diff --git a/pymc_marketing/mmm/transformers.py b/pymc_marketing/mmm/transformers.py
index a37e8abff..8f2ab48aa 100644
--- a/pymc_marketing/mmm/transformers.py
+++ b/pymc_marketing/mmm/transformers.py
@@ -58,7 +58,7 @@ def batched_convolution(
         ax = plt.subplot(111)
         for mode in [ConvMode.Before, ConvMode.Overlap, ConvMode.After]:
             y = batched_convolution(spends, w, mode=mode).eval()
-            suffix = "\n(default)" if mode == ConvMode.Before else ""
+            suffix = "\n(default)" if mode == ConvMode.After else ""
             plt.plot(x, y, label=f'{mode.value}{suffix}')
         plt.xlabel('time since spend', fontsize=12)
         plt.ylabel('f(time since spend)', fontsize=12)
@@ -70,16 +70,16 @@ def batched_convolution(
 
     Parameters
     ----------
-    x :
+    x : tensor_like
         The array to convolve.
-    w :
+    w : tensor_like
         The weight of the convolution. The last axis of ``w`` determines the number of steps
         to use in the convolution.
     axis : int
         The axis of ``x`` along witch to apply the convolution
     mode : ConvMode, optional
         The convolution mode determines how the convolution is applied at the boundaries
-        of the input signal, denoted as "x." The default mode is ConvMode.Before.
+        of the input signal, denoted as "x." The default mode is ConvMode.After.
 
         - ConvMode.After: Applies the convolution with the "Adstock" effect, resulting in a trailing decay effect.
         - ConvMode.Before: Applies the convolution with the "Excitement" effect, creating a leading effect
@@ -89,7 +89,7 @@ def batched_convolution(
 
     Returns
     -------
-    y :
+    y : tensor_like
         The result of convolving ``x`` with ``w`` along the desired axis. The shape of the
         result will match the shape of ``x`` up to broadcasting with ``w``. The convolved
         axis will show the results of left padding zeros to ``x`` while applying the
@@ -211,7 +211,7 @@ def geometric_adstock(
         The axis of ``x`` along witch to apply the convolution
     mode : ConvMode, optional
         The convolution mode determines how the convolution is applied at the boundaries
-        of the input signal, denoted as "x." The default mode is ConvMode.Before.
+        of the input signal, denoted as "x." The default mode is ConvMode.After.
 
         - ConvMode.After: Applies the convolution with the "Adstock" effect, resulting in a trailing decay effect.
         - ConvMode.Before: Applies the convolution with the "Excitement" effect, creating a leading effect
@@ -293,7 +293,7 @@ def delayed_adstock(
         The axis of ``x`` along witch to apply the convolution
     mode : ConvMode, optional
         The convolution mode determines how the convolution is applied at the boundaries
-        of the input signal, denoted as "x." The default mode is ConvMode.Before.
+        of the input signal, denoted as "x." The default mode is ConvMode.After.
 
         - ConvMode.After: Applies the convolution with the "Adstock" effect, resulting in a trailing decay effect.
         - ConvMode.Before: Applies the convolution with the "Excitement" effect, creating a leading effect
@@ -396,7 +396,7 @@ def weibull_adstock(
         The axis of ``x`` along witch to apply the convolution
     mode : ConvMode, optional
         The convolution mode determines how the convolution is applied at the boundaries
-        of the input signal, denoted as "x." The default mode is ConvMode.Before.
+        of the input signal, denoted as "x." The default mode is ConvMode.After.
 
         - ConvMode.After: Applies the convolution with the "Adstock" effect, resulting in a trailing decay effect.
         - ConvMode.Before: Applies the convolution with the "Excitement" effect, creating a leading effect