How would we identify a better measure of academic productivity? Measures of academic productivity, like the h-index, m-index, i10-index are useful but imperfect. Their imperfections are more than an abstract concern. Hiring committees consider them in determining how the applicant stands in relation to his peers. The weight committees give to these numbers varies, especially with newer metrics.  

How do we know which measurement of scientific productivity is the most accurate? This question suggests that no measure is accepted as generally accurate. The h-index has a retrospective validation for some fields. Have studies tracked a cohort of scientists over time to compare the ability of these measurements to predict who received a tenure-track appointment in the next five years? (I realize that is not everyone's goal. It is one that tenure committees care about.)
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.