From 2662f994f06cfe9f3cdf6a59a4d6cc9488130f6e Mon Sep 17 00:00:00 2001 From: uri Date: Tue, 27 May 2025 15:16:52 +0200 Subject: [PATCH] Switch monitoring to track YourTripQuestionaire --- ab_test_guest_journey_monitoring.ipynb | 153 +++++++++---------------- 1 file changed, 57 insertions(+), 96 deletions(-) diff --git a/ab_test_guest_journey_monitoring.ipynb b/ab_test_guest_journey_monitoring.ipynb index 4f267b5..69a2241 100644 --- a/ab_test_guest_journey_monitoring.ipynb +++ b/ab_test_guest_journey_monitoring.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -29,14 +29,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/joaquin/.superhog-dwh/credentials.yml\n" + "/home/uri/.superhog-dwh/credentials.yml\n" ] } ], @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -111,17 +111,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# A/B test name to measure\n", - "#ab_test_name = \"AAVariantTest\"\n", - "ab_test_name = \"WelcomePageDestinationContext\"\n", + "ab_test_name = 'YourTripQuestionaire'\n", "\n", "# Define the variations in which we want to run the tests\n", - "var_A = 'GenericImageAndCopy' # Ideally, this should be the control group\n", - "var_B = 'ContextSpecificImageAndCopy' # Ideally, this should be the study group\n", + "var_A = 'DontShowYourTripQuestions' # Ideally, this should be the control group\n", + "var_B = 'ShowYourTripQuestions' # Ideally, this should be the study group\n", "\n", "variations = [var_A, var_B]\n" ] @@ -136,54 +135,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " ab_test_name variation last_update \\\n", - "0 WelcomePageDestinationContext ContextSpecificImageAndCopy 2025-04-24 \n", - "1 WelcomePageDestinationContext GenericImageAndCopy 2025-04-24 \n", + "Empty DataFrame\n", + "Columns: [ab_test_name, variation, last_update, guest_journeys_count, guest_journey_started_count, guest_journey_completed_count, guest_journey_with_responses_count, guest_journey_with_payment_count, guest_revenue_count, deposit_count, waiver_count, check_in_cover_count, guest_revenue_sum, deposit_sum, waiver_sum, check_in_cover_sum, guest_revenue_avg_per_guest_journey, guest_revenue_sdv_per_guest_journey, deposit_avg_per_guest_journey, deposit_sdv_per_guest_journey, waiver_avg_per_guest_journey, waiver_sdv_per_guest_journey, check_in_cover_avg_per_guest_journey, check_in_cover_sdv_per_guest_journey, csat_avg_per_guest_journey_with_response, csat_sdv_per_guest_journey_with_response]\n", + "Index: []\n", "\n", - " guest_journeys_count guest_journey_started_count \\\n", - "0 190 188 \n", - "1 199 199 \n", - "\n", - " guest_journey_completed_count guest_journey_with_responses_count \\\n", - "0 112 34 \n", - "1 115 47 \n", - "\n", - " guest_journey_with_payment_count guest_revenue_count deposit_count ... \\\n", - "0 58 58 12 ... \n", - "1 50 50 10 ... \n", - "\n", - " guest_revenue_avg_per_guest_journey guest_revenue_sdv_per_guest_journey \\\n", - "0 6.837889 11.673403 \n", - "1 5.728471 11.115316 \n", - "\n", - " deposit_avg_per_guest_journey deposit_sdv_per_guest_journey \\\n", - "0 0.383661 1.845352 \n", - "1 0.361084 1.708223 \n", - "\n", - " waiver_avg_per_guest_journey waiver_sdv_per_guest_journey \\\n", - "0 6.172824 11.320728 \n", - "1 5.240985 10.864534 \n", - "\n", - " check_in_cover_avg_per_guest_journey check_in_cover_sdv_per_guest_journey \\\n", - "0 0.281404 1.569646 \n", - "1 0.126402 1.030621 \n", - "\n", - " csat_avg_per_guest_journey_with_response \\\n", - "0 3.882353 \n", - "1 3.723404 \n", - "\n", - " csat_sdv_per_guest_journey_with_response \n", - "0 1.066422 \n", - "1 0.993503 \n", - "\n", - "[2 rows x 26 columns]\n" + "[0 rows x 26 columns]\n" ] } ], @@ -230,14 +193,8 @@ " \n", "from\n", "\tintermediate.int_core__ab_test_monitoring_guest_journey gj\n", - "left join\n", - "\tintermediate.int_core__bookings b on b.id_verification_request = gj.id_verification_request\n", - "left join\n", - "\tintermediate.int_core__accommodation a on a.id_accommodation = b.id_accommodation\n", "where\n", "\tgj.ab_test_name = '{}'\n", - " and gj.first_appearance_at_utc >= '2025-04-23 18:05:00'\n", - " and a.town in ('London', 'Greater London')\n", "group by\n", "\t1,2\n", "\"\"\".format(ab_test_name)\n", @@ -256,18 +213,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "IndexError", + "evalue": "single positional indexer is out-of-bounds", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[19], line 9\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Find the total count and other metadata\u001b[39;00m\n\u001b[1;32m 8\u001b[0m total_count \u001b[38;5;241m=\u001b[39m grouped_data\u001b[38;5;241m.\u001b[39msum()\n\u001b[0;32m----> 9\u001b[0m ab_test_name \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mab_test_name\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;66;03m# Assuming all rows are for the same A/B test\u001b[39;00m\n\u001b[1;32m 10\u001b[0m last_update \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlast_update\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmax()\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# Create a pie chart using Seaborn styling\u001b[39;00m\n", + "File \u001b[0;32m~/Workspace/data-jupyter-notebooks/venv/lib/python3.12/site-packages/pandas/core/indexing.py:1191\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1189\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m 1190\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_deprecated_callable_usage(key, maybe_callable)\n\u001b[0;32m-> 1191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Workspace/data-jupyter-notebooks/venv/lib/python3.12/site-packages/pandas/core/indexing.py:1752\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1749\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index by location index with a non-integer key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1751\u001b[0m \u001b[38;5;66;03m# validate the location\u001b[39;00m\n\u001b[0;32m-> 1752\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_integer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1754\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_ixs(key, axis\u001b[38;5;241m=\u001b[39maxis)\n", + "File \u001b[0;32m~/Workspace/data-jupyter-notebooks/venv/lib/python3.12/site-packages/pandas/core/indexing.py:1685\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_integer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1683\u001b[0m len_axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis))\n\u001b[1;32m 1684\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m key \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis:\n\u001b[0;32m-> 1685\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle positional indexer is out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mIndexError\u001b[0m: single positional indexer is out-of-bounds" + ] } ], "source": [ @@ -329,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -407,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -497,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -568,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -576,16 +537,16 @@ "output_type": "stream", "text": [ " metric relative_difference p_value\n", - "0 conversion_rate 0.020046 0.816792\n", - "1 payment_rate 0.214947 0.234461\n", - "2 waiver_payment_rate 0.204474 0.328839\n", - "3 deposit_payment_rate 0.256842 0.581725\n", - "4 CIH_payment_rate 1.094737 0.279124\n", - "5 avg_guest_revenue_per_gj 0.193667 0.338740\n", - "6 avg_waiver_revenue_per_gj 0.177799 0.408923\n", - "7 avg_deposit_revenue_per_gj 0.062525 0.900604\n", - "8 avg_CIH_revenue_per_gj 1.226264 0.253386\n", - "9 avg_csat_per_gj_with_response 0.042689 0.500513\n" + "0 conversion_rate -0.022089 0.732507\n", + "1 payment_rate -0.019231 0.843783\n", + "2 waiver_payment_rate -0.109637 0.344132\n", + "3 deposit_payment_rate 0.358974 0.297872\n", + "4 CIH_payment_rate -0.235577 0.722020\n", + "5 avg_guest_revenue_per_gj -0.087267 0.434288\n", + "6 avg_waiver_revenue_per_gj -0.104328 0.391617\n", + "7 avg_deposit_revenue_per_gj 0.264787 0.459148\n", + "8 avg_CIH_revenue_per_gj -0.191473 0.779274\n", + "9 avg_csat_per_gj_with_response 0.133721 0.051334\n" ] } ], @@ -616,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -624,27 +585,27 @@ "output_type": "stream", "text": [ "\n", - "WelcomePageDestinationContext results (last updated at 2025-04-24)\n", + "WelcomePageDestinationContext results (last updated at 2025-05-27)\n", "\n", - "Total Guest Journeys affected by this A/B test: 389 - Total Guest Revenue: 2439 GBP.\n", - " Variation GenericImageAndCopy: Guest Journeys 199 (51.2%) - Guest Revenue: 1139 GBP (46.7%).\n", - " Variation ContextSpecificImageAndCopy: Guest Journeys 190 (48.8%) - Guest Revenue: 1299 GBP (53.3%).\n", + "Total Guest Journeys affected by this A/B test: 420 - Total Guest Revenue: 5280 GBP.\n", + " Variation GenericImageAndCopy: Guest Journeys 212 (50.5%) - Guest Revenue: 2786 GBP (52.8%).\n", + " Variation ContextSpecificImageAndCopy: Guest Journeys 208 (49.5%) - Guest Revenue: 2494 GBP (47.2%).\n", "\n", "Main Metrics - Comparing ContextSpecificImageAndCopy vs. GenericImageAndCopy.\n", "\n", - "CONVERSION RATE (not significant): 58.9% vs. 57.8% (1.2% ppts.| 2.0%).\n", - "PAYMENT RATE (not significant): 30.5% vs. 25.1% (5.4% ppts.| 21.5%).\n", - "AVG GUEST REVENUE PER GJ (not significant): 6.84 vs. 5.73 (1.11 ppts.| 19.4%).\n", + "CONVERSION RATE (not significant): 68.3% vs. 69.8% (-1.5% ppts.| -2.2%).\n", + "PAYMENT RATE (not significant): 49.0% vs. 50.0% (-1.0% ppts.| -1.9%).\n", + "AVG GUEST REVENUE PER GJ (not significant): 11.99 vs. 13.14 (-1.15 ppts.| -8.7%).\n", "\n", "Other Metrics\n", "\n", - "WAIVER PAYMENT RATE (not significant): 24.2% vs. 20.1% (4.1% ppts.| 20.4%).\n", - "DEPOSIT PAYMENT RATE (not significant): 6.3% vs. 5.0% (1.3% ppts.| 25.7%).\n", - "CIH PAYMENT RATE (not significant): 3.2% vs. 1.5% (1.7% ppts.| 109.5%).\n", - "AVG WAIVER REVENUE PER GJ (not significant): 6.17 vs. 5.24 (0.93 ppts.| 17.8%).\n", - "AVG DEPOSIT REVENUE PER GJ (not significant): 0.38 vs. 0.36 (0.02 ppts.| 6.3%).\n", - "AVG CIH REVENUE PER GJ (not significant): 0.28 vs. 0.13 (0.16 ppts.| 122.6%).\n", - "AVG CSAT PER GJ WITH RESPONSE (not significant): 3.88 vs. 3.72 (0.16 ppts.| 4.3%).\n" + "WAIVER PAYMENT RATE (not significant): 36.5% vs. 41.0% (-4.5% ppts.| -11.0%).\n", + "DEPOSIT PAYMENT RATE (not significant): 11.5% vs. 8.5% (3.0% ppts.| 35.9%).\n", + "CIH PAYMENT RATE (not significant): 1.4% vs. 1.9% (-0.4% ppts.| -23.6%).\n", + "AVG WAIVER REVENUE PER GJ (not significant): 11.05 vs. 12.34 (-1.29 ppts.| -10.4%).\n", + "AVG DEPOSIT REVENUE PER GJ (not significant): 0.82 vs. 0.65 (0.17 ppts.| 26.5%).\n", + "AVG CIH REVENUE PER GJ (not significant): 0.13 vs. 0.16 (-0.03 ppts.| -19.1%).\n", + "AVG CSAT PER GJ WITH RESPONSE (not significant): 3.75 vs. 3.31 (0.44 ppts.| 13.4%).\n" ] } ], @@ -705,7 +666,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "venv", "language": "python", "name": "python3" },