How to Identify Software Companies in the AI Era? The Hottest Metric in the U.S. Market: NRR

The AI wave is both an opportunity and a minefield, and investors urgently need a more reliable compass to navigate.

Currently, AI is profoundly changing the investment logic of the software industry. To sift through numerous companies and identify the true "AI winners," the U.S. capital market is focusing on a key metric—Net Revenue Retention (NRR).

This is not just a financial figure; it is seen as a new method for assessing a company's intrinsic growth momentum and customer stickiness, especially in judging the market acceptance of its AI products. However, this metric lacks a unified standard, with various companies employing different calculation methods, making horizontal comparisons nearly impossible and leaving room for data "beautification."

Investors' "New Favorite": Why is NRR So Important?

NRR, also known as Net Dollar Retention, fundamentally measures a company's ability to continue generating revenue from existing customers. An NRR exceeding 100% intuitively indicates that existing customers are not only retained but are also increasing their spending.

According to Rishi Jaluria, a software stock analyst at Royal Bank of Canada Capital Markets, this intrinsic growth is crucial. "If customers find tremendous value in your product, they are willing to invest more year after year."

He pointed out that enhancing the "wallet share" of existing customers is a more efficient and sustainable growth model compared to the increasingly costly customer acquisition market. For this reason, a high NRR is often seen as strong evidence of the success of a company's AI product strategy.

Beware of Number Games: The Financial Reporting Magic of "Honor Roll" Figma

Of course, this new method is not without its flaws. Investors need to maintain a level of clarity when using NRR. According to surveys, there is currently no unified industry calculation standard for this metric.

For example, IPO documents reveal that software company Figma disclosed a striking NRR of 132% in its IPO filing, but its calculation scope was limited to customers with annual contract values exceeding $10,000, excluding a large number of smaller clients.

While the company claims this is more valuable for reference, DA Davidson analyst Gil Luria pointed out that its "calculation method is controversial"—the company only selected 11,000 customers (accounting for 2.4% of total customers) with annual repurchase amounts exceeding $10,000 as a sample, which contributed 64% of revenue, but excluding 87.6% of customers rendered horizontal comparisons meaningless.

Other companies also have their own "tricks." It is understood that software development tool company GitLab initially reported an exact NRR of 148% at its IPO, but later quarterly reports changed to only disclosing whether it fell below the threshold of 130%, leaving investors in an information blind spot.

Cybersecurity company Rubrik uses the average NRR over the last four quarters and refuses to disclose single-quarter data; data management company Snowflake has extended the NRR calculation period from the standard 12 months to two years

From Horizontal Comparison to Vertical Tracking: The Correct "Opening Method" for NRR

Despite differences in calculation methods, NRR is still regarded as an important screening tool as the software sector faces widespread pressure from AI impacts.

Gil Luria believes that investors should shift their focus from comparing the absolute values of NRR among different companies to tracking the quarterly changes in NRR for the same company.

"Now, if you focus on those companies whose NRR this quarter is higher than the previous quarter, that’s a great clue for judging who is performing better."

Gil Luria uses Snowflake and Datadog as examples, pointing out that these companies' NRR metrics have rebounded from recent lows, strongly demonstrating that their AI products are receiving positive market reception. This dynamic, vertical observation method provides investors with a new tool to filter out market noise and discover real value.

Therefore, when applying this new method of NRR, understanding the underlying calculation logic and integrating it with other financial data for comprehensive judgment is the wise approach