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+ Coverage   95.59%   96.50%   +0.90%     
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@cetagostini I think I should be adding in the channel spend variables into the model formula, right?

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cetagostini commented on 2025-11-05T07:17:35Z
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All good, but two important points:

1. The spend or response should not be scaled. So, you can safely pass everything in the original scale, the model will handle everything under the hood. Doc here: https://github.com/pymc-labs/pymc-marketing/blob/main/pymc_marketing/mmm/multidimensional.py#L1828
2. The impact input should be MEAN not cumulative. Basically, this same values divide by the N length of the experiment.

drbenvincent commented on 2025-11-13T14:11:05Z
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Removed mention of normalising the results in cb64c19

drbenvincent commented on 2025-11-13T14:28:55Z
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remaining stuff about the mean (lift/week of the experiment) should be done in dc21e9c

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cetagostini commented on 2025-11-05T07:17:36Z
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Good, I'll make a PR to make things clear there as well.

drbenvincent commented on 2025-11-06T09:47:21Z
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I've already given that a go in a PR that I think you've seen pymc-labs/pymc-marketing#2061

I've already given that a go in a PR that I think you've seen pymc-labs/pymc-marketing#2061

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Removed mention of normalising the results in cb64c19

View entire conversation on ReviewNB

remaining stuff about the mean (lift/week of the experiment) should be done in dc21e9c

View entire conversation on ReviewNB