Title: Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets
Authors: Martin Huber - University of Fribourg (Switzerland)
Jonas Meier - University of Amsterdam (Netherlands) [presenting]
Hannes Wallimann - University of Applied Sciences and Arts Lucerne (Switzerland)
Abstract: The demand effects of discounts on train tickets issued by the Swiss Federal Railways, the so-called supersaver tickets, is assessed based on machine learning. 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. Furthermore, we use causal machine learning to assess the impact of the discount rate on rescheduling a trip, which seems relevant in 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 rates 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.