I am using logistic regression in a work setting (e.g. subscription conversion in a technology product). I would like to communicate about what independent variables a team should focus on to increase an outcome variable.
The independent variables of this regression include both continuous and categorical variables. Let’s say we have these independent variables:
If we run the regression with these variables “as is”, communicating the variable coefficients relative to each other could be confusing. Increasing
Avg_items by 1 may have a much smaller impact than increasing day_active by 1, but increasing
Avg_items by X may lead to the same impact as increasing day_active by 1. This insight is somewhat hidden though since a stakeholder just looking at the coefficients might conclude increasing day_active by 1 has a higher ROI than focusing on
Avg_items, even though that may not be true.
Also, it's non-intuitive comparing the coefficients of a categorical and continuous variable, especially for prioritization.
Another choice is to change
Avg_items into categorical variables so that the variables become unitless and therefore coefficients more readily comparable.
I’ve seen people transform continuous variables into categorical ones by looking at the distribution of the variable and splitting it at the knee in the distribution or just using quantiles, etc. Seems reasonable.
The obvious downside is that we could be losing a lot of information binning the continuous variables into categorical ones. I guess we could compare regression diagnostics between a model with the continuous variables and one with just categorical variables to quantify how much we are losing. But the loss may be worth it if it makes the results easier to explain and understandable by stakeholders.
Does this all make sense? Or are there other ideas I should be considering for communicating drivers of a product outcome using regression, or communicating headroom?