Analysts often cherry-pick the model that looks best. BMA avoids this bias by running thousands of models with different variable combinations and averaging them based on how likely they are to be true.
"Which variables actually matter?"
"How robust is this insight?"
"Are we overfitting?"
It's a weighted democracy of models. Better models get more votes. The final prediction includes the uncertainty of model selection itself.
The Problem
Fragile models that break with new data.
What It Reveals
The 'Probability of Inclusion' for each channel.
Decision Enabled
Focus only on variables that persist across models.
Used in our Insight Engine to ensure we only surface insights that are statistically robust, not random flukes.
A bar chart of Inclusion Probabilities. If "TV Spend" has a 99% probability, it's definitely driving sales. If "Twitter" is 30%, it's likely noise.