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

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  • Martin Huber
  • Jonas Meier
  • Hannes Wallimann

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.

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  • Martin Huber & Jonas Meier & Hannes Wallimann, 2021. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Papers 2105.01426, arXiv.org, revised Jun 2022.
    • Handle: RePEc:arx:papers:2105.01426
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    2. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
    3. Zabaleta, Mercedes Elena Martínez & Luna, Raúl Enrique Rodríguez, 2023. "Inteligencia empresarial y su rol en la generación de valor en los procesos de negocios," Revista Tendencias, Universidad de Narino, vol. 24(1), pages 226-251, January.
    4. Hannes Wallimann & Kevin Blattler & Widar von Arx, 2021. "Do price reductions attract customers in urban public transport? A synthetic control approach," Papers 2111.14613, arXiv.org, revised Mar 2022.
    5. Hintermann, Beat & Thommen, Christoph, 2022. "Price versus Commitment: Managing the Demand for Off-peak Train Tickets in a Field Experiment," Working papers 2022/05, Faculty of Business and Economics - University of Basel.
    6. Thommen, Christoph & Hintermann, Beat, 2023. "Price versus Commitment: Managing the demand for off-peak train tickets in a field experiment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    7. Hannes Wallimann, 2024. "Austria's KlimaTicket: Assessing the short-term impact of a cheap nationwide travel pass on demand," Papers 2401.06835, arXiv.org, revised Feb 2024.

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy

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