How would we identify a better measure of academic productivity?
I don't know that anyone has done this on a broad scale for tenure. There is a fairly recent study that used a machine learning approach to figure out what metrics are predictive of success in achieving a faculty position.

van Dijk, D., Manor, O., &amp; Carey, L. B. (2014). Publication metrics and success on the academic job market. Current Biology, 24(11), R516-R517.

For an overview and a simplied version of the application, see this Science Careers story.

From the paper's summary:


  The number of applicants vastly outnumbers the available academic
  faculty positions. What makes a successful academic job market
  candidate is the subject of much current discussion [1–4] . Yet, so
  far there has been no quantitative analysis of who becomes a principal
  investigator (PI). We here use a machine-learning approach to predict
  who becomes a PI, based on data from over 25,000 scientists in PubMed.
  We show that success in academia is predictable. It depends on the
  number of publications, the impact factor (IF) of the journals in
  which those papers are published, and the number of papers that
  receive more citations than average for the journal in which they were
  published (citations/IF). However, both the scientist’s gender and the
  rank of their university are also of importance, suggesting that
  non-publication features play a statistically significant role in the
  academic hiring process. Our model (www.pipredictor.com) allows anyone
  to calculate their likelihood of becoming a PI.


tl;dr Publish lots of papers that get lots of citations in good journals. But you knew that already.