If you work at a publicly traded company, chances are your job comes down to 2 things: increase revenue and reduce cost.
Sometimes the line between action and outcome can get a little blurry. Execs make decisions to maximize revenue and minimize cost 10 years down the line. External comms might publish content to build brand, which will drive more revenue a year down the line. But on a principal basis, it all comes back to the same thing. Revenue up, cost down.
The question of ‘how to be a great’ analyst might vary from team to team and person by person. But there are a reasonably well defined set of outcomes the team can drive:
Each has a variety of tools, best practices, and quirks. Each probably has a dozen books for how to excel at a particular niche¹. What I’d like to encourage is to start with the outcome, then work backwards to the task. Or at a bare minimum identify the desired outcome, and keep it in mind as you build.
The OG ‘pls pull this number for me’ ask. Should we increase the fee cap or not? Should we launch the pilot globally? How many operators should we hire in Q3? At the lowest level this might be 6 lines of SQL. At the highest level this might be a 6-page causal inference analysis of the brand marketing impact on conversion. The sweet spot is somewhere in between, and most likely consists of a clear recommendation, a few charts or tables, and at times some supporting analysis. 90% of experiments and causal analysis land here.
The key is:
Aka made the same set of decisions multiple times². Air conditioners keep the room calibrated to 70 degrees. Analyst-driven feedback loops keep your team calibrated to their north star. Sometimes that’s maximizing conversion. Sometimes that’s operator performance (surprise: tracking back to profitability and conversion). Sometimes that’s defect rate.
The tools of the trade here are dashboards and narratives.