Discrete Choice Models in PyMC-Marketing
A Choose-Your-Own-Adventure in Bayesian Consumer Choice Modeling
Preliminaries
Who am I?
- I’m a data scientist at Personio
- Bayesian statistician,
- Reformed philosopher and logician.
- Website: https://nathanielf.github.io/
Code or it didn’t Happen
The worked examples used here can be found here
My Website
The Adventure Begins:
Choosing our Own Path
- 21 Discrete Possible Endings!
- The Luxury of being able to explore all paths
- Deterministic Routing and preferred Endings
- Peeking ahead and “cheating” fate.
The Scaling Difficulty
Genuine Uncertainty and the Multiplicity of Paths
- Continuum Possible Endings
- Impossibility of precise Survey and Oversight
- Probabilistic Choice over Paths
- Certainty (if any) only in Expectation and Breadth of Possibility
Sampling the Path Trajectories
Bayesian Inference over Unrealised Worlds
Inference: What is the most plausible world given the data?
\[ p(\theta_{w_{i}} | Y) = \dfrac{p(\theta_{w_{i}})p(Y | \theta_{i})}{\sum_{j}^{N} p(\theta_{w_j})p(Y | \theta_{w_j}) }\]
Counterfactual Inference: What plausibly happens in nearby worlds?
\(\mathbf{\theta_{w_{1}}} \rightsquigarrow\)
\(\mathbf{\theta_{w_{2}}} \rightsquigarrow\)
\(\mathbf{\theta_{w_{3}}} \rightsquigarrow\)
\(f(\alpha_{w_1}, \beta_{w_1}^{0}, \beta_{w_1}^{1}) \rightsquigarrow\)
\(f(\alpha_{w_2}, \beta_{w_2}^{0}, \beta_{w_2}^{1}) \rightsquigarrow\)
\(f(\alpha_{w_3}, \beta_{w_3}^{0}, \beta_{w_3}^{1}) \rightsquigarrow\)
- \(p(Y |w_1)\)
\(\downarrow\) - \(p(Y | w_2)\) \(\downarrow\)
- \(p(Y | w3)\) \(\downarrow\)
Revealed Preference
Utility driven Choice
The Consumer’s Dilemma
“What drives human choice?”
- Utility maximization
- Rational decision-making
- Expected value calculations
- Impulse and advertising?
Each purchase tells a story of preference…
Relative Utility and Marginal Effects
Paths Diverge
Do you value the company of others? Do you fear it? What about the average cave dweller?
\[ u(\text{Light shaft + Silence}) - u(\text{Glowing Fire + Conversational Echoes}) > 0?\]
PyMC-Marketing
Bayesian Marketing Analytics
- Uncertainty-aware decisions: model not just what people choose, but how sure we are.
- Causal inference ready: run interventions, not just predictions.
- Modern Bayesian engine for scalability, flexibility, and transparency.
- Intuitive syntax with powerful underlying math.
Choice Data
Choice Scenarios specified with attributes and choice outcomes for each discrete alternative
PyMC-Marketing Implementation
Trace Paths through Possible Worlds
utility_formulas = [
"gc ~ ic_gc + oc_gc | income + rooms + agehed",
"gr ~ ic_gr + oc_gr | income + rooms + agehed",
"ec ~ ic_ec + oc_ec | income + rooms + agehed",
"er ~ ic_er + oc_er | income + rooms + agehed",
"hp ~ ic_hp + oc_hp | income + rooms + agehed",
]
mnl = MNLogit(df, utility_formulas, "depvar", covariates=["ic", "oc"])
mnl.sample()
The IIA Problem - A Plot Twist
- 50% Red Bus 50% Car
- What happens if we introduce a Blue Bus?
- 33% Red Bus, 33% Blue Bus, 33% Car
The Multinomial Logit enforces the Indepdence of Irrelevant Alternatives property into preference calculations.
\[\dfrac{P_{j}}{P_{i}} = \dfrac{ \dfrac{e^{U_{j}}}{\sum_{i}^{n}e^{U_{k}}}}{\dfrac{e^{U_{i}}}{\sum_{i}^{n}e^{U_{k}}}} = \dfrac{e^{U_{j}}}{e^{U_{i}}} = e^{U_{j} - U_{k}}\]
Key Take-away: The Model Ignores Market Structure
Counterfactual Inference
Counterfactual Plausibility as Criteria of Adequacy
new_policy_df = df.copy()
new_policy_df[["ic_ec", "ic_er"]] = new_policy_df[["ic_ec", "ic_er"]] * 1.5
## Posterior Predictive Forecast under counterfactual setting
idata_new_policy = mnl.apply_intervention(new_choice_df=new_policy_df)
## Compare Old and New Policy Settings
change_df = mnl.calculate_share_change(mnl.idata, mnl.intervention_idata)
change_df
Here be Dragons
Considered Choice
Branching Probability Trees
\(U = Y + W\)
\(P(i) \text{ when } i \in Alts\)
\(P(\text{choose nest B}) \cdot P(\text{choose i} | \text{ i} \in \text{B})\)
\(P(\text{choose nest B}) = \dfrac{e^{W + \lambda_{k}I_{k}}}{\sum_{l=1}^{K} e^{W + \lambda_{l}I_{l}}}\)
\(P(\text{choose i} | \text{ i} \in \text{B}) = \dfrac{e^{Y_{i} / \lambda_{k}}}{\sum_{j \in B_{k}} e^{Y_{j} / \lambda_{k}}}\)
\(I_{k} = ln \sum_{j \in B_{k}} e^{Y_{j} / \lambda_{k}} \\ \text{ and } \lambda_{k} \sim Beta(1, 1)\)
The log-sum component allows for the utility of any alternatives within a nest to “bubble up” and influence the attractiveness of the overall nest.
Behaviourial Insight in Product Preference
The relative importance of product attributes implied by our observed data
The relative importance of installation costs versus operating costs might suggest where to impose a novel pricing strategy?
Credible Counterfactuals
Non-Proportional Substitution
new_policy_df = df.copy()
new_policy_df[["ic_ec", "ic_er"]] = new_policy_df[["ic_ec", "ic_er"]] * 1.5
idata_new_policy_1 = nstL_1.apply_intervention(new_choice_df=new_policy_df)
change_df_1 = nstL_1.calculate_share_change(nstL_1.idata, nstL_1.intervention_idata)
change_df_1
Nested Logit allows for patterns of Non-Proportional Substitution under counterfactual settings
Market Interventions and Implied Worlds
