Three ways venture capitalists use data analytics

The art of venture capitalism (VC) is being transformed by the science of data analytics. Many early-stage startup investors use data-driven decision-making to guide their lead sourcing and investments. The ability to review vast volumes of big data can help venture capitalists answer three crucial questions: Where are the best companies and entrepreneurs? Whether to invest? What KPIs should be set for the new company?

1. Where are the best companies and entrepreneurs

While the instinct for scouting great investment opportunities is usually developed through years in the field, venture capital firms and associates can improve their scouting process by using data to analyze a variety of ecosystems. According to a report by data-driven VC, it is predicted that by next year, 75% of all VC deal considerations will use data, analytics and artificial intelligence to inform investment decisions.

Data availability, when combined with consolidated analytics, machine learning models and Financial AI tools are helping venture capitalists to be more efficient. With access to quality data and analysis, venture capitalists can assess more tech companies in more global locations, reduce their miss rate and fast track investment decisions and deal flow.

Data-driven initiatives are also helping VC firms reduce gender bias and make better, fairer investment decisions.

2. Whether to invest in a particular company?

While there isn’t a lot of visibility into startup investments, predictive modelling techniques can help investors check their gut-instincts against the facts. Predictive modelling evaluates multiple factors that forecast the likelihood of a startup’s success. This works in the same way a credit score is calculated by assigning marks to a variety of attributes or factors in the algorithm. Some examples of insights that help determine if a company is likely to thrive are as follows:

Team background: Criteria for educational background, employment history, and entrepreneurial experience, especially if the management team has a relevant background in the field. Data shows that a startup with two founders from different universities is twice as likely to succeed as those with founders from the same university.

Funding: An important performance metric is whether a company will go on to raise an additional round of funding, from seed stage to series A. A seed investment of at least $1.5 million is an early indicator of a company’s future success. Those with less than $1.5 million tend not to raise the additional funding needed to be successful.

Digital footprint: Probably the most extensive source of information, especially for B2C companies, is a company’s digital footprint. Linkedin, Facebook, web traffic, and so on, can provide useful insights for investors. By identifying keywords that express positive or negative sentiments, it's possible to rank each company’s public sentiment quantitatively. Venture Capitalists can then set alerts if there are any “sentiment-changing” events.

Financial information: Analytics has enabled VCs to examine traditional financial sources and discover insights deep within central bank reports and the company earning statements to enhance the due diligence and obtain a clear understanding a startup’s valuation, market position and growth potential.

3.What KPIs should be set for the new company?

Once a venture capital investment has been made, the business model includes benchmarks to track the start-ups performance. The VC establishes specific metrics for their startup investments to make sure the portfolio company is heading in the right direction. This way the VC can quickly diagnose and resolve any potential problems before they become real issues. The most common key performance indicators (KPIs) include:

Revenue

• MMR: Monthly Recurring Revenue
• CMMR: Committed monthly recurring revenue
• ARR: Annual Recurring Revenue
• ARRR: Annual Run Rate Revenue
• Growth in Recurring Revenue
• Revenue Growth Rate

Customers

• LTV: Lifetime Value of Customer
• CAC: Customer Acquisition Cost
• Customer Acquisition Cost Payback Period
• Customer Churn: Gross and Net
• Conversion Rates: Sales Funnel; Site conversion; etc.

Cash flow

• Gross burn rate
• Net burn rate
• Free cash flow over time.

All of these metrics indicate the primary consideration: return on investment (ROI). The higher the number in the ROI equation, the more money a VC will make for every dollar invested. Finally, it’s important to establish key industry-specific metrics. At Phocas, we work with companies to establish industry-specific metrics as well as KPIs for their unique needs. You won’t need a data scientist or data teams to measure these metrics as the software can be used in-house by all people.