to-end data audit of their deal platform. They were looking to evolve the platform to make a scalable and unified sourcing tool with consistent architecture and infrastructure. They wanted an independent third-party to assess the technical decisions made to date in the development.

The firm needed help integrating 4 newly acquired CRM/ERP companies. Each acquired company had its own databases, in its own format. A lack of visibility into the sales process of these siloed data systems hindered coordination, planning, and tracking. The blind sales department had no concise data to direct their time and resources. Due to disparate data sets, the company had no insight into the efficacy of their upper funnel engagement or attribution across their sales cycle. As a result, they had a low conversion rate on sales efforts.

The company’s existing development team had no resources for an internally focused, stand-alone project so they hired Blue Orange. The goal was to provide architectural guidance on their data infrastructure to support unified data and sales optimization.

A complete project assessment was provided with a budget for the AWS data lake transition and the cost benefit analysis of these improvements to the sales projections with the new system.

Sector Finance

Vertical Sales Optimization

Stage Predictive Platform

The Solution

PHASE 1: Data Transformation

Created a scalable architecture that confidently could handle all data-driven operational growth and would not be outpaced by all the input.

PHASE 2: Data Visualization and Integration

Improved sales modeling and oversight with real-time, full-funnel dashboards.

PHASE 3: Predictive Analytics and Automation

Increased top of funnel conversion using ML prediction to improve lead segmentation.

Quick results. They needed to solve their critical problems quickly and then add complexity later.

Additional PHASE 4: Improve Portfolio Company Performance

As an investor, offering real-time data science as a service to your portfolio companies, you will improve their performance and thus your investment.

PHASE 2: Data Visualization and Integration

PHASE 3: Predictive Analytics and Automation

Phase 2 & 3 Results

Improved and Automated Deal Sourcing Platform

In private equity, you are only as good as your last investment deal. If the pipeline for deals dries up, deploying your “dry powder” (i.e., unallocated capital) becomes much more difficult. That’s why we recommend using data science to automate your deal sourcing.

  • Scrape and Collect. Code a bot to collect data from public sources (e.g., stocks, Venture Beat, Bloomberg, news/current events, social media. etc) and private sources about potential companies in the market. Compile all this data into a unified source without the limits of rows and columns to search for trends and associations with real-time data. Immediately have thousands of companies added to your watch list.
  • Track. Turn on notifications for spikes in value, public interest, or anomalies of a defined magnitude. Train your system to identify triggers of big events.
  • Quantify Non-Financial Data. Most private equity transactions involve private companies where there is limited public financial data. At the deal sourcing stage, you need identify promising deals based on non-financial data points such as growth and engagement trends in web traffic and social media. While Internet engagement does not equate to revenue, rapid growth in these metrics suggests that a company is successfully attracting attention.
  • Score Prospects. Next, organize your potential companies by scoring and ranking companies to identify promise. Find companies that fit your financial criteria in terms of profitability and growth, with minimal competitive investors. Each classification or grouping method can be altered, weighed, and calculated differently to optimize results continuously. Traditional, manually built lead scoring systems are by definition time-consuming and error-prone. Also, they are rigid and do not allow keeping up with new customer data inputs. Predictive analytics can be employed to maximize lead understanding. Predictive lead scoring models can process data related to past deals (both won and failed) and assign a fit score to each lead, corresponding to the likelihood of each customer being a win.
  • Advanced filtering: Tailor your outreach efforts by narrowing down the options to only those with the most potential. Saving human interaction and effort for deals that are worth company investment to pursue.
  • Enable Peer Analysis. When you look at three different companies in the cybersecurity software field, how do you evaluate them? Use data analytics to review how end customers are discussing these products (e.g., using sentiment analysis). Further in the deal process, you can use analytics to compare business model differences (e.g. pricing, customer lifetime value, and expenses).
  • Refine Investment Models. Modeling future investment returns in private equity is nothing new. Data analytics has a role to play in helping you to improve the reliability of those projections. For instance, a portfolio company projects 50% year over year revenue growth. How do you know if that is a credible forecast? Use data analytics to conduct a bottom-up financial forecast. If the company relies heavily on digital marketing methods to acquire leads and customers, it will be even easier to develop these models.

To deliver those capabilities, you don’t need to build your internal analytics and consulting department. Instead, leverage Blue Orange Digital. We can step in and help your company optimize its operations.

Your Next Step To Get Started

There are two pathways to bring data science and analytics capabilities to your firm. First, you can adopt the “build” approach – hire a whole department of specialists in data. This approach can work! However, it is slow and expensive to build such a department, especially if it is outside of your firm’s core competency.

The second choice: partner with a data science firm like Blue Orange. With this approach, you get data science expertise for your portfolio firms as needed.

Contact us to take a closer look at how Blue Orange makes both of these wins possible.

See PHASE 1: Data Transformation

See Additional PHASE 4: Improve Portfolio Company Performance


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