Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets

Abstract

We assess the demand effects of discounts on train tickets issued by the Swiss Federal Railways, the so-called 'supersaver tickets', based on machine learning, a subfield of artificial intelligence. Considering a survey-based sample of buyers of supersaver tickets, we investigate which customer-or trip-related characteristics (including the discount rate) predict buying behavior, namely: booking a trip otherwise not realized by train, buying a first-rather than second-class ticket, or rescheduling a trip (e.g. away from rush hours) when being offered a supersaver ticket. Predictive machine learning suggests that customer's age, demand-related information for a specific connection (like departure time and utilization), and the discount level permit forecasting buying behavior to a certain extent. Furthermore, we use causal machine learning to assess the impact of the discount rate on rescheduling a trip, which seems relevant in the light of capacity constraints at rush hours. Assuming that (i) the discount rate is quasi-random conditional on our rich set of characteristics and (ii) the buying decision increases weakly monotonically in the discount rate, we identify the discount rate's effect among 'always buyers', who would have traveled even without a discount, based on our survey that asks about customer behavior in the absence of discounts. We find that on average, increasing the discount rate by one percentage point increases the share of rescheduled trips by 0.16 percentage points among always buyers. Investigating effect heterogeneity across observables suggests that the effects are higher for leisure travelers and during peak hours when controlling several other characteristics.

Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-Mode Online Panel

“…The double machine learning approach can be successfully applied in all kinds of settings where researchers are interested in explaining the effects of treatment variables while controlling for a high number of covariates. Recently, many studies have been published which apply the double machine learning technique in economics, for example, to analyze gender differences in wage expectations (Bach et al, 2018a;Fernandes et al, 2021;Wunsch & Strittmatter, 2021) or to estimate the effect of policies/programs (Denisova-Schmidt et al, 2021;Goller et al, 2021;Huber et al, 2021;Knaus, 2021;Knaus et al, 2020).…”

Essays on causal inference

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