identifying key factors for the commercial success of an integrated <» HEE
journ Py | e) F anning and ticke ating s sme artphone ap plicz ation
University of Life Sciences and
Natural Resources, Vienna
Astrid GuUhnemann!; Simon Shepherd2
“Institute for Transport Studies (IVe), University of Natural Resources and Life Sciences, Vienna; 2Institute for Transport Studies (ITS), University of Leeds
astrid.quehnemann@ boku.ac.at; s.p.shepherd@ its. leeds.ac.uk
| Introduction BCui sees ———————
- A‘one-stop-shop’ mobile phone travel application that integrates journey Bass Sry model wth users pecoming inactive and re-engaging ' ame! | Tota Return on ivestnent
planning, booking, payment nd rea:ne tec inomaton i be iP Gp lhe rp aly rent Roly acheved an eveegeenggemetine EE
developed and tested in the Smarter Travel Solution (STS) project for users are allowed to re-engage after a lag time, depending on user satisfaction above 15 months (5 months excl. development costs) =
West Yorkshire, UK. 5) (Mleasesietien motielanvinasiversan encansmentime even in best case scenario with 100% user satisfaction. =
° The aim is to support a shift away from private car travel by providing Four factors drive user satisfaction based on a Set of desired functionalities of the © Ticketing availability
STS app (Fausset, 2014): ticketing, journey planner, rewards and travel benefits. h er
Me ue information and easy payment methods for Average engagement tie is.driven by Usersatsincton, inaretaimple anpreach Influence small as main operator (55%) included from start.
directly proportional to it.
6 2 18
Maximum Average Engagement Time [months]
» The question is how many people (3) App usage and revenue calculation . — . High expectations for rewards lead to strong drop in user ee
wil use the app and how is use = Morty app usage estate based on assumption on daly tips by actve users ststecton and trough reinforcing loop substantal tae
will impact their travel behaviour. ae crag PP per tip. PPP ris g PP reduction of active users and return on investment 1 dally reward expected 303.485 377 494
Es advertising, purchases of ad-free version and a share of ticket sales revenue.
° There iS limited research on the tmoraoe NOS ° | fF inuiry no ele Monthly active users and Rol after 24 months depending
. . ' . Oo 8@ aS ¢UCIILY PJYUUITeY VIG! on journey planner quality at release and after 24 months
diffusion of journey planning apps , au Diftone Hoda (6) srt — Available functionality and design of journey ——.
and on the factors influencing Joume User fete titi Minto | = planner significantly influence number of Es _
the uptake and use of them. Planning Ticketing Engagement = \ active users. In worst cases with only 20% of = (i
\ ee Monthly customer rate payment ' : ,”
5 saticrmmae ciara” quality throughout, users drop by a third. = Y
Flo = ( : ~\ ects —- ~ Fora positive Rol, quality at release need to ee ! eee
| Objectives in \ pe atleast 60%
ws _ 7 \ eats ema ame ‘ \ Varying weights of journey planner quality
' ' 1 ' ‘ewards J ‘ve os 7 : ES ; \ \ e ; ‘evar hir j ale ~aticf> T On | TO) SC B Cas To B Cc
Assess the potential impacts of this integrated STS app on travellers, / - | get Sale ee (4) susssosinye =| Active users decrease considerably because Combi ae Wl Combi ase We
society and businesses. a . oe oa || | a \ adoption through word of mouth and 200000 100000
1 Adimpressions —— ee +4 "share of ticketing i y Ticketing \ ATA ‘ . H H An
- Develop models that allow forecasting | ee moe meet sath || willingness of inactive users to reconsider is = | oy) / aan
how many travellers will use th oe en Dei i | dampened by lower satisfaction. This leads 100,000 | -50,000
» Ow Many travemers wir use me app \ \ ST ade Matti i, = | | to a significantly delayed saturation. atone 100000 on
B. how each individual app user will change travel behaviour \ \ SS ee pate ae wi a Earliest profit making month increases from oe aN am
C. how this aggregate behaviour change will impact the transport system and society. \ a beg et ii tae month 4 in best case to 11 in combined and 20,000 | 4/ 40,000 |
(3)\ [ Avenpnmot ——BSSotonotagp . ‘ | 13 with higher journey planner weight. 0.000 20,000 [«
A) Diffusion and B) Individual Travel Behaviour a —— 2 aes ae ee (2) 0
veage changeve SSA Usage ea ea Conclusions & Outlook
I a tia | | om A |
A psa + ee : Fapeced monthly eran Nlsecentin
M odels for | " egisiere ee ‘stig? _ rewards per user sponsor and user
: eS Adoption Rate EE Gee , aa i . . . .
For A) , C) uilereeuianal on STS evaluation | he ess a | SS : ovat ° Quality of journey planner and rewards are key factors for user satisfaction
ransport System and Socie net imal Ny © or Mout |) " \ nn. ; i i
inal ul \Seagane "7 = & engagement time and consequently adoption, retention and use.
aenily Wie TaCiors inet InMUeHee'ine GBI aha USE Gr ule STS Spp \O uae tt, tee EEE Vv | ae - More research is needed on how reward schemes and capabilities of the
Ie = 4 offered modes Ni _ Lamget quality of
Use simulations to contribute to the development of a business model for running eS Se - ° we wenden aoe journey planner such as modes included, options for personalisation,
a commercially successful app a a quality of the user interface influence user satisfaction.
eee eee ss oi ia 2 ee App user surveys will be carried out to provide data for extending the user
satisfaction model based on a technology acceptance model (TAM) (see
e.g. Tsai, 2010) and/or S-O-R (Stimuli — Organism — Response) model (see
Fang etal., 2017).
The release of the app will provide data for the model calibration.
¢ A segmentation by type of users will be applied to explore variations in
expectations and experiences and to model impacts on travel behaviour.
(4) Reinforcing Loop: Ticketing availability
New Operators will offer their tickets for sale through the app if they expect sufficient
profit from doing so. This will increase availability of tickets, in turn increasing user
satisfaction and use of the app, incentivising more operators to join the scheme.
(5) Reinforcing Loop: Rewards
We assume that users expect a certain number of rewards per month. Rewards are
provided by sponsors. Their number depends on the active users of the STS app. If
The purpose of developing an evaluation framework is to define the criteria
& indicators against which the impacts of the app will be assessed
Logic maps (AECOM & PTEG, 2012; Hills, 2010) are used to visualise
interconnections between variables that influence the success of the STS
app and help to identify measurable indicators for such variables,
developed based on a feasibility stay and BEGUSHIONS with partners
e
Fa! ‘a ~~
Logic Map apasa ee REE come ine 01 these increase, more sponsors can be attracted and more rewards offered, leading to SiGciGcil' pe )
Commercial Pe i vase ses es = —
Success hg aM lng uh ae rise een a higher user satisfaction and consequently more users. - AECOM, PTEG (2012) LSTF Monitoring and Evaluation Guidance - Final Report.
eae We ‘ Transport Operators) . = ope . e . 4 i .
Fes) co cee a negra mpleenatos : quantitative, from system (6) App operating costs and profitability calculation * Fang,J., Zhao, Z., Wen, C., & Wang, R. (2017). Design and performance attributes driving mobile travel
ot x oy eee TES ee —— . . application engagement. International J ournal of Information Management, 37(4), 269-283
Zz a \ shat TateporeO pert) App profitability is calculated as the sum of monthly revenue minus marketing costs . “ oy Lane , ,
one ye R f BS App | Lagi Innovation potential eulccessiulcreatoniol an qualitative, d ts f ti th STs C ts f ti h I q ts = Fausset, R. (2014). The Smarter Travel Solution Feasibility Study. Final Report. 15t J uly 2014.
erating Cis sts Operating Costs ae | Meee | e improved database . . . co _
ns a ae Ao Cama) | | | -osibeeninage ———Userasessnen qatar pore aNd COSTS OF Operaung ME > 1? APP. LOSIS OF Operating SUCT an app Usually CONSIS - Little, J.D. C., & Graves, S. C. (2008). Little’s Law. In T. J. Chhajed, D., Lowe (Ed.), Building Intuition
at . ‘ew ‘sae Te End user mobility impacts revel tine savings auntie asthe sven of costs of ticket fulfilment, cost of payment processing, customer support and Insights from Basic Operations Management Models and Principles (pp. 81-100). Springer
frame tt, / BED | iw vonie| | | eraser hetescein sche noes NGS) re maintenance costs, and technical costs. - Nel, P. (2016). The MIT way to spot unicorns with mad cow disease | Pieter Nel | Pulse | Linkedin
‘of ce eee groups ¢ Tsai, C.-Y. (2010). An analysis of usage intentions for mobile travel guide systems. African J ournal of
ene user ess costoftavelf sefreportedintentons Business Management, 4(13), 2962-2970.
