Tr@Ins2 User Studies Tom Evens

393 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
393
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Tr@Ins2 User Studies Tom Evens

  1. 1. User studies Tom Evens IBBT – MICT – UGent
  2. 2. Objectives  Predict market adoption potential  Forecast adoption segmentation  Profiling adopter segments  Socio-demographics  Willingness to pay  Drivers and thresholds  Needs and wants … 2
  3. 3. Methodology  Electronic survey (www.internetopdetrein.be)  Promoted via  Metro  Bond van Trein-, Tram- en Busgebruikers (BTTB)  UGent  Digimedia  ZDNet  1324 cases were filled in  After cleaning: 964 relevant cases 3
  4. 4. Methodology (2)  By means of Product Specific Adoption Potential  Identifying user needs and wants  Predicting adoption potential  Essential input for:  Development business model  Deployment network architecture  Set up introduction strategies 4
  5. 5. Product Specific Adoption Potential (PSAP)  Target: offering added value services  Question: which added value to whom?  Answer: segmentation and targeting  Target?  Challenge  Mixed Solution Generating Acquisition  Powerful segmentation  Complex issues ‘Quali’ methodology  Multiple applications  Difficult conceptual level for +  Profiling segments respondent PSAP survey  Clear targeting & marketing 5
  6. 6. PSAP is based on recent views of double-peaked adoption curve PULL: PUSH/ Mass Market PULL Adoption Innovation Dislikers Pr oduct Mat ur i t y & Ri sk Aver si on Innovators Early Early Late Laggards Adopters Majority Majority Key char act er i st i cs: • No cl assi cal segm ent si zes • Pr oduct m ur i t y and r i sk aver si on ( x- axi s) r epr esent s r ough, at but no concr et e t i m i ne el • Pot ent i al m ket i s not necessar i l y 10% si nce Innovat i on ar , 6 Di sl i ker s m exi st t hat w l l never be i nt er est ed t o pur chase ay i
  7. 7. Adoption potential forecast 40% 35% IOT 30% 25% 20% 15% Rogers 10% 5% 0% innovators early adopters early majority late majority laggards Rogers 2,5% 13,5% 34,0% 34,0% 16,0% IOT 12,8% 14,2% 11,6% 28,9% 32,6% 7
  8. 8. Differences between classes 40% 35% 30% Class 1 25% 20% 15% Class 2 10% 5% Rogers 0% innovators early adopters early majority late majority laggards 8
  9. 9. Cumulative adoption: S-Curve 120% 100% 80% 60% 40% 20% 0% innovators early adopters early majority late majority laggards Rogers 2,50% 16% 50% 84% 100% IOT 12,80% 27,00% 38,60% 67,40% 100% 9
  10. 10. Drivers and motivations  Important factors to accelerate (drivers) or slow down (barriers)  Opportunity to keep informed (information)  Making the most of the journey (utility)  Stay in contact (communication)  Current infrastructure not suitable  Already Internet at home/work  Raise productivity  Prefer talking with passengers (social contact) 10
  11. 11. Willingness to pay  Innovators and Early Adopters likely to pay  Early Majority only if price is low enough  Especially for browsing and e-mail applications  Also updated news, travel info and corporate network (VPN) popular  In general:  Innovators business applications  Early Adopters more entertainment oriented 11
  12. 12. Quality of Service  Most important features  Cost  Predictability/reliability  High speed  Least important features  Ease of payment  Anonymity  Service desk assistance 12
  13. 13. Conclusions  Clear market potential  Short term: 10-15% passengers  Long term: 30% passengers  Small but heavily interested target public  Price is crucial factor for adoption  Willingness to pay highest among I and EA  I business and EA entertainment applications 13
  14. 14. Thank you for listening Questions? Further information? Tom Evens Korte Meer 7-9-11 9000 Gent 09/264.68.83 Tom.Evens@UGent.be 14

×