Product Cannibalization A Prototypical Marketing Science Problem - PowerPoint PPT Presentation

The webinar will start at: 13:00:00 The current time is: 13:00:49 Central Daylight Time UTC-5 Product Cannibalization A Prototypical Marketing Science Problem

Introduction Your Hosts Today • Stefan Conrady stefan.conrady@bayesia.us • Stacey Blodgett stacey.blodgett@bayesia.us BayesiaLab.com 2

Today’s Program Motivation & Background • Definitions • Introductory Example Representation • Conceptual Framework: Bayesian Networks • Probabilistic Reasoning Learning, Estimation, and Inference • Causal Reasoning? • Unsupervised Learning • Disjunctive Cause Criterion • Assign Utilities • Evaluate Policies All Fictional Numbers stefan.conrady@bayesia.us 3

Webinar Slides & Recording Available stefan.conrady@bayesia.us 4

Motivation & Background Definitions • Typically, a new product adversely affects the sales of existing products: • If it affects your competitor’s products, it’s Conquest • If it affects your own products, it’s BayesiaLab.com 5

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Motivation & Background Introductory Example: 2000 BMW X5 • First SUV in the BMW product portfolio. X5 BayesiaLab.com 7

Motivation & Background Introductory Example: New BMW X3 vs. Existing BMW X5 • New, smaller X3 launched in 2004 Cannibalization? X3 X5 Product B Product A BayesiaLab.com 8

Bayesian Network Representation

Bayesian Network Representation Conceptual Network Product B causes P(Sales A |Sales B ) lower sales of P(Sales B ) Product A “Cannibalization” + – BayesiaLab.com 10

Bayesian Network Representation Obvious, as we encoded that as our domain knowledge Inference into the network. • Computing the cannibalization effect C of Product B on Product A: • C(B A) = -0.3 (unit effect) Existing Product A Existing Product A Mean: 0.900 Dev: 0.831 Mean: 1.200 Dev: 0.748 Value: 1.200 Value: 0.900 (-0.300) 40.00% 0 20.00% 0 40.00% 1 30.00% 1 30.00% 2 40.00% 2 New Product B New Product B Mean: 1.000 Dev: 0.000 Mean: 0.000 Dev: 0.000 Value: 1.000 (+1.000) Value: 0.000 0.00% 0 100.00% 0 100.00% 1 0.00% 1 0.00% 2 0.00% 2 BayesiaLab.com 11

Bayesian Network Representation Can’t we do this in Excel? BayesiaLab.com 12

Motivation & Background Example: BMW Portfolio of “Utility - Type” Vehicles in 2018 All products are cannibalizing each other! BayesiaLab.com 13

Bayesian Network Representation A Fully Connected Network? ? Can we specify it? No. Can we machine-learn it? Perhaps. BayesiaLab.com 14

Learning & Estimating Cannibalization

Learning & Estimating Cannibalization Couldn’t we just ask auto buyers? BayesiaLab.com 16

Learning & Estimating Cannibalization Understanding Cannibalization by Other Means? • Trade-Ins • New and old product not comparable • Auto Buyer Surveys (2 nd Choice) • Respondents tend to exaggerate their counterfactual choice (“I would have bought the convertible, but we need the third row.”) • Choice Experiments • Hypothetical choices are noncommittal • Expensive to conduct BayesiaLab.com 17

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Map of Analytic Modeling & Reasoning Data Model Source Theory Theory Description Prediction Explanation Simulation Attribution Optimization Model Purpose Association/Correlation Causation BayesiaLab.com 19

Map of Analytic Modeling & Reasoning Data Model Source Theory Description Prediction Explanation Simulation Attribution Optimization Model Purpose Association/Correlation Causation BayesiaLab.com 20

Learning & Estimating Cannibalization A Fictional Case Study

Learning & Estimating Cannibalization Case Study Question: • What is the cannibalization effect of B on A, C, and D? D C A B BayesiaLab.com 22

Learning & Estimating Cannibalization Daily Sales Data Objective: To machine-learn a Bayesian network model from the sales data. BayesiaLab.com 23

A desktop software for: encoding • learning • editing • performing inference • analyzing • • simulating optimizing • with Bayesian networks. BayesiaLab.com 24

Data Import Wizard BayesiaLab.com 25

Variable Type Definition BayesiaLab.com 26

Discretization BayesiaLab.com 27

Unconnected Network BayesiaLab.com 28

Unsupervised Learning Using the EQ Algorithm BayesiaLab.com 29

How can we use this network to calculate the causal effect of B on A, C, and D? Counterintuitive arc directions! Final Network BayesiaLab.com 30

Disjunctive Cause Criterion BayesiaLab.com 31

Disjunctive Cause Criterion VanderWeele and Shpitser (2011) Cannibaliz ed Product • “We propose that control be made for any [pre -treatment] covariate that is either a cause of treatment or of the outcome or both.” Confounder Cannibaliz ing Product Implementation in BayesiaLab: IMPORTANT ASSUMPTION: Likelihood Matching on Confounders in Direct Effects Analysis NO UNOBSERVED CONFOUNDERS  Causal Effect, i.e., the Cannibalization Rate BayesiaLab.com 32

Map of Analytic Modeling & Reasoning Data Model Source Confounders Theory Description Prediction Explanation Simulation Attribution Optimization Model Purpose Association/Correlation Causation BayesiaLab.com 33

We need to define confounders and non-confounders . By default, all nodes are confounders . Final Network BayesiaLab.com 34

Computing the Direct Effect of B on A BayesiaLab.com 35

Direct Effect of B on A BayesiaLab.com 36

Direct Effect of B on C BayesiaLab.com 37

Direct Effect of B on D BayesiaLab.com 38

Adding a Decision Node BayesiaLab.com 39

Adding Utility Nodes BayesiaLab.com 40

Policy “B”: Utilities=90.285 Comparing Policies “B” vs. “No B” BayesiaLab.com 41

Policy “No B”: Utilities=98.321 Comparing Policies “B” vs. “No B” BayesiaLab.com 42

VR In Conclusion… 43

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Thank You! stefan.conrady@bayesia.us BayesianNetwork linkedin.com/in/stefanconrady facebook.com/bayesia BayesiaLab.com 51