When e-commerce pioneers like Amazon and eBay emerged in the 1990s, financial markets faced new challenges. Traditional accounting, built for brick-and-mortar factories and physical inventory, struggled to capture the value created by software-driven businesses. The dot-com boom and crash highlighted the limitations of relying solely on historical accounting records to understand rapidly changing consumer demand.<\/p>\n\n\n\n
For accounting and finance professionals, historical revenue reflects what has already happened. In contrast, online customer traffic leaves a real-time digital trace of user interest long before cash flows hit the income statement. A study published in the Journal of Accounting and Public Policy<\/em> by accountancy professors Po-Chang Chen and Matthew Wieland, together with Theodore Sougiannis (University of Illinois at Urbana-Champaign) and Zach Zhiguang Wang (Uber), investigates a compelling question: <\/p>\n\n\n\n When companies voluntarily publish internal online user metrics (e.g., monthly active users), does it provide useful information about future financial performance beyond external third-party tracking tools? <\/p>\n\n\n\n For website owners and corporate executives, digital audience measurement generally falls into two distinct buckets: first-party disclosures<\/strong> and third-party estimates<\/strong>.<\/p>\n\n\n\n Measuring web traffic accurately across the internet is notoriously difficult due to non-human bot traffic, cookie deletion, shared family screens, and multi-device browsing. While third-party tools offer valuable cross-industry comparisons, panel-based estimates suffer from statistical sampling noise. Direct internal analytics, by contrast, measure exact account activity across platforms.<\/p>\n\n\n\n A timeline comparing third-party monthly data releases against quarterly company earnings announcements (using Facebook as an example). While external vendors provide more frequent updates, internal company disclosures deliver exact, comprehensive engagement numbers alongside quarterly financial results<\/em>.<\/p>\n\n\n\n Source: Chen, P.-C., Sougiannis, T., Wang, Z. Z., & Wieland, M. (2026). The incremental informativeness of firm-disclosed web traffic metrics beyond third-party estimates. Journal of Accounting and Public Policy<\/em>, 58<\/em>, Article 107450.<\/p>\n<\/blockquote>\n\n\n\n To test which traffic numbers offer genuine financial foresight, the researchers analyzed 69 public internet companies, comparing 21 firms that voluntarily disclosed their internal user traffic metrics (such as monthly active users) against 48 firms that relied strictly on external third-party tracking.<\/p>\n\n\n\n The researchers found that first-party web traffic growth strongly predicts actual sales growth two, three, and four quarters into the future<\/strong>. In contrast, third-party vendor growth estimates held no statistically significant predictive power for future sales growth once other financial factors were accounted for.<\/p>\n\n\n\n The researchers suggest that this lag reflects the time required to convert increased user activity into monetized sales, subscriptions or advertising revenue. Early surges in user visits reflect growing brand awareness and consideration, but converting that top-of-funnel attention into monetized sales, subscriptions, or advertising revenue takes between 6 to 12 months<\/strong> to materialize on financial reports.<\/p>\n\n\n\n The study also found that analysts and investors respond to these disclosures in several ways:<\/p>\n\n\n\n Because voluntary disclosures are published at management’s discretion, regulators like the U.S. Securities and Exchange Commission (SEC) have occasionally expressed concern that companies might strategically inflate user metrics to mask poor financial performance.<\/p>\n\n\n\n The researchers found no evidence to support these concerns. The researchers conducted extensive integrity checks and found no evidence of opportunistic reporting<\/strong>:<\/p>\n\n\n\n Instead, companies that choose to disclose user metrics tend to operate social platforms, digital marketplaces, and online communities, where active user participation directly drives the platform’s underlying economic value.<\/p>\n\n\n\n For investors and financial analysts, company-reported user metrics can provide an additional indicator of future revenue growth. Because changes in online customer activity may precede changes in reported sales, these metrics can provide information that complements traditional financial measures. <\/p>\n\n\n\n <\/p>\n\n\n\n \u201cI enjoy looking at information that investors can use to evaluate firms when traditional financial statements do not fully capture the key drivers of the business. What made this setting especially interesting to me was the availability of two different sources of information about essentially the same underlying activity\u2014metrics voluntarily disclosed by firms and estimates produced by third-party vendors.\u201d<\/p>MATThew Wieland<\/cite><\/blockquote><\/figure>\n<\/div>\n\n\n\n \u201cAs someone interested in firms’ financial reporting choices and quality, I was drawn to the research idea because we observed that some, but not all, internet firms chose to voluntarily report web traffic (a nonfinancial performance indicator) in their earnings releases. In addition to this unique reporting practice, third-party vendors also regularly provide estimates of similar information. The availability of two information sources (as Matt mentioned) makes it particularly interesting to me to investigate whether and how firm disclosures (arguably more accurate and complete, but subject to greater strategic reporting biases than vendor estimates) provide additional informational value to investors and analysts.\u201d<\/p>Po-Chang Chen<\/cite><\/blockquote><\/figure>\n<\/div>\n<\/div>\n\n\n\n <\/p>\n\n\n\nFirst-Party Data vs. Third-Party Estimates<\/strong><\/h1>\n\n\n\n
\n
![\"Timeline](\"https:\/\/sites.miamioh.edu\/doamu\/files\/2026\/10\/Web-traffic-timeline.jpg\")
<\/figure>\n\n\n\n\n
The Predictive Horizon: Two to Four Quarters Ahead<\/strong><\/h1>\n\n\n\n
\n
Debunking the Manipulation Myth<\/strong><\/h1>\n\n\n\n
\n
Implications for Financial Analysis<\/strong><\/h1>\n\n\n\n
Why study this topic?<\/em><\/h2>\n\n\n\n
![\"Matt](\"https:\/\/sites.miamioh.edu\/doamu\/files\/2026\/10\/Wieland-Matt-O10016.jpg\")
<\/figure>\n<\/div>\n<\/div>\n\n\n\n![\"Po-Chang](\"https:\/\/sites.miamioh.edu\/doamu\/files\/2026\/10\/Chen-Po-Chang-O05024_002.jpg\")
<\/figure>\n<\/div>\n\n\n\n