Flat payments with fewer leads lift a higher-ed funnel from 7.8% to 9.4%

The figures reach the dev.to write-up secondhand, from Search Engine Land's analysis of schema, AI search, and entity gaps ^[4]. A lead-to-payment rate is payments divided by leads. The report says payment volume stayed roughly flat while lead volume fell ^[3]. Hold payments exactly equal across the two periods and the leads in the later one work out to 7.8/9.4 of the earlier level, about 17% fewer ^[1]. On that reading, the climb from 7.8% to 9.4% and the drop in lead volume are one measurement taken from either side of the division.

The two rates also cover windows of different length. The 7.8% is a 2025 figure and the 9.4% covers January through August 2026 ^[2], which is twelve months against eight ^[2]. Flat payment volume over eight months against twelve would mean payments per month went up. Flat within comparable months would mean something else. The write-up does not give lead or payment counts.

Entity coverage and structured data change what an AI system can state about an institution before anyone clicks, and the work at the two university partners was aimed at AI Overview and citation discovery ^[1]. If the pre-click summary is more accurate about what a university offers, fewer unqualified people submit a form, and the rate rises for a reason a marketer should want. Any other change that strips low-converting leads without stripping payments produces the same number. The account describes a program of entity gaps, schema updates, AI citation alignment, and higher education marketing, and says it does not isolate the precise contribution of each change ^[5]. Reported outcomes varied by partner and by measurement area ^[6].

The measurement discipline in the post survives better than its headline figure. Citation counts can show that an organization is becoming more visible in AI search experiences, and by themselves they do not show whether the resulting visitors are qualified or likely to become applicants or paying users ^[7]. The four questions the post puts to site owners include whether AI-related visibility is measured separately from traditional traffic and rankings, and whether visibility data can be connected to leads, qualified actions, conversion rates, and payments ^[8]. That last join is the cheap part of this work: a lead identifier that survives into the payment record. The same post closes by offering to identify entity coverage gaps and pointing readers at an AI Visibility / GEO Checker scan ^[9].