diff --git a/customer_segments/customer_segments.html b/customer_segments/customer_segments.html index 388f6ae..65d5b99 100644 --- a/customer_segments/customer_segments.html +++ b/customer_segments/customer_segments.html @@ -13636,7 +13636,7 @@
<matplotlib.axes._subplots.AxesSubplot at 0x7f75cfe07860>+
<matplotlib.axes._subplots.AxesSubplot at 0x7fda386164e0>@@ -13679,7 +13679,7 @@
Answer:
<matplotlib.axes._subplots.AxesSubplot at 0x7f75d22bdf60>+
<matplotlib.axes._subplots.AxesSubplot at 0x7fda3acd8f60>
Answer: -I would say that a lower delivery frequency would have less impact on Detergents_Paper, and Frozen categories, and the bigger impact on Fresh and Delicatessen. I am inclined to comment that customers from Segment 1 are more likely to accept the change because their purchases are less affected by Fresh and Delicatessen.
+To run an A/B test, the wholesale distributor could select a subset of the customers (say 10%) in which both Clusters are equally represented; this subset of customers would have the delivery frequency changed for a certain period. Customer reactions will then be analyzed for the two clusters separately, and each cluster may or may not have the change applied permanently.