{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multi-options model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from prayas import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The experiment consists of multiple variants and in each variant the visitor has one ore more options to choose. A detailed explanation of the methodology is available in *[Bayesian A/B Testing for Business Decisions](https://arxiv.org/abs/2003.02769)* by Shafi Kamalbasha and Manuel J. A. Eugster (2020)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, the experiment consists of two variants with each variant having `9` different options from which the visitor can choose:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "m = MultiOptionsModel(variants=[\"Original\", \"Progressive\"],\n", " options=[9, 9],\n", " baseline=\"Original\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the *conversion* measure, we are also interested in measuring the *revenue* and the *gain* both in Euro:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "rev_a = [27.95, 47.95, 63.95, \n", " 35.95, 63.95, 79.95,\n", " 79.95, 151.95, 223.95]\n", "rev_b = [34.95, 59.95, 79.95,\n", " 37.95, 67.95, 84.95,\n", " 69.95, 132.95, 195.95]\n", "\n", "m.add_measure(\"revenue\", \n", " success_value=[rev_a, rev_b])\n", "\n", "m.add_measure(\"gain\", \n", " success_value=[rev_a, rev_b],\n", " nonsuccess_value=[np.repeat(-0.06, 9),\n", " np.repeat(-0.04, 9)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The full model specification for this experiment is:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Multi-options model\n", "Variants : Original, Progressive\n", "Baseline : Original\n", "Measures : conversion, revenue, gain\n", "Primary measure : conversion\n", "Maximum loss threshold: 5 \n" ] } ], "source": [ "print(m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set the result of the experiment:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "m.set_result(successes=[[50, 5, 5, 28, 7, 5, 20, 1, 6],\n", " [28, 3, 6, 30, 6, 5, 27, 6, 3]],\n", " trials=[8067, 8082])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Investigate the result:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "m.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot provides the posteriors of the measures 'conversion rate', 'revenue', and 'gain'. We can already see that for different measures the location of the posteriors to each other is different.\n", "\n", "Get details on the result:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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VariantMeasureProbabilityToBeBestProbabilityToBeatBaselineUpliftFromBaselinePotentialLossFromBaselineMaxUpliftMaxPotentialLoss
0Progressivegain0.891150.8904541.6230161.98130342.2303771.957154
1Originalconversion0.796650.000000.0000000.00000010.6956821.450911
2Originalrevenue0.509200.000000.0000000.0000000.4634935.613769
3Progressiverevenue0.490800.49015-0.5798316.336800-0.4613546.372550
4Progressiveconversion0.203350.20305-9.65405610.990136-9.66224011.121591
5Originalgain0.108850.000000.0000000.000000-29.69153131.152837
\n", "
" ], "text/plain": [ " Variant Measure ProbabilityToBeBest ProbabilityToBeatBaseline \\\n", "0 Progressive gain 0.89115 0.89045 \n", "1 Original conversion 0.79665 0.00000 \n", "2 Original revenue 0.50920 0.00000 \n", "3 Progressive revenue 0.49080 0.49015 \n", "4 Progressive conversion 0.20335 0.20305 \n", "5 Original gain 0.10885 0.00000 \n", "\n", " UpliftFromBaseline PotentialLossFromBaseline MaxUplift MaxPotentialLoss \n", "0 41.623016 1.981303 42.230377 1.957154 \n", "1 0.000000 0.000000 10.695682 1.450911 \n", "2 0.000000 0.000000 0.463493 5.613769 \n", "3 -0.579831 6.336800 -0.461354 6.372550 \n", "4 -9.654056 10.990136 -9.662240 11.121591 \n", "5 0.000000 0.000000 -29.691531 31.152837 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.score_baseline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The progressive variant has lower conversion rates than the original variant (-10% on average) but the revenue per visitor is almost the same for both variants since the probability to be best is almost 50% for both variants; both variants are equally likely to be the best. The progressive variant has less conversions for the lower priced option compared to the original, but makes up for the lost revenue with higher conversions on the premium product. For the metric gain per visitor, progressive discount had the highest probability to be best with a low expected loss of 2%." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }