Analytics Request Intake Template

Basedash's intake framework sorts incoming requests by urgency and effort, and separately flags a new dashboard as costly because it creates ongoing maintenance, without saying how costly relative to what it saves. Sigma's self-service guide goes one step further: audit the ticket queue, then sort candidates by frequency multiplied by time-to-fulfill, because the top decile by that measure is where self-service returns the most hours. Both stop at consumption. Neither prices what building the fix actually costs, so neither can say whether building it pays off.

So Repeat Request Analysis carries both halves. Every topic that recurs three or more times gets a build-hours estimate next to the monthly hours it currently costs to answer by hand, and dividing the first by the second gives a payback period in weeks. A topic that recurs often but needs an expensive build, because it joins systems with no shared key, can pay back slower than a topic that recurs less but is cheap to build. Ranking by consumption alone cannot see that difference, because it never asks what the fix costs.

Kestrel Software, a fictional B2B SaaS company with a three-person analytics team, logged 340 requests over six months. Nine topics recurred three or more times, consuming 99.75 of 238.58 total ad hoc hours. Priced for payback, only two clear a one-quarter threshold: trial-to-paid conversion and logo churn, an 11-hour combined build reclaiming 7.08 hours a month. Pipeline coverage for board prep recurred seven times, more than five of the other eight, yet has the worst payback of all nine at 27.9 weeks, because its ten-hour build cost outweighs what it saves.