diff --git a/samples/improvado_cross_channel_data_model_and_visualization/README.md b/samples/improvado_cross_channel_data_model_and_visualization/README.md
new file mode 100644
index 0000000..7bf0910
--- /dev/null
+++ b/samples/improvado_cross_channel_data_model_and_visualization/README.md
@@ -0,0 +1,5 @@
+# Improvado Cross-Channel data model Snowflake Notebooks
+
+## Overview
+This Salesforce notebook demonstrates a cross-channel data model that consolidates key marketing metrics across primary data sources, campaigns, ad sets, ads, and creatives. The notebook includes a simple visualization to help illustrate the data relationships and insights.
+
diff --git a/samples/improvado_cross_channel_data_model_and_visualization/improvado_cross_channel_snowflake_notebook.ipynb b/samples/improvado_cross_channel_data_model_and_visualization/improvado_cross_channel_snowflake_notebook.ipynb
new file mode 100644
index 0000000..c4512cc
--- /dev/null
+++ b/samples/improvado_cross_channel_data_model_and_visualization/improvado_cross_channel_snowflake_notebook.ipynb
@@ -0,0 +1,94 @@
+{
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Streamlit Notebook",
+   "name": "streamlit"
+  }
+ },
+ "nbformat_minor": 5,
+ "nbformat": 4,
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1ae26c7c-c3cc-4df5-9018-7b53f32242e0",
+   "metadata": {
+    "name": "cell7",
+    "collapsed": false
+   },
+   "source": "# Improvado Cross-Channel Model overview and visualization "
+  },
+  {
+   "cell_type": "code",
+   "id": "3775908f-ca36-4846-8f38-5adca39217f2",
+   "metadata": {
+    "language": "python",
+    "name": "cell1",
+    "collapsed": false
+   },
+   "source": "# Import python packages\nimport streamlit as st\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport matplotlib.dates as mdates\n\n# We can also use Snowpark for our analyses!\nfrom snowflake.snowpark.context import get_active_session\nsession = get_active_session()\n",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "id": "8d50cbf4-0c8d-4950-86cb-114990437ac9",
+   "metadata": {
+    "language": "sql",
+    "name": "cell2",
+    "collapsed": false
+   },
+   "source": "-- Write SQL query to make simple marketing performance report\n\nSELECT      date,\n            datasource,\n            campaign_type,\n            SUM(clicks) as clicks,\n            SUM(impressions) as impressions,\n            SUM(spend) as spend,\n            SUM(revenue) as revenue,\n            CASE\n                WHEN SUM(clicks) = 0.0 THEN 0.0\n                ELSE SUM(spend)/SUM(clicks)\n            END as CPC,\n            CASE\n                WHEN AVG(impressions) = 0.0 THEN 0.0 \n                ELSE AVG(clicks)/AVG(impressions)\n            END as CTR,\n            CASE\n                WHEN SUM(SPEND) = 0.0 THEN 0.0\n                ELSE SUM(Revenue)/SUM(SPEND)*100\n            END as ROAS\nFROM        TESTDB.TESTSCHEMA.CROSS_CHANNEL_MODEL_FOR_SNOWFLAKE_AI\nGROUP BY    date,\n            datasource,\n            campaign_type",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "id": "c695373e-ac74-4b62-a1f1-08206cbd5c81",
+   "metadata": {
+    "language": "python",
+    "name": "cell3",
+    "codeCollapsed": false,
+    "collapsed": false
+   },
+   "source": "# Create dataframe from SQL result\ncross_channel_df = cell2.to_pandas()\n\n# Ensure the DATE column is in datetime format\ncross_channel_df['DATE'] = pd.to_datetime(cross_channel_df['DATE'])\n\n# Create a new column for the week\ncross_channel_df['WEEK'] = cross_channel_df['DATE'].dt.to_period('W').apply(lambda r: r.start_time)\n\n# Set the style for the plots\nsns.set(style=\"whitegrid\")\n\n# Define the size of each plot (square)\nplot_size = 5",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "id": "837652e1-c186-4a53-bc12-86b22326e56c",
+   "metadata": {
+    "language": "python",
+    "name": "cell4",
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": "# Create a figure and axis for the plot\nfig, ax = plt.subplots(figsize=(plot_size, plot_size))\n\n# Plot Spend by datasource and week\nsns.lineplot(data=cross_channel_df, x='WEEK', y='SPEND', hue='DATASOURCE', marker='o', ax=ax)\nax.set_title('Spend by Datasource and Week', fontsize=12)\nax.set_ylabel('Spend', fontsize=10)\nax.set_xlabel('Month', fontsize=10)\n\n# Format the x-axis to show month labels\nax.xaxis.set_major_locator(mdates.MonthLocator())\nax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\nplt.xticks(rotation=45, fontsize=8)\nplt.yticks(fontsize=8)\n\n# Adjust legend font size\nax.legend(fontsize=8)\n\n# Adjust layout to prevent overlap\nplt.tight_layout()\n\n# Show the plot\nplt.show()",
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "1b076288-d652-4911-98e7-bb3a3fae1c60",
+   "metadata": {
+    "language": "python",
+    "name": "cell5",
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": "# Create a figure and axis for the plot\nfig, ax = plt.subplots(figsize=(plot_size, plot_size))\n\n# Plot Revenue by datasource and week\nsns.lineplot(data=cross_channel_df, x='WEEK', y='REVENUE', hue='DATASOURCE', marker='o', ax=ax)\nax.set_title('Revenue by Datasource and Week', fontsize=12)\nax.set_ylabel('Revenue', fontsize=10)\nax.set_xlabel('Month', fontsize=10)\n\n# Format the x-axis to show month labels\nax.xaxis.set_major_locator(mdates.MonthLocator())\nax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\nplt.xticks(rotation=45, fontsize=8)\nplt.yticks(fontsize=8)\n\n# Adjust legend font size\nax.legend(fontsize=8)\n\n# Adjust layout to prevent overlap\nplt.tight_layout()\n\n# Show the plot\nplt.show()",
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "142d59e6-65a4-4afc-bdd5-c606403c72bc",
+   "metadata": {
+    "language": "python",
+    "name": "cell6",
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": "# Create a figure and axis for the plot\nfig, ax = plt.subplots(figsize=(plot_size, plot_size))\n\n# Plot ROAS by datasource and week\nsns.lineplot(data=cross_channel_df, x='WEEK', y='ROAS', hue='DATASOURCE', marker='o', ax=ax)\nax.set_title('ROAS by Datasource and Week', fontsize=12)\nax.set_ylabel('ROAS', fontsize=10)\nax.set_xlabel('Month', fontsize=10)\n\n# Format the x-axis to show month labels\nax.xaxis.set_major_locator(mdates.MonthLocator())\nax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\nplt.xticks(rotation=45, fontsize=8)\nplt.yticks(fontsize=8)\n\n# Adjust legend font size\nax.legend(fontsize=8)\n\n# Adjust layout to prevent overlap\nplt.tight_layout()\n\n# Show the plot\nplt.show()",
+   "execution_count": null
+  }
+ ]
+}
\ No newline at end of file