# Inconsistent ROAS when migrating to multi-dim MMM class

**URL:** <https://discourse.pymc.io/t/inconsistent-roas-when-migrating-to-multi-dim-mmm-class/17488>\
**Category:** v5\
**Tags:** modeling\
**Created:** [December 15, 2025, 1:17pm UTC](https://discourse.pymc.io/t/inconsistent-roas-when-migrating-to-multi-dim-mmm-class/17488 "2025-12-15T13:17:14Z")\
**Posts on this page:** 6\
**Page:** 1

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**Author:** ![NRJ](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/nrj/32/9541_2.png) [@NRJ](https://discourse.pymc.io/u/NRJ)\
**Post date:** [December 15, 2025, 1:17pm UTC](https://discourse.pymc.io/t/inconsistent-roas-when-migrating-to-multi-dim-mmm-class/17488/1 "2025-12-15T13:17:14Z")

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Hello,  
I trained an MMM using pymc-marketing and realized that it’s probably better to migrate to the multi-dim MMM class for budget allocation analysis, even though I have a single geo to model. Despite keeping everything the same (input data, model configs, random seed for model fit), I see a significant difference in the ROAS between the two models. In my understanding, the results matched better with the real-world scenario for the single geo MMM. I don’t understand what could have caused this — looking for advice. Please let me know if I should provide more context. Thank you.

```auto
prior_sigma = total_spend_per_channel / total_spend_per_channel.sum()

my_model_config = {
“intercept”: Prior(“HalfNormal”, sigma=5, dims=(“geo”,)),
“saturation_beta”: Prior(“HalfNormal”, sigma=prior_sigma, dims=(“channel”,)),
“gamma_control”: Prior(“Normal”, mu=0, sigma=0.05, dims=(“control”,)),
“gamma_fourier”: Prior(“Laplace”, mu=0, b=0.2, dims=(“geo”, “fourier_mode”)),
“likelihood”: Prior(“TruncatedNormal”, sigma=Prior(“HalfNormal”, sigma=6), dims=(“date”, “geo”)),
}

my_sampler_config = {“progressbar”: True}

mmm = MMM(
date_column="date",
target_column="y",
channel_columns=channels,
control_columns=controls,
scaling={
“channel”: {“method”: “max”, “dims”: ()},
“target”: {“method”: “max”, “dims”: ()},
},
adstock=GeometricAdstock(l_max=10),
saturation=LogisticSaturation(),
yearly_seasonality=2,
model_config=my_model_config,
sampler_config=my_sampler_config,
dims=("geo",),
)

```

---

<div class="post-metadata">

**Author:** ![TeemuSailynoja](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/teemusailynoja/32/9204_2.png) [@TeemuSailynoja](https://discourse.pymc.io/u/TeemuSailynoja)\
**Post date:** [December 21, 2025, 11:31pm UTC](https://discourse.pymc.io/t/inconsistent-roas-when-migrating-to-multi-dim-mmm-class/17488/2 "2025-12-21T23:31:50Z")

</div>

An important difference is that the multidimensional MMM defaults to pooled modeling where no explicit dimensions are specified, whereas the single dimensional MMM defaults to separate model.

Looking at your `model_config`, you might want to add in priors with `dims=”channel”` for `adstock_alpha`, and `saturation_lam`. This dhould unify the model to what you had with the single dimensional class.

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<div class="post-metadata">

**Author:** ![NRJ](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/nrj/32/9541_2.png) [@NRJ](https://discourse.pymc.io/u/NRJ)\
**Post date:** [December 22, 2025, 2:47pm UTC](https://discourse.pymc.io/t/inconsistent-roas-when-migrating-to-multi-dim-mmm-class/17488/3 "2025-12-22T14:47:34Z")

</div>

Hello,

Thanks for writing, I tried adding dims argument in Prior for `adstock_alpha` and `saturation_lam` in `my_model_config` and it looks like the following

```auto
my_model_config = {

    "intercept": Prior("HalfNormal", sigma=5),

"saturation_beta": Prior("HalfNormal", sigma=prior_sigma, dims=("channel")),

"saturation_lam": Prior("Gamma", alpha=3, beta=1, dims=("channel")),

"adstock_alpha": Prior("Beta", alpha=1, beta=3, dims=("channel")),

"gamma_control": Prior("Normal", mu=0, sigma=0.05, dims=("control")),

"gamma_fourier": Prior("Laplace", mu=0, b=0.2, dims=("geo","fourier_mode")),

"likelihood": Prior("TruncatedNormal", sigma=Prior("HalfNormal", sigma=6), dims=("date")),

} 

```

I could see improvement in low spend channels, but for big channels ROAS are high and not matching with the regular mmm. Multi-d MMM gives the following

`{'Channel-a': 9.21,`  
` 'Channel-b': 0.22,`  
` 'Channel-c': 0.76,`  
` 'Channel-d': 1.11,`  
` 'Channel-e': 0.86,`  
` 'Channel-f': 1.74,`  
` 'Channel-g': 1.07,`  
` 'Channel-h': 2.33,`  
` 'Channel-i': 0.185}`

and ROAS from regular MMM are as follows

`{'Channel-a': 2.13`  
` 'Channel-b': 0.82,`  
` 'Channel-c': 0.56,`  
` 'Channel-d': 1.71,`  
` 'Channel-e': 0.89,`  
` 'Channel-f': 1.23,`  
` 'Channel-g': 0.15,`  
` 'Channel-h': 1.89,`  
` 'Channel-i': 0.38}`

In my case, ‘Channel-a’ is the one with highest spend share of 50% and multi-d MMM is really off in predicting its ROAS (with a huge credible interval). I also tried adding the `‘geo’ argument` in `dims` but it didn’t help either. To add more context, `prior_sigma` which in my case is spend share is the following

```auto
array([0.49728545, 0.08402681, 0.10286331, 0.0533288 , 0.02376035,
       0.06204508, 0.15416075, 0.01652881, 0.00600064])

```

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<div class="post-metadata">

**Author:** ![NRJ](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/nrj/32/9541_2.png) [@NRJ](https://discourse.pymc.io/u/NRJ)\
**Post date:** [March 17, 2026, 2:56pm UTC](https://discourse.pymc.io/t/inconsistent-roas-when-migrating-to-multi-dim-mmm-class/17488/4 "2026-03-17T14:56:41Z")

</div>

I figured out the issue, in the new multi MMM class, xarray channel coordinate in model’s inference data (idata) are in **alphabetic** order whereas in the older MMM class it was in the same order as defined in channels column. This created a problem when I was using the same code for calculating ROAS (contribution.sum()/spend.sum()) from regular MMM in new multi MMM. So in short for e.g. I was accidentally dividing channel A’s contribution from channel B’s spend sum.

---

<div class="post-metadata">

**Author:** ![jessegrabowski](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jessegrabowski/32/5010_2.png) [@jessegrabowski](https://discourse.pymc.io/u/jessegrabowski)\
**Post date:** [March 17, 2026, 3:04pm UTC](https://discourse.pymc.io/t/inconsistent-roas-when-migrating-to-multi-dim-mmm-class/17488/5 "2026-03-17T15:04:29Z")

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I’d open an issue about that if I were you, seems like surprising behavior

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<div class="post-metadata">

**Author:** ![NRJ](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/nrj/32/9541_2.png) [@NRJ](https://discourse.pymc.io/u/NRJ)\
**Post date:** [March 18, 2026, 10:52am UTC](https://discourse.pymc.io/t/inconsistent-roas-when-migrating-to-multi-dim-mmm-class/17488/6 "2026-03-18T10:52:12Z")

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sure, that’s a good idea. I have created one.