I am working on a marketing attribution model and my R-squared is quite low, but the coefficients for our ad spend are statistically significant. My manager is worried the model is "broken" because it doesn't explain all the variance. How do you communicate the value of a model when the predictive power is limited but the insights are still valid?
3 answers
This is a classic communication hurdle in data analytics. I usually explain that R-squared only tells us how much of the "noise" we can explain, not whether our "signal" is accurate. In human behavior fields like marketing, a high R-squared is almost suspicious because people are unpredictable. I focus the conversation on the P-values and the direction of the coefficients. Use an analogy: even if we can't predict exactly when every person will buy, we can prove that spending $1 on Ads leads to a $5 increase in revenue. Shift the focus from "prediction" to "influence" and "decision support."
Have you tried using a visual representation, like a scatter plot with the regression line, to show them how the general trend holds up despite the scattered data points?
In my experience, stakeholders only care about the R-squared because they read it in a blog post once. Just show them the ROI and they will stop asking.
True, Sandra. At the end of the day, business value trumps statistical perfection every single time in a corporate environment.
That’s a great idea, Jeffrey. I actually created a "Confidence Interval" band around the trend line. It helped my manager see that while we can't pin down a specific number, the probability of a positive return is very high. It changed the conversation from 'why is this wrong' to 'what is the range of likely outcomes,' which is a much healthier place for a business to be when planning budgets.